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Exploring New Frontiers of Knowledge

Are you looking to grow and develop your research skills this summer while working on cutting-edge research projects and getting paid? Lassonde has a competition for two specific awards that will let you do just that:
 
1. NSERC USRA
2. LURA
 
NSERC USRA is the Undergraduate Student Research Award (USRA) from The Natural Sciences and Engineering Research Council of Canada (NSERC) and LURA is the Lassonde Undergraduate Research Award (LURA).
 
You can apply for both awards. Each award is approximately $10,000 for a mandatory 16-week project with a Lassonde professor. The project could qualify as your co-op term as well. You can apply for both awards but can only hold one of them.
 
All information you need about how to find a supervisor and what to include in your application can be found below.

NEW THIS YEAR

Pilot LURA Initiative
We are participating in the “Women in Engineering Co-op Stream” through the Lassonde Co-op Program. It’s a new initiative to increase the retention and recruitment of women in Engineering. As a result, TWO special LURA positions for first-year female engineering students have been created for students that are registered in the program. For more information on this initiative and to assess your eligibility, please contact the Co-op team at lsecoop@lassonde.yorku.ca.

NSERC USRA Indigenous and Black Student Initiative
NSERC encourages qualified Indigenous and Black students to apply to this program. NSERC announced additional support for Black students including an unlimited number of positions. The Indigenous and Black student USRA awards are adjudicated alongside the NSERC USRA awards. All applications from Indigenous and/or Black students whose application otherwise meets all qualifications will be put forward to NSERC for funding.

LURA/USRA Application Process Timeline Summer 2023

Zoom Information Session
Meeting ID: 910 2770 5676
Passcode: 629659
February 6, 2023
6:00 pm – 7:00 pm

Zoom Drop-in Summer UG Research Programs
Meeting ID: 626 139 1181
Passcode: 5987
February 13, 2023
9:00 am – 10:00 am

Complete the Supervisor
Endorsement Forms
February 13, 2023

Application Deadline
February 15, 2023

Awards Announced
March, 2023

Summer Undergraduate
Research Program Starts
May, 2023

The Lassonde School of Engineering offers a paid summer program for undergraduate students to conduct research with a professor. The program is made possible through two awards:

• Lassonde Undergraduate Research Award (LURA)
• Natural Sciences and Engineering Research Council of Canada (NSERC) USRA programs.

The LURA program is funded by Lassonde + supervisor.
The USRA program is funded by NSERC + Lassonde + supervisor.
2023 Guidelines for Students – Summer UG Research Programs
VALUE
$10,000

DURATION
4 Months
Tuesday, January 10, 2023: Competition Will Open – Students will be able to view all available positions with supervisors and start the application process

Tuesday, January 17, 2023, | 11:00 am – 12:00 pm: Live Information Session – Location: Bergeron Centre of Engineering Excellence | Room 125

Thursday, January 26, 2023, | 2:00 pm – 3:00 pm: Zoom Drop-in Summer UG Research Programs
Meeting ID: 626 139 1181 | Passcode: 5987 | Join Here

Monday, February 6, 2023, | 6:00 pm – 7:00 pm: Zoom Information Session
Meeting ID: 910 2770 5676 | Passcode: 629659 | Join Here

Monday, February 13, 2023, | 9:00 am – 10:00 am: Zoom Drop-in Summer UG Research Programs
Meeting ID: 626 139 1181 | Passcode: 5987 | Join Here

Monday, February 13, 2023: Last day for professors to decide which student(s) to support and complete the Supervisor Endorsement Forms

Wednesday, February 15, 2023: Application Deadline

Late March 2023: Awards announced

May 2023: Summer Undergraduate Research Program Starts

May 2023: Orientation Session: Copy of last year’s slides are available here and the Zoom recording is here

June 2023: How to Write an Abstract Workshop

July 2023: Abstract Submission Deadline

July 2023: How to Give a Presentation Workshop

August 2023: Presentation Submission Deadline

August 2023: Summer Undergraduate Research Conference
APPLICATION PROCESS

• Students contact the professor of their interest to be matched.
• Once the professor has chosen a student(s) who fit the particular project, a complete application is submitted online via Fluid Review (see Application Components below).
• Each student can submit only ONE application to the Lassonde School of Engineering

REQUIRED APPLICATION COMPONENTS

• Contact Details
• Resume (2 pg. maximum)

Research Interest Statement (1 pg.)
Provide examples of both your research interests and any previous experience with research projects either course-based or in a lab. You can include reasons for wanting to participate in a research project, as well as your future aspirations and career plans.

Supervisor Endorsement Form
Your potential supervisor must provide you with the completed and signed Supervisor Endorsement form for you to upload. Click here to access the form.

Transcript
– York University Students – Upload an unofficial transcript (https://registrar.yorku.ca/index.php/transcripts) OR upload a grade report AND course and grade list
– Non-York University Students – Official transcripts are required – consult your registrar’s office for details on how to obtain them.
The award is open to all undergraduate levels and does not depend on the financial needs of the student.

LURA

• LURA is open to ALL full-time undergraduate students who meet the requirements for the project (as determined by the supervisor).
• Students in the Lassonde School of Engineering, other faculties at York University, or other schools across Canada can apply.
• International Students can apply. International students (not enrolled at York) may need to pay registration fees of $600.

USRA

• USRA is open to full-time undergraduate students who meet the requirements for the project, (as determined by the supervisor).
• Applicants must have completed all the course requirements of at least the first year of university study for their degree.
• Applicants must hold the status of either Canadian Citizen or Permanent Resident of Canada.
• The applicant needs to be registered as a full-time student.
• Additional requirements are listed on the NSERC USRA website.
• Aboriginal students are encouraged to apply: NSERC USRAs awarded to Aboriginal students do not count towards the university’s quota.

Lassonde School of Engineering supports an inclusive research environment in support to strengthen the research community through equity, diversity, and inclusion. We encourage and aim to increase the participation of women in the program and strive to reach a balanced representation in the trainees’ population.
Applications will be adjudicated by a selection committee, using the following criteria:

• 60% GPA (cumulative)
• 30% Research Potential (based on the statement of research interest and previous research experience)
• 10% Communication (based on clarity and presentation of the complete application)

In addition to the evaluation criteria above, the eligibility of each application will be dependent upon a satisfactory training plan submitted by the professor on the Supervisor Endorsement Form.
Q: I am currently in my first year, am I likely to win an award at all?
A: Both first-year and upper-year students apply for this program, so it could be a bit more competitive for a first-year. But we still encourage first-year students to apply, even if just to get the experience so that they will hit the ground running with your application the following year. Also, some first-year students do win awards.

Q: Can I apply for LURA or USRA as a fourth-year? If I am going to graduate this winter term, can I apply for this award?
A: Yes

Q: Can you still apply for USRA/LURA if you are graduating in April?
A: YES

Q: Is the NSERC USRA prioritized for upper-year students?
A: The USRA is more competitive and upper-year students may have more experience, but NO it is not prioritized.

Q: The research that I am interested in is USRA and I am an international student. Can I apply?
A: You need to apply with a professor who is willing to host LURA students.

Q: If I do not win a LURA or USRA, are there other opportunities that I can access to gain research experience?
A: You can look into the Research at York (RAY) Program run by the Career Centre https://sfs.yorku.ca/work-study-programs/how-to-apply-for-ray-positions

Q: Are there other ways to find a professor?
A: Yes, reach out to any Lassonde professor, whose work you find interesting. You can also talk to TAs in your favourite classes for guidance.

Q: So, do I have to talk to the professor about this?
A: Yes, The professor who posted a project as USRA may not be willing to host LURA students (they cost more), but you can always contact the professor and ask. Make sure to include your resume and unofficial transcripts in that e-mail.

Q: If we are endorsed for a research position by a prof, do we automatically get an award?
A: No, once you are endorsed by a professor you then submit an application which will be adjudicated via a competitive process. If you are successful in that competition, you will win an award.

Q: I am still waiting on one of my final marks to be posted. Should I wait until the mark is posted before I request the transcript to avoid an NGR on my transcript?
A: You will need to submit your application by the deadline. Students can upload unofficial transcripts.

Q: Are first-year students allowed to apply for the LURA? I have only attended one term.
A: Yes, first-year students may apply.

Q: Is the B average requirement only for USRA or also for LURA?
A: Only for USRA

Q: Is there a registration fee?
A: No

Q: Am I allowed to apply to research with more than one professor?
A: You may discuss projects with more than one professor, but only submit one application. If more than one professor is willing to endorse you, you will need to choose one of them.

Q: Will we receive a copy of the slides after the info session?
A: Click here for last year’s slides and this year’s slides are available here.

Q: When we need to contact a “supervisor”, does that mean the specific professor that is listed under the position we are interested in?
A: Yes

Q: Are professors allowed to endorse more than the number of positions available?
A: Yes, as long as they are willing to host all students who get an award.

Q: Do I need to submit the Research Interest Statement to the professor we want to work with?
A: It is a good idea to send the RI to the professor before applying.

Q: Will the research affect the fall semester?
A: No, the research will be done before the fall semester starts.

Q: Is the interest research statement structured more like a cover letter?
A: Yes, explain how your interests and experiences and skills align with the project

Q: I’m planning on applying to the summer research program with a professor from the Faculty of Health/ Faculty of Science, do I go through Lassonde’s submission process?
A: No, the Lassonde LURA/USRA competition is only to do summer research with a Lassonde School of Engineering professor. Other Faculties will have their process and timelines. Please reach out to program organizers at the respective Faculties.
 
Faculty of Science
Stefanie Bernaudo – sbr@yorku.ca
Faculty of Health (contact Departments directly)
Kinesiology and Health Science (KAHS): ugkhs@yorku.ca 
Global Health (GLBH): sgh@yorku.ca 
Psychology (PSYC): psyc@yorku.ca 
School of Health Policy and Management (SHPM): shpm@yorku.ca 
Nursing (NURS): nursing@yorku.ca
Holding the award

• The award will be held for 16 mandatory weeks, from May to August 2023. During this time undergraduate students will work full-time with a Lassonde researcher to complete a pre-defined research project
• Students may not enrol in more than 3-course credits during the tenure of the award. Applicants are encouraged to discuss this with the potential supervisor.
• Award recipients are required to participate in the Lassonde Undergraduate Student Research Conference.

Before applying

• Before applying for the awards, the applicant is required to identify a supervisor (Lassonde professor), who is willing to support the applicant.

One combined application

• A combined competition is held for both USRA and LURA; students submit one application and are considered for both awards if they meet the eligibility (see below).
• Top applicants will be awarded USRA, and other strong applicants will be awarded LURA.
• This year, there is funding to support over 60 student awards.

Information sessions

Please attend:

Tuesday, January 17, 2023, | 11:00 am – 12:00 pm ET: Live Information Session
Location: Bergeron Centre of Engineering Excellence | Room 125

Tuesday, February 6, 2023, | 6:00 pm – 7:00 pm ET: Zoom Information Session
Meeting ID: 910 2770 5676 | Passcode: 629659 |
Join Here

During each session, the program and application process will be presented and students will have opportunities to ask questions. No pre-registration is required.

If you were unable to attend the information or drop-in sessions, a copy of the slides are available here. Last years are available here and last year’s Zoom recording from the February 11, 2022 info session is here.

Co-op

• Both LURA and USRA positions qualify as Co-op positions. Applicants intending to consider the research award a Co-op term, need to contact the Co-op office ASAP for additional details.

NEW THIS YEAR

Pilot LURA Initiative

We are participating in the “Women in Engineering Co-op Stream” through the Lassonde Co-op Program. It’s a new initiative to increase the retention and recruitment of women in Engineering. As a result, TWO special LURA positions for first-year female engineering students have been created for students that are registered in the program. For more information on this initiative and to assess your eligibility, please contact the Co-op team at lsecoop@lassonde.yorku.ca.

NSERC USRA Indigenous and Black Student Initiative

NSERC encourages qualified Indigenous and Black students to apply to this program. NSERC announced additional support for Black students including unlimited number of positions. The Indigenous and Black student USRA awards are adjudicated alongside the NSERC USRA awards. All applications from Indigenous and/or Black students whose application otherwise meets all qualifications will be put forward to NSERC for funding.
Contact us at resday@yorku.ca

Browse Projects:

Projects will continue to be updated until January 31, 2023. Keep visiting the site for updates. You are encouraged to reach out to professors directly to discover their interest in hosting LURA/USRA students.

Techno-economic Assessment of Drinking Water Supply Options in Nunavut in the Age of Climate Change
Professor: Stephanie Gora
Lab Website: https://lassonde.yorku.ca/users/stephanie-gora
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 1
Project Description: Located in the Canadian Arctic, the territory of Nunavut is situated in a zone of continuous permafrost. Scattered across the vast area of approximately 1.9 million km2 are 25 small fly-in communities (<10,000 people). The remoteness of these communities results in significant challenges in building and operating community water infrastructure.
Water supply systems across the territory are challenged by aging infrastructure, poor operational maintenance, lack of preventative maintenance, and outdated technologies that are poorly suited for the communities’ water sources. Water infrastructure within communities was originally built 20 – 30 years ago, with some systems built as long as 50 years ago. While some communities have seen upgrades in the last 20 years, almost every community has experienced challenges due to their water infrastructure in recent years.
In addition to water infrastructure challenges, the warming in the Arctic due to climate change is altering the hydrological and geological dynamics. Most documented are the glacier retreat, the thinning of sea and lake ice, and permafrost thawing. Permafrost thawing has implications for ground stability, including concerns related to mass movements, hydrological connectivity, biogeochemical cycling, and microbial activity – all of which could impact water supply and infrastructure. The impact is expected to be a major burden on government resources.
Duties and Responsibilities: The student will: conduct a literature review about the impacts of climate change on northern water infrastructure, collect data about the cost of building and operating water infrastructure in Nunavut and other northern territories, perform interviews with government and industry employees, develop accurate and up-to-date cost curves for different water management options considering climate change, and identify funding sources and programs available to communities in Nunavut for water infrastructure projects.
All research activities can be conducted remotely or on York University’s Keele Campus. The student will be invited to join Dr. Gora’s research team meetings, which are held twice a month on campus. Dr. Gora will pay for the student to register as an attendee at the American Water Works Association’s Annual Conference (ACE), a major water conference that will be held in Toronto from June 11th – 14th 2023.
Desired Technical Skills: Skills in Excel, Word, PowerPoint, and R.
Desired Course(s): Civil, chemical, or environmental engineering, environmental science, or environmental studies student.
The ideal applicant will have successfully completed courses related to economics, engineering/scientific communications, water process and/or infrastructure design, and/or environmental engineering/science. Exceptions to this will be considered for high-potential first-year students participating in the Women in Engineering Co-op Stream program at York University.
Other Desired Qualifications: Strong communication skills (writing, presenting, data visualization), Research experience (literature review, scientific method, etc.), and Interest in pursuing research and/or a career related to drinking water infrastructure.
Contact Info:
Stephanie Gora (stephanie.gora@lassonde.yorku.ca)



Microplastics Analysis in Water and Wastewater Samples
Professor:
Satinder Kaur Brar
Lab Website:
https://lassonde.yorku.ca/tabes/satinder-kaur-brar-program-director/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions:
2
Project Description:
Microplastic particles are plastics that are smaller than five millimetres in size. They come from synthetic textiles, city dust, tires, road markings, marine coatings, personal care products and engineered plastic pellets. All these products after usage end up releasing significant amounts of microplastics which eventually end up in sewage systems and migrate from there to surface water sources or soil. This project has two main objectives: pre-treatment of collected surface water/wastewater samples to extract the microplastics for analysis and identification and quantification of microplastics extracted from the collected surface water/wastewater samples using various instruments.
Duties and Responsibilities: Work on a variety of research projects including microplastic analysis and usage of highly sensitive and sophisticated equipment related to microplastic detection. Write reports to summarize data and the implications of the results. Assist the Ph.D. students to run the experiments. Analyzing the data collected from these studies, drawing high-level conclusions and recommendations. Consolidating insights on specific questions from various studies and sources. Analyze research findings and develop presentations, dashboards, models, infographics, and other items to support effective decision-making. Learn qualitative research methods and protocol analysis.
Desired Technical Skills:
Skills in oral and written communication, project management, equipment calibration, troubleshooting and maintenance, data analysis and problem-solving, and demonstrated proficiency in MS Word, Excel, SharePoint and Outlook.
Desired Course(s):
Courses related to chemistry, biochemistry or analytical chemistry.
Other Desired Qualifications:
Knowledge of chemistry, biochemistry, and polymer science.
Contact Info:
Rama Pulicharla (ramapuli@yorku.ca)



Wood and Timber Engineering for Fire
Professor: John Gales
Lab Website: https://yorkufire.com/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 2
Project Description: York University is a member of a multi-university endeavour to develop engineered timber structures as part of the Next Generation Wood program. As such the YorkU fire research team is undertaking multiple research projects to consider how to best design these structures for fire safety. The research will also consider sustainability objectives for optimizing these structures’ environmental portfolios, as well as consideration of adaptation of heritage buildings made of historic wood for modern use. The research will include a literary review, experimental experience, and basic modelling.
Duties and Responsibilities: The students will be expected to work as part of a diverse research team in labs, and fieldwork. Students will also conduct literary review tasks and basic computational modelling. Students will also be expected to assist research team members in a variety of other material and fire-specific projects.
Desired Technical Skills: Communication and leadership skills are an asset.
Desired Course(s): Engineering students.
Other Desired Qualifications: See desired technical skills.
Contact Info:
John Gales (jgales@yorku.ca)



Cost-Effective Enzyme Booster Technology for Petroleum Hydrocarbon Biodegradation in Cold Regions
Professor: Satinder Kaur Brar
Lab Website: https://lassonde.yorku.ca/users/satinder-brar
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: In this project, we’re going to develop an enzyme booster bioremediation technique that’s green. In addition, it can make enzyme-based site-specific cocktails for an affordable price.
Duties and Responsibilities: Under the supervision of a postdoctoral fellow, the selected candidate will participate in laboratory activities. By participating in the proposed training, the student will gain hands-on experience handling bacteria, isolating them from the soil, optimizing their growth, and identifying them. It will also help to learn good lab practice and the importance of biosafety precautions. To begin working in the lab, the student must pass biosafety training with a minimum score of 80%. During the training period, the student will become familiar with handling biohazardous materials and operating basic lab instruments such as a laminar flow, a spectrophotometer, a weighing balance, a shaking incubator, and a centrifuge. This will improve their technical proficiency. For the student to improve their learning skills and writing skills, they will need to read the literature. This will enable them to understand how the instruments they are using and the experiments they are conducting work. They will attend the weekly group meeting of our lab and also provide a weekly work report.
Concerning the project, the main aim of this project is to produce and characterize enzymes from polycyclic aromatic hydrocarbon-degrading bacteria. The student will perform the following activities: Production of enzymes from multi-culture systems and characteristics of enzymes.
Desired Technical Skills: A basic understanding of microbiology, such as the preparation of media and culture.
Desired Course(s): Courses in Microbiology, and Biology.
Other Desired Qualifications: Understanding the basic principles of reactor design.
Contact Info:
Seyyed Mohammadreza Davoodi (reza19@yorku.ca)



Measuring the Impact of Climate on Sustainable Concrete Materials
Professor: Liam Butler
Lab Website: https://lassonde.yorku.ca/users/liam-butler
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: The influence of climatic variations on Canada’s vast infrastructure stock, valued at over $850 billion, are largely ignored in infrastructure design. Variations in temperature, relative humidity, solar radiation, and precipitation, along with the increased frequency of extreme events will likely lead to cyclic fatigue and other factors that influence the behaviour of infrastructure materials. To begin to mitigate these adverse impacts, we must explore ways to reliably measure and to better understand the effects of naturally varying climates on infrastructure. On the mitigation front, great advances are being made to produce new construction materials which can be manufactured with significantly reduced carbon footprints. One such example includes low-carbon concretes which incorporate alternative cementitious and recycled materials. Although much research has been completed to better understand the mechanical properties of these materials, there is very little information on their durability characteristics under realistic conditions.
This exciting opportunity in our research group for either a LURA or a USRA student involves undertaking research to better understand how climate influences the behaviour of sustainable and low-carbon concrete structures. The selected research assistant will be responsible for planning and executing an independent research project at the Climate-Data-Driven Design (CD3) Facility for Built Infrastructure; a unique outdoor laboratory facility at York University for testing the impact of realistic climate factors and loading on the long-term performance of infrastructure materials. This research provides the basis to address several of the U.N.’s sustainable development goals including, SDG #9 Industry, Innovation, and Infrastructure; SDG #11 Sustainable Cities and Communities; SDG #12 Responsible Consumption and Production, and SDG #13 Climate Action.
As part of their day-to-day activities, the research assistant will collaborate with other researchers in our group (i.e., graduate students) to process, analyze, and interpret experimental results. They will also compare current code-based design provisions for sustainable concrete materials with experimental results and report their findings through written reports and technical articles. It is anticipated that the research conducted will yield results which may be published as conference proceedings and/or as technical journal articles.
Duties and Responsibilities: Undertaking a brief review of current design and research literature pertaining to low- or net-zero carbon concrete structures, designing, fabricating and instrumentation of sustainable concrete material specimens, performing data processing of experimental data obtained from sensors and weather station readings during outdoor testing of sustainable concrete specimens, analyzing and interpreting experimental data using statistical and other analytical approaches, reporting on experimental findings through the report and/or technical article writing, and presenting on overview of the research project and findings at the end of the term.
Desired Technical Skills: Working knowledge of MS Word, MS Excel and/or Python and/or MATLAB. Excellent written and verbal communication skills. Knowledge and past research experience working with reinforced concrete structures and materials would be considered an asset.
Desired Course(s): Engineering students with completed courses related to engineering materials, statistics, and mechanics of materials however, civil engineering students (2nd year or above) are preferred.
Other Desired Qualifications: A passion and curiosity for discovery and research. The ability to think independently and to collaborate effectively in a team environment. Excellent written and verbal communication skills and a commitment to maintaining an inclusive and welcoming working environment.
Contact Info:
Liam Butler (liam.butler@lassonde.yorku.ca)



Evaluation of a Pilot Water Treatment System at the Ajax Water Treatment Plant
Professor: Stephanie Gora
Lab Website: https://lassonde.yorku.ca/users/stephanie-gora
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Pilot drinking water systems are small scale systems designed to mimic full size municipal drinking water systems. Water system operators use pilot systems to test out potential operational changes to their full scale systems. For example, a new treatment chemical gets tested on the pilot scale system to see how it affects finished water quality before it gets added to the full scale treatment process. The pilot water treatment plant at the Ajax Water Treatment Plant (WTP) was built 20 years ago and is no longer operating optimally.
The main goal of this summer research project is to evaluate the existing pilot water treatment system at the Ajax WTP and determine how much it will cost to update it. The summer research student hired for this position will complete a condition assessment of the existing pilot system, identify any pieces of equipment that are no longer functioning or in poor condition, and create a detailed cost estimate for replacing them. The student will also conduct desktop research on municipal drinking water systems and the design and operation of pilot water treatment systems.
Duties and Responsibilities: The student will conduct regular visits to the Ajax Water Treatment Plant throughout May 2023. While there, they will review and assess the existing pilot system with operations staff, gather and analyze reports, drawings, and data associated with the pilot; and catalogue the equipment and consumables available in the laboratory at Ajax WTP. The student will conduct periodic visits to the pilot in June-August 2023, but otherwise will conduct preliminary research about pilot water systems; analyze reports and drawings about the Ajax pilot water system; contact equipment manufacturers to obtain quotes for equipment required to update the pilot water system and prepare a final cost estimate and report about their research findings. Regular meetings will be held by Zoom with the Region of Durham staff.
Over the course of the summer the student will also visit pilot drinking water systems at Toronto Water, Peel Region, and Peterborough with Dr. Gora and/or her graduate students.
The student will be invited to join Dr. Gora’s research team meetings, which are held twice a month on campus. Dr. Gora will pay for the student to register as an attendee at the American Water Works Association’s Annual Conference (ACE), a major water conference that will be held in Toronto from June 11th – 14th 2023.
This project will provide the opportunity to build a network of professional contacts with industry partners and further advance research skills and personal and professional development. Dr. Gora is a Professional Engineer and can sign off on pre-graduation engineering experience.
Desired Technical Skills: Excel, Word, Power Point, and R.
Desired Course(s): Civil, mechanical, chemical, or environmental engineering students.
Other Desired Qualifications: Required: Strong communication skills (oral, writing, presenting, data visualization), interest in pursuing research and/or a career related to drinking water infrastructure, ability to work autonomously and professionalism.
Desirable: Experience building or repairing small equipment, research experience (literature review, scientific method, etc.), and experience conducting cost estimates.
Contact Info:
Stephanie Gora (stephanie.gora@lassonde.yorku.ca)



Chemical Transport Through an Engineered Barrier to Used Nuclear Canisters
Professor: Magdalena Krol
Lab Website: https://mkrol.info.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Several countries, including Canada, Finland, Switzerland, and the United Sates are studying long – term solutions for the storage of used nuclear fuel. Current designs include the use of deep geological repositories (DGRs) that would be located several hundred meters below ground level. DGRs will house used fuel canisters (UFCs) which are usually surrounded with multiple engineered barriers, each playing a different role within the DGR. In Canada, the Nuclear Waste Management Organization (NWMO) is responsible for the design and implementation of the DGR. The current NWMO DGR design includes a steel container, a copper coating that acts as a corrosion barrier, and highly compacted bentonite (HCB) that surrounds the UFC. HCB can suppress the movement of corrosive agents to the UFC, thereby preventing corrosion of the canister. In this research project, the behaviour of the HCB under several different repository conditions will be investigated using lab experiments in order to understand how the HCB will perform as a barrier against the transport of potentially corrosion-inducing compounds. When the repository is sealed, these compounds can diffuse through the HCB and lead to microbially influenced corrosion of the UFC. Successful candidates will work in the Civil Environmental laboratory and conduct diffusion and sorption experiments to identify the diffusion/sorption parameters under different repository conditions.
Duties and Responsibilities: The undergraduate students will work closely with graduate students as well as postdoctoral fellows and the faculty supervisor, Professor Magdalena Krol. One student will be expected to work in the Civil-Environmental lab and will receive biosafety training as well as training on various laboratory analytical devices. Their responsibilities will include running diffusion and/or sorption tests and performing general lab activities. The student will also be involved in report writing, presentations, and group meetings. The second student will work with COMSOL Multiphysics platform, and their responsibilities will include the development of the transport model and testing it for quality assurance purposes. Both students will also be involved in report writing, presentations, and group meetings.
Desired Technical Skills: Good foundation in chemistry and interest in environmental issues; good communication skills and a team player; Experience working the lab and/or experience working with various simulation platforms is an asset.
Desired Course(s): Finished third year of civil engineering undergraduate program.
Other Desired Qualifications: Good communication skills, organized, and able to follow instructions.
Contact Info:
Magdalena Krol (mkrol@yorku.ca)



Thermo-Mechanical Design of Optimum Energy Piles for Canadian Environment
Professor: Kamelia Atefi-Monfared
Lab Website: https://lassonde.yorku.ca/users/catefi
Position Type:
NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Energy piles are dual-use geotechnical structures that, in addition to their original structural function, they are also designed to act as a heat exchanger for transferring heat from and/or into the underlying ground. Such structures enable the use of shallow geothermal energy, a renewable energy source, by taking advantage of relatively constant Earth temperatures throughout the year in depths of ~10 – 20 m. Energy foundations have been commonly implemented in reinforced concrete piles. Closed-loop, flexible tubing is placed within the pile reinforcement cage, through which a heat carrier fluid is circulated to extract heat from the ground. The extracted heat from the energy foundation can be used for several applications, including space heating. In the summertime, excess heat from the building can be stored in the ground, thus providing cooling. Space heating and cooling represent a substantial percentage of global total energy demands, and the majority of this demand is supplied through fossil fuels, thus a significant contributor to carbon dioxide emissions.
The additional thermal loads generated in the pile and the surrounding soils during the energy pile operation can induce expansion and contraction in the pile as well as in the surrounding porous media, which may influence the load bearing mechanism and settlements in piles and thus the serviceability and safety of the built structure. Furthermore, different soil types and on-site conditions can induce a very different thermo-mechanical response of the energy pile under thermal loads. This research project is aimed to evaluate the geomechanics of energy piles and their interaction with the surrounding porous media in Canadian environment, and to assess the thermo-mechanical performance of the pile in challenging soils. The objective is to obtain an in depth understanding of the fundamental mechanisms involved in load bearing of piles and how thermal loads can influence the aforementioned, based on which to propose guidelines for optimum design of energy piles based on Canadian climate. To achieve this, coupled numerical modelling will be conducted.
Duties and Responsibilities: Training of the numerical software will be provided to the students. Students will be: conducting a literature review, following modelling guidelines provided to them to develop and run the model, conducting data analysis, presenting deliverables during weekly meetings, and preparing a final report.
Desired Technical Skills: Numerical modelling, computational skills, data analysis, strong background in mechanics of materials and geotechnical engineering.
Desired Course(s): A strong background in LE/CIVL 2220 4.00 – Mechanics of Materials and LE/CIVL 3210 3.00 – Geotechnical Engineering.
Other Desired Qualifications: Motivated individuals with strong communication skills.
Contact Info:
Kamelia Atefi-Monfared (catefi@yorku.ca)
Satellite Technologies for Space Situational Awareness
Professor: Regina Lee
Lab Website: https://nanosatellite.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Space systems play an important and integral role in every facet of our daily lives, including national security and resource management. Therefore, it is critical to protect our valuable assets in space. In recent years, the growing numbers of resident space objects (RSOs) have become a significant concern worldwide. In an ongoing research project (in partnership with several industry and government partners) we examine a novel approach to develop technologies for space surveillance and space situational awareness (SSA-continuous monitoring of conditions and threats in space). In particular, we use wide field-of-view (FOV), low resolution imagers for space-based detection, tracking, identification and characterization of RSOs, including natural/artificial, friendly/adversarial space assets, debris, near-Earth asteroids, space-related threats, and space hazards; and to support the upcoming SSA Microsatellite mission.
Working closely with the graduate students in the team, the students will investigate techniques for RSO observation prediction, tracking algorithm implementation on FPGA and RSO identification. Students will support field campaign, design, test, integrate and demonstrate the proposed payload for an upcoming microsatellite mission.
Duties and Responsibilities: Develop algorithms for object detection, FPGA implementation, and support field work.
Desired Technical Skills: Experience with FPGA programming, Python, and MATLAB.
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact Info:
Regina Lee (reginal@yorku.ca)



Autonomous Unmanned Vehicles (AUVs): Control and Navigation
Professor: Jinjun Shan
Lab Website: https://lassonde.yorku.ca/users/jjshan
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Autonomous Unmanned Vehicles (AUVs) are systems that are capable of manoeuvring in the air, on the ground, above, or under the water. There are a number of potential applications for AUVs in civilian, military, and security areas. For example, defence patrol duties, agricultural activities, forest fire monitoring and control, grid monitoring, boarder control, search, surveillance, and rescue. AUVs have great potential benefits to Canada for numerous reasons; many of which are connected with our large uninhabited land, the largest area of forests in the world, and the longest international border in the world. This project is to develop control and navigation algorithms for AUVs for indoor and outdoor surveillance and monitoring applications.
Duties and Responsibilities: The successful students will be working with graduate students and research fellows on (1) programming; (2) hardware development; (3) hardware tests. Through these activities, the students will gain experience in control and navigation system design, hardware and software development, etc. These experiences will be very helpful for the student’s future study and work.
Desired Technical Skills: Good programming skills in MATLAB, and Linux, Enrolled in an engineering or science degree, Familiar with ROS, Team player.
Desired Course(s): Engineering or science students.
Other Desired Qualifications: In-person work is needed.
Contact Info:
Jinjun Shan (jjshan@yorku.ca)



Improvement of Smartphone Positioning
Professor: Sunil Bisnath
Lab Website: https://gnsslab.lassonde.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Low-cost GPS, and now broadly GNSS (Global Navigation Satellite System), chips in smartphones are increasing in capabilities. Coupled with users being able to access raw measurements from some models, the York GNSS Lab has been applying high-performance measurement processing techniques to improve positioning accuracy from 10s of metres to the decimetre level. This solution can also be integrated with measurements from other smartphone sensors, such as inertial measurement unit (IMU). Applications for such performance include: navigation, augmented reality apps, gaming, etc. Research areas of interest include: precision of raw satellite ranging measurements, GNSS/IMU integration, optimal estimation, performance of cellphone antennas, availability of measurements in obstructed, urban environments, tuning of processing methods for such measurements, and solution testing.
Duties and Responsibilities: Working with a team of PDF and PhD students in the collection, analysis, and processing of smartphone GNSS data, and the tuning of GNSS measurement processing algorithms. This work is globally leading-edge, so there is the high likelihood of conference and journal paper preparation experience as well.
Desired Technical Skills: Knowledge of GNSS specifically, optimal estimation, and geomatics in general, would be very helpful.  As well as the scientific method, and data analysis skills. Coding ability in, e.g. Matlab, Python, C/C++ will be very helpful.
Desired Course(s): Current BEng or BSc student in geomatics, space, computer, electrical, computer science or a related field. Having taken LE/ESSE 3670 3.00 – Global Navigation Satellite Systems or equivalent would be very helpful, but not necessary.  Strong math and coding backgrounds are significant assets.
Other Desired Qualifications: A quick learner, focused and organized, strong communications skills, proven ability to work in a group and individually, highly motivated, can work independently, and is interested in graduate school.
Contact Info:
Sunil Bisnath (sbisnath@yorku.ca)
Lock-Free Data Structures
Professor: Eric Ruppert
Lab Website: www.cse.yorku.ca/~ruppert
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: This project is concerned with the development of concurrent data structures, which can be accessed simultaneously by multiple processes. These data structures are essential for harnessing the full power of modern multicore architectures. Processes must carefully coordinate their accessto the analyze of the data structures to ensure that simultaneous updates do not cause shared data to become corrupted. The goal of the project is to develop new concurrent data structures with good time complexity.
Duties and Responsibilities:
Some background reading will be required, followed by work on the development of a new data structure and a proof of correctness for it.
Desired Technical Skills: Ability prove correctness of algorithms and analyse their complexity.
Desired Course(s): Computer science or software engineering students who have excelled in LE/EECS 3101 3.00 – Design and Analysis of Algorithms. LE/EECS 4101 3.00 – Advanced Data Structures is also helpful as a background.
Other Desired Qualifications: Strong math background.
Contact Info:
Eric Ruppert (ruppert@cse.yorku.ca)



Federated Learning in Edge Computing
Professor: Ping Wang
Lab Website: https://www.eecs.yorku.ca/~pingw/
Position Type: NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: In the traditional machine learning (ML) approach, data collected by local nodes is uploaded to the data centre and processed centrally, where formidable computational resources can be exploited. However, this approach is no longer suitable for edge computing networks. Firstly, users’ data is more privacy-sensitive than ever. Secondly, transferring data generated by a large number of nodes for processing burdens the network and will bottleneck the overall performance. Thirdly, the centric fashion involves a long propagation delay and incurs unacceptable latency, which is unbearable for many applications with instantaneous decision-making the heterogeneity of the system to fill a buffer pool. Therefore, a natural question arises with the concerns mentioned above: how can one train an ML model from decentralized data at a resource-constrained edge node? Federated Learning (FL) is a technique that fulfills this purpose. FL is an ML setting where many nodes collaboratively train a model under the orchestration of a central server (e.g. service provider) while keeping training data decentralized. However, FL also faces challenges. One major challenge is system heterogeneity. FL involves the heterogeneous participants whose local dataset, computational ability, channel condition, power level and willingness to participate may vary. Given the systems heterogeneity, an optimal strategy for resource allocation needs to be developed to maximize the efficiency of FL. Another challenge is communication costs during glosort helpthe thembal model training. Compared with the traditional distributed machine learning, where several computational centres are involved, the FL framework is usually related to a large number of edge nodes, incurring an extremely high communication cost. Therefore, the effort to reduce communication costs (e.g., model compression) is needed to improve efficiency. This project will focus on developing cutting-edge FL techniques to address these challenges.
Duties and Responsibilities: The student will gain sufficient knowledge of federated learning and gain hands-on experience in implementing federated learning algorithms. The student will do some research in the relevant field, with mentoring from senior graduate students.
Desired Technical Skills: Good at coding in Python. Have basic knowledge of machine learning.
Desired Course(s): A computer science student is preferred.
Other Desired Qualifications: Good GPA, and self-motivated.
Contact Info:
Ping Wang (pingw@yorku.ca)



Online Knapsack Models
Professor: Elisabet Burjons
Lab Website: https://lassonde.yorku.ca/users/eburjons
Position Type: Start-up
Open Positions: 1
Project Description: In the online knapsack problem an algorithm receives items one by one with the goal of filling up a knapsack as full as possible, but the algorithm must make a decision on every item before the next item arrives. A lot of variations exist for this problem trying to optimize the algorithms to work in different settings. This problem sets benchmarks for other online algorithms which have practical applications in optimization. This project should look into the existing models for the knapsack problem and find a new result for one of the existing models.
Duties and Responsibilities: Survey the existing models for the online knapsack problem and understand what the existing results cover. Then try to find a new result from the existing research. At the end a report should be written with the conclusions.
Desired Technical Skills: Good understanding of algorithms and discrete mathematics.
Desired Course(s): LE/EECS 3101 3.00 – Design and Analysis of Algorithms.
Other Desired Qualifications: A good grade in LE/EECS 3101 3.00 – Design and Analysis of Algorithms.
Contact Info:
Elisabet Burjons (burjons@yorku.ca)



Tuning up Big Data Systems with Machine Learning
Professor: Jarek Szlichta
Lab Website: https://www.yorku.ca/lassonde/lab/data-science/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 3
Project Description: Modern data systems such as IBM DB2 have dozens of configuration parameters (e.g., buffer pool, sort heap and parallelism degree) that heavily influence their performance. Since manual specification is cumbersome, we propose an automatic tuning system via deep reinforcement learning through actor-critic networks. We utilize transfer learning, as training machine learning models only on execution times of business queries is prohibitively expensive for large workloads. Thus, we plan to train models first on the estimated costs of queries and then fine-tune it on execution times. We also translate high-dimensional query execution plans into a low-dimensional embedding space.
Duties and Responsibilities: Students’ duties and responsibilities will include: reviewing related work in automatic knobs tuning for data systems, designing large-scale machine learning-driven approaches to the tuning of configuration parameters, implementing the solution with the deep reinforcement learning model, conducting comprehensive experimental evaluation over synthetic and real-world query workloads, and writing a research paper to be submitted to one of the top-tier conferences in data science, such as VLDB, ACM SIGMOD, IEEE ICDE and EDBT.
Desired Technical Skills: Have algorithmic design and development knowledge, and proven strong programming skills.
Desired Course(s): Recommended to have completed some of the data science courses such as LE/EECS 4415 3.00 – Big Data Systems, LE/EECS 4411 3.00 – Database Management Systems, LE/EECS 4412 3.00 – Data Mining, LE/EECS 4404 3.00 – Introduction to Machine Learning and Pattern Recognition, LE/EECS 3421 3.00 – Introduction to Database Systems, etc.
Other Desired Qualifications: Other qualifications include good communication skills.
Contact Info:
Jarek Szlichta (szlichta@yorku.ca)



Development of Wearable Microfluidic Patches Integrated with Organic Electrochemical Transistor (OECT) Sensors for the Detection of Inflammatory Biomarkers in Sweat 
Professor: Razieh (Neda) Salahandish 
Lab Website: https://lassonde.yorku.ca/users/rsalahandish 
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions:
Project Description: Continuous and point-of-care detection of inflammatory biomarkers can be a major breakthrough for the prevention of a large number of secondary diseases caused by chronic inflammation, such as cancer, diabetes, or heart diseases. With the emerging evidence on the existence of multiple protein biomarkers associated with inflammatory conditions in sweat samples of individuals, there is hence an incredible opportunity to devise on-skin patches allowing for the smart detection of such complex biomolecules. Although some efforts have been made to recognize protein biomarkers using in-vitro diagnostic platforms, powered by electrochemical or optical techniques, the complexity of these methods, as well as the lack of advanced technologies for providing a sufficient level of miniaturization and automatization for near-patient use has impeded their clinical applicability and scalable use. Capillary microfluidic systems, provide a significant potential for automating biological assays and can be accompanied by different types of sensors and electronic readouts. On the other hand, multiple complex biomarker detections, i.e. protein detection, can be executed via highly sensitive and at the same time less cumbersome methods, including transistor-based techniques. In this project, an organic electrochemical transistor (OECT) based immunosensor will be devised for detecting principal protein biomarkers, in a miniaturized manner for being compatible with microfluidic platforms. The system will be developed incorporating a printing technology for the fabrication of nanostructure-contained smart inks and will be tested using chemical and physical characterization techniques to ensure its robustness and sensing reliability. A capillary microfluidic system will also be fabricated in a wearable format for collecting sweat samples from designated areas on the skin, and delivering them to the sensing unit, allowing for proper interaction of the target analyte with the recognition molecules, while preventing evaporation and contamination. The microfluidic platform will be composed of functional layers from medical bonding layers to thermoplastics, laser cut to yield specific operations. The performance of the integrated system will be assessed by performing on-chip detection assays using protein-spiked samples in artificial sweat. This project will establish the foundations of creating compact tools equipped with miniaturized sensors for on-site detection of complex biomarkers, with profound potential for transforming the field of engineering smart sensors and in-vitro diagnostic medical devices, for enhanced monitoring and detection of chronic inflammation. 
Duties and Responsibilities: For this project, I will hire three undergraduate students in order for conducting experimental and practical laboratory-based parts of the research. 
Students 1 and 2: With a background in either electrical engineering, chemistry, or material/chemical engineering they will be responsible to help my research assistants in fabricating the nano-biosensor. The students will be trained on and are expected to perform various chemical/electrochemical laboratory tasks such as preparation of solutions, synthesis of several nanomaterials including MXenes, preparing screen-printing inks and performing printing process for electrodes, conducting electrical measurements associated with the characterization of the sensor, basic data analysis, literature review, and drafting scientific reports and documents. 
Student 3: With a background in mechanical or electrical engineering, they will assist the researchers in my lab in the design and fabrication of wearable microfluidic chips. The student will be trained on the basics of technical drawing and creating computer-aided design sketches, fabrication techniques such as laser cutting and printing, and fundamentals of microfluidics and medical device design. The student will become familiar with high-level diagnostic research and gain hands-on experience while executing microfluidic fabrication, characterization tests, and CAD designs. 
Desired Technical Skills:
Students 1 and 2: Familiarity with basics and fundamental knowledge of chemistry, chemical reactions, electrochemistry, the process of material synthesis, and concepts of transistors and electrical circuits.
Student 3: Familiarity with fluid mechanics basics; concepts of pressure, velocity, hydraulic resistance, and capillary flow. Familiarity with technical drawings and engineering designs, and manufacturing methods. 
Desired Course(s):
Student 1 and Student 2: Electrical engineering, chemical engineering, chemistry, or material sciences students with background courses in general chemistry and fundamentals of electrical circuits.
Student 3: A mechanical, or electrical engineering student with a background in fabrication techniques and engineering design.
Other Desired Qualifications: As the nature of the research projects in my lab demands collaborative efforts, I welcome students who are eager to perform teamwork, excel at communication skills, problem-solving and critical thinking, and are fast learners. 
Contact Info: Razieh (Neda) Salahandish (raziehs@yorku.ca)



Audio-Video Scene Recognition
Professor: Richard P. Wildes
Lab Website: https://vision.eecs.yorku.ca/main/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Scene recognition in videos refers to the task of leveraging a temporal sequence of images to identify scenes (e.g., flowing rivers guides, vehicular traffic, crowds of people). This ability is important for artificial intelligence, as it helps a system understand its environment and thereby guide subsequent operations. Temporal and spatial features (e.g., texture and motion) are the most important properties to extract from videos lacking audio for scene classification. While state-of-the-art algorithms yield promising results on this task, their performance greatly degrades when visual clues are not well captured (e.g. hampered by occlusion from obstacles). Thus, another source of information should be helpful. The audio signal is perhaps the most reliable accompanying information source but is little considered in current computational video understanding. This project will explore this novel approach to scene recognition, i.e. combining audio and video for enhanced overall performance.
Duties and Responsibilities: The student will: Review the relevant literature on AI-based audio-video scene recognition and processing, gather and annotate videos from the web to augment our lab’s database, experiment with audio-video scene recognition algorithms, present their work at the LSE UG Research Conference, and attend lab meetings.
Desired Technical Skills: Experience with Python and PHP programming as well with UNIX and Windows OS, mathematical maturity equivalent to at least second-year LSE undergraduate, and good communication skills.
Desired Course(s): Required: At least second year completion of undergraduate degree or equivalent in computer science, software engineering or computer engineering. LE/EECS 3451 4.00 – Signals and Systems, and LE/EECS 4422 3.00 – Computer Vision are an asset.
Other Desired Qualifications: Ability to work in a small group.
Contact Info:
Richard P. Wildes (wildes@cse.yorku.ca)



Human-Computer Interaction in Virtual Reality
Professor: Robert Allison
Lab Website: https://percept.eecs.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 2
Project Description: Students will help design, develop and conduct experiments related to human-computer interaction in virtual environments and digital media. In our lab, we have a wide range of apparatuses to study human perception in computer-mediated worlds including a new and unique fully immersive virtual environment display. The student will develop interactive 3D virtual worlds and conduct experiments to study self-motion perception, visual perception, and human-computer interaction in these virtual worlds. In particular, working with a senior graduate student or postdoctoral fellow, the successful applicant will model 3D environments, render them in virtual reality or other digital media display, develop/implement interaction methods to control and interact with the simulation, and/or develop and run experimental scenarios to investigate these issues with human participants.
Duties and Responsibilities: Depending on skills and preparation the student would be responsible for: literature reviews and research, design of virtual environments, computer programming, testing, recruiting participants, conducting user studies, modelling and data analysis, preparation of reports, graphics and presentations.
Desired Technical Skills: Good programming skills, previous work with computer graphics or virtual reality would be helpful, as would basic mechanical skills. Students with background in psychology and interest in experimental psychology are also welcome to apply. Artistic background or skill would be an asset but is not required.
Desired Course(s): Digital media, electrical engineering, computer engineering, computer science, psychology, or vision science students.
Other Desired Qualifications: Students should be self-directed and work well in a team environment.
Contact Info: Robert Allison (allison@eecs.yorku.ca)



Evidence Synthesis: Development of a Web-Based Application to Assess Systematic Reviews Reported with PRISMA 2020 and SEGRESS
Professor: Alvine Boaye Belle
Lab Website: https://lassonde.yorku.ca/users/alvinebelle
Position Type:
Lassonde Undergraduate Research Award (LURA)
Open Positions: 1
Project Description: Systematic reviews aim to provide high-quality evidence-based syntheses for efficacy under real-world conditions and allow understanding the correlations between exposures and outcomes. They are increasingly popular and have several stakeholders (e.g., healthcare providers, researchers, educators, students, journal editors, policymakers, and managers) to whom they help make informed recommendations for practice or policy. However, they usually exhibit low methodological and reporting quality. To tackle this, reporting guidelines (e.g., PRISMA 2020, SEGRESS) have been developed to support systematic reviews reporting and assessment. Following such guidelines is crucial to ensure that a review is transparent, complete, trustworthy, reproducible, and unbiased. However, systematic reviewers usually fail to adhere to existing reporting guidelines, which may significantly decrease the quality of the reviews they report and may result in systematic reviews that lack methodological rigour, yield low-credible findings, and may mislead decision-makers. To assess the extent to which a review is well-constructed, we will develop a checklist-based approach that embodies the most common features of reporting guidelines. We will then derive from that approach a quantitative measure that allows assessing the systematicity of reviews i.e. the extent to which they exhibit good methodological and reporting quality. We will implement the proposed approach as a web-based tool. We will conduct experiments on reviews published in the computing area to assess their systematicity and make some recommendations to improve that systematicity.
Duties and Responsibilities: Exploring the literature on systematic reviews and reporting guidelines, developing a web-based tool supporting the assessment of systematic reviews, collecting systematic reviews from well-established databases (e.g., Google Scholar, IEEE Xplore, ACM Digital Library) and focusing on the computing area, and generating charts allowing to visually represent the results obtained from the experiments conducted on the systematic reviews.
Desired Technical Skills: Good oral and written skills in English, the ability to write scripts in Python, good web-based development skills (e.g. good knowledge of JavaScript/Angular frameworks, Java Spring framework, AWS) and familiarity with git. (e.g. GitHub)
Desired Course(s):
Fourth-year computer science student. The student should have completed LE/EECS 4413 3.00 – Building E-Commerce Systems, or a similar course, or should have industrial experience (e.g. internship) on web-based development.
Other Desired Qualifications: N/A
Contact Info: Alvine Boaye Belle (alvine.belle@lassonde.yorku)



An Online Tool to Refactor Assurance Cases
Professor: Alvine Boaye Belle
Lab Website: https://lassonde.yorku.ca/users/alvinebelle
Position Type:
Lassonde Undergraduate Research Award (LURA)
Open Positions: 2
Project Description: The expression assurance case is a general term that includes dependability cases, safety cases, security cases, and trust cases. According to SACM (Structured Assurance Case Metamodel), an assurance case is a “set of auditable claims, arguments, and evidence created to support the claim that a defined system/service will satisfy particular requirements”. An assurance case is a document that eases the exchange of information between various system stakeholders (e.g. suppliers, acquirers), and between the operator and regulator, where the knowledge regarding a system’s requirements (e.g. safety, security, reliability) is convincingly conveyed. Assurance cases are structured as a hierarchy of claims, with lower-level claims drawing on concrete evidence, and also serving as evidence to justify claims higher in the hierarchy. Assurance cases rely on arguments that connect sub-claims to the top claim (main claim) and describe the exact way in which justification of the top claim is based on the sub-claims. The top claim is a statement such as a system supports non-obvious requirements. In assurance cases, concrete facts such as algorithms, test results, formal reviews, simulations, resource diagrams as well as various system artifacts can serve as evidence relevant to desirable requirements. This evidence is combined with arguments demonstrating how that evidence supports the non-obvious requirements. The most-used graphical notation to represent assurance cases is the goal structuring notation (GSN). In an assurance case, the nature of the support of each child’s claim to its parent claim can be specified in the GSN using support patterns. Still, several assurance cases do not yet rely on such support patterns. We will therefore develop a web-based tool that will help refactor assurance cases represented in GSN by applying support patterns. This will increase the correctness of assurance cases by making sure the inferential links between each parent claim and its supporting children (i.e. child claims) are properly represented/structured.
Duties and Responsibilities: Exploring the literature on assurance cases and support patterns, and developing a web-based tool supporting the refactoring of assurance cases based on support patterns.
Desired Technical Skills: Good oral and written skills in English, good web-based development skills (e.g. good knowledge of JavaScript/Angular frameworks, Java Spring framework, AWS), and Familiarity with git. (e.g. GitHub)
Fourth-year computer science student. The student should have completed LE/EECS 4413 3.00 – Building E-Commerce Systems, or a similar course, or should have industrial experience (e.g. internship) on web-based development.
Other Desired Qualifications: N/A
Contact Info: Alvine Boaye Belle (alvine.belle@lassonde.yorku.ca)



Long Duration Marine Autonomy
Professor: Michael Jenkin
Lab Website: https://vgr.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 3
Project Description: Monitoring water bodies for contaminants requires the development of autonomous vessels that can operate for long periods of time without human interaction. This project involves refinement and enhancement of the Eddy II autonomous surface vessel platform in order to enable it to run on solar power. Projects planned for this summer include: automatic docking alongside a floating dock and log duration path execution while monitoring solar charging. The vast majority of the work will take place on Stong Pond, although occasional deployment on small lakes roughly 1-2 hours from Toronto are planned for the summer.
Duties and Responsibilities: Hardware and software development, robot deployment, and mission log visualization.
Desired Technical Skills: Good Python skills, and the ability to work in a group as well as independently.
Desired Course(s): Upper year students in computer science, computer engineering, or software engineering.
Other Desired Qualifications: Ability to work independently, and good safety skills.
Contact Info: Michael Jenkin (jenkin@yorku.ca)



Avatars to Support Museum Visitors
Professor: Michael Jenkin
Lab Website: https://vgr.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Working with an external partner, this project involves refinements to an existing avatar system to support interactions in a museum with visitors. This avatar engages in a dialog with visitors to the facility, answering questions, and monitoring the general state of the visitor.
Duties and Responsibilities: Software development, interaction with external industrial partner, and attending to test site to monitor system deployment.
Desired Technical Skills: Good Python programming skills.
Desired Course(s): Second or third year students in computer science, or computer engineering.
Other Desired Qualifications: Ability to work well with industrial partners and the museum.
Contact Info: Michael Jenkin (jenkin@yorku.ca)



Privacy in Sociotechnical Systems
Professor: Yan Shvartzshnaider
Lab Website: https://www.yorku.ca/lassonde/privacy/
Position Type:
NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Modern sociotechnical systems share and collect vast amounts of information. These systems violate users’ privacy by ignoring the context in which the information is shared and by implementing privacy models that fail to incorporate contextual information norms.
Using techniques in natural language processing, machine learning, network, and data analysis, this project is set to explore the privacy implications of mobile apps, online platforms, and other systems in different social contexts/settings.
To tackle this challenge, the project will operationalize a cutting-edge privacy theory and methodologies to conduct an analysis of existing technologies and design privacy-enhancing tools.
Duties and Responsibilities: Students will help analyze information handling practices of online services and design privacy-enhancing tools. Specific tasks include: comprehensive literature review of existing methodologies and tools, analysis of privacy policies and regulations, visualization of information collection practices, and design of a web-based interface for analyzing extracted privacy statements to identify vague, misleading, or incomplete privacy statements.
Desired Technical Skills: Good programming skills overall, and experience in using Jupyter and/or R for data analysis.
Desired Course(s): Software engineering, computer science, and information science students. Note: students with diverse backgrounds, including in technical fields, social sciences and humanities are encouraged to apply.
Other Desired Qualifications: Experience with machine learning, natural language processing techniques, HCI design and web development. interest in usable privacy, critical analysis of privacy policies and privacy related regulation.
Contact Info: Yan Shvartzshnaider (rhythm.lab@yorku.ca)



Fuzz Testing for Deep Learning Models
Professor: Song Wang
Lab Website: https://www.eecs.yorku.ca/~wangsong/index.html
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 2
Project Description: In this project, we will explore new fuzzing testing techniques for finding bugs in deep learning models. Testing core deep learning models is challenging, as the effectiveness of such testing requires model-specific input constraints. Existing fuzzing tools have no knowledge of such constraints, to fill this gap, this project plans to design and implement a document-guided fuzz testing technique for deep learning models, which leverages model-specific constraints from model documents and uses these constraints to guide the generation of valid seeds for fuzzing.
Duties and Responsibilities: Help with the implementation of tools, collecting experiment data, running experiments, and paper writing if needed.
Desired Technical Skills: Python programming; knowing some basic of DL frameworks e.g., tensorflow and pytorch.
Desired Course(s): LE/EECS 2030 3.00 – Advanced Object Oriented Programming, and LE/EECS 3311 3.00 – Software Design.
Other Desired Qualifications: Be self-motivated.
Contact Info: Song Wang (wangsong@yorku.ca)



Development of a Gallium Nitride (GaN)-FET Boost-Type Photovoltaic (PV) Maximum Power Point Tracker for Microgrid Energy Network
Professor: John Lam
Lab Website: https://pelser.lab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Microgrid with high voltage DC distribution (~400Vdc) provides an attractive power architecture for applications such as small-scale renewable energy systems, commercial buildings, and data centre
s with storage due to: fewer number of power conversion stages, higher overall efficiency, and the ease of inter-connecting with back-up storage. In a DC microgrid where photovoltaic (PV) energy is one of the distributed energy resources, a power converter is needed to step-up the PV panel output voltage to match that of the DC distribution grid. The PV converter is also responsible for extracting the maximum amount of PV energy for different irradiation level. With the inherent fast switching speed of the emerging wide bandgap devices, such as Gallium Nitride (GaN) FETs, very high frequency power conversion in multi-MHz range is now feasible. This research project is to investigate and develop a GaN-FET step-up PV Maximum Power Point Tracker (MPPT) for use in the DC-distributed microgrid. The developed PV MPPT topology will be analyzed, and its performance will be verified using power electronics simulation software, such as PowerSIM or Simetrix. Preliminary hardware validation on a proof-of-concept prototype will also be performed.
Duties and Responsibilities: Using simulation software such as PowerSIM (PSIM) or Simetrix, to study the switching behaviour of the selected GaN-FET in the boost-type MPPT and to perform simulation verification, performing topology and circuit analysis, weekly meetings with the project supervisor, and performing preliminary hardware validation of the developed research idea.
Desired Technical Skills: Problem solving skills, circuit analysis, and basic programming skills in MATLAB.
Desired Course(s): An electrical engineering student who has taken LE/EECS 2200 3.00 – Electrical Circuits, and LE/EECS 3201 4.00 – Digital Logic Design and courses related to electronics, power electronics, and introduction to energy systems.
Other Desired Qualifications: Hardworking, team player, and strong written and oral communication skills.
Contact Info: John Lam (johnlam@eecs.yorku.ca)



Reliable In-Flight Connectivity with 6G Integrated Satellite-Aerial-Terrestrial Networks
Professor: Hina Tabassum
Lab Website: https://sites.google.com/a/kaust.edu.sa/hina-tabassum/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 1
Project Description: Low-cost and reliable in-flight connectivity is a long-standing wireless communication problem as the airplane is far away from both the satellites and the ground base stations during most of the flight time. Traditional transmissions at microwave frequencies allow for long link distances but the data rate is not high. This project will focus on investigating the feasibility performance of various channels on the terahertz band (THz, 0.3 THz -10 THz) and determine the feasibility of this band for aviation. Considering the non-flat Earth geometry and the main features of the frequency-selective THz channel, we will optimize the transmission frequency selection at various altitudes. The project will be executed in three steps: simulations of integrated satellite-aerial-terrestrial network, stochastic geometry analysis, and machine learning to optimize the transmission frequencies at different altitudes.
Duties and Responsibilities: Simulation, mathematical modelling, and analysis, write-up of a report/article towards the end.
Desired Technical Skills: Mathematics, and machine learning.
Desired Course(s): A student from the Electrical Engineering and Computer Science department or the Earth and Space Science and Engineering department.
Other Desired Qualifications: Some background on digital communication or communication networks, probability, and statistics, and basic knowledge of machine learning is required.
Contact Info:
Hina Tabassum (hinat@yorku.ca)



“Ellipti-linear” Representations for Estimation of the 3D Rim of an Object from its 2D Occluding Contour
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: The local shape of the occluding contour of an object is known to constrain the local shape of the object surface, however these constraints are qualitative. While strict quantitative constraints relating the occluding contour to solid shapes are unlikely, we posit here that typical regularities of common objects and rules of projection induce dependencies that can be used to derive statistical estimates of quantitative solid shapes from the occluding contour. To explore this conjecture, we partition the problem into two parts: estimation of the 3D rim from the 2D occluding contour, and estimation of the visible surface shape from the estimated 3D rim. We train and evaluate statistical models on two distinct 3D object datasets and evaluate their ability to capture statistical regularities that enable 3D estimation of the object shape.
Line and ellipses are invariant under projection, making them convenient contour representations for the estimation of the 3D rim from the occluding contour. In this project we will therefore focus specifically on “elliptilinear” representations of the occluding contour and rim, i.e. piecewise elliptical curves, with linear intervals occurring with non-vanishing probability. 
Duties and Responsibilities: The student will inherit 3D object datasets and software designed to recover optimal ellipti-linear representations of occluding contours. The student will validate the software and then analyze how these occluding contour representations relate to ellipti-linear approximations of the rim. Based on this analysis, the student will develop an algorithm to estimate the 3D shape of the rim from the 2D shape of the occluding contour. The student will have regular meetings with collaborator Yiming Qian (A*Star Singapore) as well as tri-weekly meetings with principal investigator Prof. James Elder.
At the end of the summer, the student will deliver code and labeled data in the form of a GitHub repository and an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: MATLAB, and an aptitude in mathematics and statistics.
Desired Course(s): N/A
Other Desired Qualifications: MATLAB, and an aptitude in mathematics and statistics.
Contact Info:
James Elder (jelder@yorku.ca)



Evaluating Systems for Long-Term Tracking of Hockey Players
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Tracking players in sporting footage is extremely useful for both coaching and game statistics. Tracking involves detection of a bounding box around each player in each frame and associating these boxes across frames. The problem is challenging due to occlusions and wide variations in pose. Motion blur and changing illumination complicate the task further.
Most of the current research in the area has focused on short-term tracking (30 to 60 seconds). However, real-world applications such as tracking hockey players call for tracking for longer time intervals. In this project, the student will explore various approaches to long-term tracking of hockey players.
Supervisor: Maria Koshkina, PhD Student.
Duties and Responsibilities: The student will be responsible for investigation, implementation, and quantitative evaluation of state-of-the-art approaches for long-term object tracking and their application to hockey player tracking. The student will have daily meetings with PhD student Maria Koshkina to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder. 
At the end of the summer the student will deliver code and labelled data in the form of a GitHub repository, as well as an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Python programming experience, and understanding of deep learning methodology.
Desired Course(s): N/A
Other Desired Qualifications: Understanding of deep learning methodology, and Python programming experience.
Contact Info: Anna Kajor (akajor@yorku.ca)



Video-Based Traffic Analytics at Intersections
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: The goal of the project is to research and develop computer vision algorithms, software, and specialized hardware for the analysis of mixed traffic at intersections. Multiple cameras will be employed. Road users will be detected and classified as motor vehicles, pedestrians, and bicycles. Motor vehicles will be further classified as vehicles with two wheels (motorcycles) and vehicles with 4 or more wheels (cars, trucks, buses). Road users will be geo-located within a 3D model of the intersection, tracked, and classified according to trajectory. 
The research will include the design and development of systems for traffic counting and traffic anomaly detection. A system for 3D visualization of recorded or streaming traffic data (digital intersection) will also be designed and developed.
Supervisors: Sajjad Savoji, Bardia Esmaeili.
Duties and Responsibilities: Assist in ground-truthing and evaluation of algorithms for detection, classification, tracking, and trajectory classification of motor vehicles at intersections. Tabulate and analyze results, identifying failure modes. The student will have daily meetings with Master’s student Sajjad Savoji and PhD student Bardia Esmaeili to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder.
At the end of the summer the student will deliver code and labelled data in the form of a GitHub repository, as well as an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Software – Python, MATLAB, and concepts – Familiarity with computer vision and 3D geometry skills preferred.
Desired Course(s): N/A
Other Desired Qualifications: Software – Python, MATLAB, and concepts – Familiarity with computer vision and 3D geometry skills preferred.
Contact Info: Anna Kajor (akajor@yorku.ca)



LiDAR-Free 3D Ground-Truthing of Motor Vehicles
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: This is a novel symmetry-based framework for single-view 3D ground-truthing of motor vehicles. LiDAR-based 3D ground-truthing is expensive, requires joint calibration of LiDARs and cameras, and may be inaccurate in the far field where LiDAR returns are sparse. We are developing a tool to annotate and ground truth 3D location, pose and shape of motor vehicles from a 2D image based on 3D symmetry cues and a generalized cylinder model of motor vehicles. The annotation and ground truth will be used to train a deep learning network for 3D object detection and estimation of motor vehicles.
Supervisor: Thao Tran.
Duties and Responsibilities: The student will work on statistical integration of the annotations with an online database of vehicle dimensions as well as OpenGL code to visualize estimated generalized cylinder models of the annotated vehicles. The student will have daily meetings with Master’s student Thao Tran to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder. 
At the end of the summer the student will deliver code and labelled data in the form of a GitHub repository as well as an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Software – Python, OpenGL, and concepts – Familiarity with computer vision and 3D geometry skills preferred.
Desired Course(s): N/A
Other Desired Qualifications: Software – Python, OpenGL, and concepts – Familiarity with computer vision and 3D geometry skills preferred.
Contact Info: Anna Kajor (akajor@yorku.ca)



Improved Control of a Robot Attentive Sensor
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: To be effective, a social robot must reliably detect and recognize people in all visual directions and in both near and far fields. A major challenge is the resolution/field-of-view trade-off, for which we have developed a novel attentive sensing solution. Panoramic low-resolution pre-attentive sensing is provided by an array of wide-angle cameras, while attentive sensing is achieved with a high-resolution, narrow field-of-view camera and a mirror-based gaze deflection system. Quantitative evaluation on a novel dataset shows that this attentive sensing strategy can yield good panoramic face recognition performance in the wild out to distances of ~35m.
Currently the system operates at about 0.3 fixations per second, far slower than the human fixation rate of 2-3 fixations per second. In this project we will redesign our system to come closer to human performance. This will involve lightening the payload, updating the motor, improving the control system, and updating the attentive camera to allow manual control of focus so that the camera can change focus while making a saccade.
Supervisors: Mohammad Akhavan, Helio Perroni-Filho.
Duties and Responsibilities:
The student will work closely with the supervisors to develop and test algorithms for video-based highway traffic analytics. The student will have daily meetings with graduate student Mohammad Akhavan and Senior Robotics Engineer Helio Perroni-Filho to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder.
At the end of the summer the student will demonstrate the redesigned attentive sensor system, deliver documented software in the form of a GitHub repository and deliver an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Software skills, control theory and algorithms, systems design.
Desired Course(s): N/A
Other Desired Qualifications: Software skills, control theory and algorithms, systems design.
Contact Info: Anna Kajor (akajor@yorku.ca)



Cleanbot: Mobile Robot Control for UV Disinfection
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Ultraviolet-C (UV-C) light is known to be effective for surface disinfection against pathogens such as COVID-19. Unfortunately, existing delivery methods are incomplete, leaving high-risk ‘shadow’ regions (e.g. the undersides of surfaces and doorknobs) unsterilized. This project addresses this problem with an agile, fully autonomous, AI-driven Cleanbot solution based on recent advances in UV-C LED technology. While parked and charging, the Cleanbot will use onboard cameras, computer vision and machine learning algorithms to visually monitor a room for human activity, building a map of high-risk areas. When activated, the Cleanbot will autonomously tour the room, optimally orienting multiple articulating UV-C panels to irradiate and disinfect identified high-risk surfaces, including those not reachable by existing systems. 
Supervisors: Mohammad Akhavan, Helio Perroni-Filho.
Duties and Responsibilities: The student will work closely with the supervisors to design, build, and validate sensing and control algorithms to guide the Cleanbot LED panels to sweep over surfaces, ensuring adequate radiation for sterilization. These algorithms will first be tested in simulation (Gazebo) and then verified using the Cleanbot prototype in the lab. The student will have daily meetings with Master’s student Mohammad Akhavan and Senior Robotics Engineer Helio Perroni-Filho to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder. 
At the end of the summer the student will demonstrate simulations and live demonstrations of Cleanbot control, as well as an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: ROS, control algorithms, software skills, 3D geometry.
Desired Course(s): N/A
Other Desired Qualifications: ROS, control algorithms, software skills, 3D geometry.
Contact Info: Anna Kajor (akajor@yorku.ca)



Using Semantics and Geometry to Improve the Generalization of Monocular 3D Perception Systems
Professor:
James Elder
Lab Website:
https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions:
1
Project Description:
Our pilot studies suggest that both humans and networks rely strongly on elevation in the image as a cue to depth, consistent with the dominance of ground plane surfaces. Moreover, our recent work on scene classification has suggested that human awareness of scene semantics can precede and inform awareness of 3D spatial layout. These findings motivate a novel approach to monocular depth estimation in which depth is inferred directly from semantic surface labels and the statistics of 3D spatial relationships between surfaces.
To explore this novel approach, we will again employ a dataset of planar projections generated from the SYNS dataset. State-of-the-art deep semantic and instance segmentation systems will be applied to estimate the semantic surface labels and individuate objects. In the first stage of inference, known view geometry will be used to assign depths to all ground-plane semantic categories (e.g. road, sidewalk, field). In a second stage of inference, semantic regions adjacent to ground-plane regions will be assigned depths based upon vertical propagation from assigned ground-plane depths. In a third stage, for regions not adjacent to ground-plane regions, depths will be estimated based upon adjacent regions already assigned depths and a learned table of expected relative depth for pairs of adjacent semantic categories. Our goal is to carefully refine the algorithm by also incorporating instance segmentation and more carefully encoding 3D spatial relationships between semantic categories. Results will be compared against state-of-the-art deep network systems.
Supervisors: Alek Trajcevski, Hossein Hosseini.
Duties and Responsibilities: The student will work closely with the supervisors to develop and test algorithms for monocular estimation of absolute and relative depth on held-out datasets. The student will have daily meetings with graduate students Alek Trajcevski and Hossein Hosseini to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder. 
At the end of the summer the student will deliver documented software in the form of a GitHub repository and an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills:
Software skills, 3D geometry, machine learning.
Desired Course(s): N/A
Other Desired Qualifications:
Software skills, 3D geometry, machine learning.
Contact Info: James Elder (jelder@yorku.ca)



Computer Vision Systems for Highway Traffic Analytics
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Traffic congestion is a major challenge throughout the world. It affects commute time, mobility, and accessibility and is a driving factor in increasing the harmful gasses that are the main culprits of the greenhouse effect. Congestion can potentially be mitigated over short time scales through improvements to signal timing and rapid detection and resolution of traffic incidents, and over longer time scales through strategic roadway improvements and optimization of public transit systems. However, all of these mitigations depend critically on an accurate understanding of lane-by-lane traffic density and speed distributions. Historically, these data sets are obtained from embedded inductive loops, but these are expensive to maintain. This motivates the development of fully automated AI-based video analytics systems to accurately measure traffic density and speed and to detect anomalous traffic incidents.
In the proposed research we will focus specifically on highways, which in large metropolitan areas are typically the key pinch points that determine overall congestion. One challenge to achieving our objective is the volume of video data required to densely sample these highways and to react to measurements in a timely way. Distributing this quantity of data to the cloud or a central control centre for processing entails an enormous investment in communication and storage and, despite advances in low-latency communication protocols, will inevitably lead to delays in response. It also raises privacy and data security issues. These challenges motivate an edge solution, where video is processed by embedded systems located on-site, and only low-bandwidth and anonymized derived analytics products are communicated centrally. In the proposed research, we plan to design such an embedded system, optimized for highway deployment.
The system will be designed for mounting at 10m height on either meridian or side poles, and will incorporate multiple cameras that together provide a field of view covering all lanes of traffic in both directions. These will be paired with an advanced embedded AI device such as an NVIDIA Jetson AGX Orin. Deep networks will be pruned and quantized (INT8, FP16, Mixed Precision) to meet throughput requirements. Provided functionality will include: Automatic camera calibration and highway understanding that allows events localized in the image to be precisely back projected to highway ground coordinates, detection, classification, and segmentation of motor vehicles, vehicle speed estimation, detection of anomalies, including accidents and stopped vehicles, reporting of anomalous vehicles through automatic number plate recognition.
Supervisor: Shreejal Trevedi.
Duties and Responsibilities:
The student will work closely with the supervisors to develop and test algorithms for video-based highway traffic analytics. The student will have daily meetings with graduate student Shreejal Trivedi to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder and with project partners at the Ministry of Transportation Ontario.
The planned outcome of this research project is a prototype embedded traffic analytics products optimized for highway deployment. We hope to ultimately commercialize this device through licensing or a start-up company. We believe this device will ultimately replace costly inductive loop systems and contribute to better traffic analytics, thus ultimately reducing congestion and greenhouse gas emissions.
At the end of the summer the student will deliver documented software in the form of a GitHub repository and an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Software skills, and systems hardware skills.
Desired Course(s): N/A
Other Desired Qualifications: Software skills, and systems hardware skills.
Contact Info:
Anna Kajor (akajor@yorku.ca)



Improving Configural Processing In Deep Neural Networks
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: ImageNet-trained networks fail to develop a configural representation of object shape. A main reason for this failure may be the impoverished nature of the image labelling task. While the human brain of course supports object recognition, humans form much richer representations of objects that allow us to appreciate many diverse qualities of objects and their spatial relationships to each other and the rest of the visual scene. To support this diverse experience of objects, we hypothesize that the brain establishes useful intermediate representations that can subserve multiple tasks. In this project, we explore this hypothesis computationally by modifying existing deep network architectures to expand the task from object labelling to also include object localization, segmentation, and monocular depth estimation. Critically, losses will be applied not only at the outputs of each respective head, but also at intermediate stages of computation. These intermediate losses will take the form of regional forms of their respective tasks, with spatial extent matched to the nominal receptive field size.
We hypothesize that this will lead to more holistic processing as the network takes advantage of synergies between the tasks, and local shortcuts that might be useful for recognition are disfavoured since they do not contribute to other tasks.
Supervisor: Nima Vahdat.
Duties and Responsibilities: The student will work closely with the supervisors to develop and test algorithms for video-based highway traffic analytics. The student will have daily meetings with graduate student Nima Vahdat to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder.
At the end of the summer the student will deliver documented software in the form of a GitHub repository and an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Software skills, Python, PyTorch, and deep learning.
Desired Course(s):
N/A
Other Desired Qualifications: Software skills, Python, PyTorch, and deep learning.
Contact Info:
Anna Kajor (akajor@yorku.ca)



Long-Term Operation of an Attentive Social Robot
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: We have developed an attentive social robot that is capable of attentively searching for specific individuals, recognizing them at a distance using facial recognition software, and approaching them for more information. However, these behaviours have only been demonstrated under control conditions and for short operating periods. In this project, we will develop the enhancements to software, hardware, and protocol required to allow the robot to operate continuously and reliably for long durations, collecting useful social information in a multi-room environment.
Supervisor: Helio Perroni-Filho.
Duties and Responsibilities: The student will work closely with the supervisors to develop and test algorithms for video-based highway traffic analytics. The student will have daily meetings with Senior Robotics Engineer Helio Perroni-Filho to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder.
At the end of the summer the student will demonstrate long-term operation of the robot and deliver an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Software skills, and systems design.
Desired Course(s):
N/A
Other Desired Qualifications: Software skills, and systems design.
Contact Info:
Anna Kajor (akajor@yorku.ca)



Eliminating Occlusion Artifacts in a Robot Attentive Sensor
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: We have developed an attentive social robot that is capable of attentively searching for specific individuals, recognizing them at a distance using facial recognition software, and approaching them for more information. One limitation of our attentive sensor is that the field of view is partially occluded in four directions by the aluminum posts that support the motorized mirror. Fortunately, due to focal blur the occlusion is only partial, which suggests that it could potentially be calibrated out. The goal of this project is to develop and evaluate algorithms for minimizing this artifact.
Supervisor: Helio Perroni-Filho.
Duties and Responsibilities: The student will work closely with the supervisors to develop and test algorithms for removing the occlusion artifact. The student will have daily meetings with Senior Robotics Engineer Helio Perroni-Filho to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder.
At the end of the summer the student will demonstrate the occlusion removal method as implemented in the robot and deliver an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Software skills, and systems design.
Desired Course(s): N/A
Other Desired Qualifications: Software skills, and systems design.
Contact Info:
Anna Kajor (akajor@yorku.ca)



Markov Chain Monte Carlo Method For Generating Naturalistic Shapes
Professor: James Elder
Lab Website: https://www.elderlab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: We have developed a novel Markov Chain Monte Carlo method for created 2D planar shapes with prescribed curvature statistics. These stimuli are useful for behavioural and neurophysiological experiments in human and animal models, and to evaluate computational models for object perception.
Unfortunately, our software contains some bugs; occasionally, the bounding contours of these shapes self-intersect, leading to pathological stimuli. The goal of this project is to find and correct these bugs.
Duties and Responsibilities: The student will work closely with the supervisor to debug this software and deliver a corrected version in the form of a GitHub repository. The student will have regular meetings with principal investigator Prof. James Elder.
At the end of the summer the student will demonstrate and validate the corrected software and deliver an engineering report in LaTeX that documents the objectives, methods, results, and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference.
Desired Technical Skills: Software skills, mathematical skills, and statistics.
Desired Course(s): N/A
Other Desired Qualifications: Software skills, mathematical skills, and statistics.
Contact Info:
Anna Kajor (akajor@yorku.ca)



Deep Learning Solutions for Compressed Wi-Fi Sensing
Professor: Hina Tabassum
Lab Website: https://sites.google.com/a/kaust.edu.sa/hina-tabassum/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 1
Project Description: Since Wi-Fi sensing is cost-effective and privacy preserving, it is becoming a critical technique for situational awareness and human activity recognition. Wi-Fi sensing relies on the extraction of Channel State Information (CSI) at each transmission subchannel. However, CSI measurement has high dimension and sampling rates which makes real-time CSI streaming challenging. This project will focus on developing an end-to-end deep learning framework that yields a compressed and discriminative feature space from fine-grained CSI with extremely low error of data reconstruction and higher prediction accuracy. The student will be able to learn the process of CSI extraction with the available equipment in the lab.
Duties and Responsibilities: Participate in hands-on CSI data collection, prepare a report on the procedure, and contribute to the development of the deep-learning architecture to efficiently compress and reconstruct CSI.
Desired Technical Skills: Machine Learning.
Desired Course(s): Machine learning courses.
Other Desired Qualifications: Machine learning courses, mathematics and statistics.
Contact Info:
Hina Tabassum (hinat@yorku.ca)



Online Algorithms with Prediction
Professor: Shahin Kamali
Lab Website: https://www.eecs.yorku.ca/~kamalis/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: A problem is “online” if its input appears sequentially, and decisions about each revealed part of the sequence must be taken instantly and irrevocably. For instance, in online trading problems, you need to trade an asset when the online input sequence is the set of prices that are revealed every day, and you must make the irrevocable decision of trading or not-trading each day. Recently, online algorithms with predictions have received considerable attention. Under this model, online algorithms receive possibly erroneous predictions about the input sequence (e.g. using machine learning predictors). The goal is to design algorithms with a good consistency (the performance when the predictions are correct) and good robustness (the performance when the predictions are erroneous). An algorithm that blindly trusts the prediction has good consistency but not necessarily good robustness, and an algorithm that ignores prediction is likely to have good robustness but not necessarily good consistency.
In this project, we aim to design online algorithms that offer a good (perhaps optimal) trade-off between consistency and robustness. The problems that we consider include a variety of topics that range from graph problems, scheduling problems, packing problems, and computational geometry problems. Depending on the students’ backgrounds and interests, they will focus on one or two online problems. Students will have a chance to learn about online algorithms, the classic framework of competitive analysis, Pareto-optimality, and basic techniques in the design of online algorithms.
Duties and Responsibilities: Do a literature review (reading papers) and present a review of the existing work, inspect ways to improve existing algorithms to design and analyze new (hopefully improved) algorithms, implement the studied algorithms and perform an experimental analysis of the studied algorithms, and write a final report with a summary of the findings in the form of a research paper.
Desired Technical Skills: Strong background in theoretical computer science, and sufficient coding skills for conducting experimental analysis of algorithms.
Desired Course(s): Computer science student, preference will be given to students who have completed LE/EECS 3101 3.00 – Design and Analysis of Algorithms with an A+ or A grade.
Other Desired Qualifications: N/A
Contact Info:
Shahin Kamali (kamalis@yorku.ca)



Fairness in Algorithm Design
Professor: Shahin Kamali
Lab Website: https://www.eecs.yorku.ca/~kamalis/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: In recent years, designing ” fair ” algorithms has received considerable attention in theoretical computer science. The objective is to design algorithms that have good performance and are fair at the same time. Consider the classic “secretary problem,” where the input is a randomly permuted set of applicants, each having a qualification score, who have applied for k secretary jobs. Applicants are interviewed one by one, and an immediate decision must be made about hiring or rejecting each applicant, depending on their qualification score detected during the interview. The classic optimal algorithms for the problem, interviews the first r applicants (for a value of r to be calculated), reject all these applicants and record the best score M among them, and hire the applicants with a score better than M among the remaining applicants. This algorithm is unfair because an applicant’s chance of getting the job depends on the interview day (the first r applicants have 0% chance of being hired). Moreover, if applicants come from two or more groups (e.g., male and female), it is fair to hire a roughly equal number of applicants from each group; this fairness requirement is not present in the existing algorithms.
In this project, we aim to design fair algorithms with good performance. The problems that we consider include a variety of topics that range from graph problems, scheduling problems, packing problems, and computational geometry problems. Students will focus on one or two online problems depending on their backgrounds and interests.
Duties and Responsibilities: Do a literature review (reading papers) and present a review of the existing work, inspect ways to improve existing algorithms to design and analyze new (hopefully improved) algorithms, implement the studied algorithms and perform an experimental analysis of the studied algorithms, and write a final report with a summary of the findings in the form of a research paper.
Desired Technical Skills: Strong background in theoretical computer science, and sufficient coding skills for conducting experimental analysis of algorithms.
Desired Course(s): Computer science student, preference will be given to students who have completed LE/EECS 3101 3.00 – Design and Analysis of Algorithms with an A+ or A grade.
Other Desired Qualifications: N/A
Contact Info:
Shahin Kamali (kamalis@yorku.ca)



Graph Burning: A Model for Information Distribution in Social Networks
Professor: Shahin Kamali
Lab Website: https://www.eecs.yorku.ca/~kamalis/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: The goal of the graph burning problem is to burn a graph as quickly as possible. Here, graphs model social networks, and burning models; how fast gossip, fake news, or memes disseminates in the network. The burning occurs in rounds; at each round, a fire starts at an unburned vertex, while all existing fires extend to their unburned neighbours. Burning completes when all vertices are burned. The goal of an algorithm is to choose the initial location of the fire for each round in a way to minimize the number of rounds it takes to burn the graph. The problem is NP-hard, even for simple graph families like trees of path forests. The best existing algorithm has an approximation ratio of 4.
Our goal in this project is to study the burning problem for various graph families, such as planar graphs and graphs of bounded bandwidth. In particular, we want to achieve an approximation factor better than 4 for these graphs.
Duties and Responsibilities: Do a literature review (reading papers) and present a review of the existing work, inspect ways to improve existing algorithms to design and analyze new (hopefully improved) algorithms, implement the studied algorithms and perform an experimental analysis of the studied algorithms, and write a final report with a summary of the findings in the form of a research paper.
Desired Technical Skills: Strong background in theoretical computer science, and sufficient coding skills for conducting experimental analysis of algorithms.
Desired Course(s): Computer science student, preference will be given to students who have completed LE/EECS 3101 3.00 – Design and Analysis of Algorithms with an A+ or A grade.
Other Desired Qualifications: N/A
Contact Info:
Shahin Kamali (kamalis@yorku.ca)



Compact Graph Data Structures
Professor: Shahin Kamali
Lab Website: https://www.eecs.yorku.ca/~kamalis/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Suppose you have a graph and want to store it in a data structure. Your goal is to store the graph compactly and answer basic queries about neighbours and the degree of vertices.
You can store it in an adjacency list, but it wastes space, and you cannot efficiently report neighbours of a vertex, one by one, in constant-time-per neighbour (this is called neighbourhood query). If you use an adjacency list, you cannot determine whether two vertices are connected in constant time (this is called an adjacency query). In both cases of the adjacency matrix and adjacency list, you cannot determine the degree of a vertex (degree query) in constant time.
In this project, we aim to design compact data structures for various graph families that allow answering adjacency, neighbourhood, and degree queries in constant time. For example, data structures exist for the family of trees (loopless graphs) that take O(n) space and answer all queries in constant time. We want to extend this type of result to other graph families. Examples include: outerplanar graphs, polyominoes, coin graphs, and graphs of specific width parameters.
Duties and Responsibilities: Do a literature review (reading papers) and present a review of the existing work, inspect ways to improve existing data structures to design and analyze new (hopefully improved) ones, and write a final report with a summary of the findings in the form of a research paper.
Desired Technical Skills: Strong background in theoretical computer science.
Desired Course(s): Computer science student, preference will be given to students who have completed LE/EECS 3101 3.00 – Design and Analysis of Algorithms with an A+ or A grade.
Other Desired Qualifications: N/A
Contact Info:
Shahin Kamali (kamalis@yorku.ca)



Learning Analytics Application (LAApp)
Professor: Pooja Vashisth
Lab Website: https://lassonde.yorku.ca/users/pvashisth
Position Type:
Lassonde Undergraduate Research Award (LURA); Women in Engineering Co-op Stream
Open Positions: 3
Project Description: Problems and Needs: Moodle – York University Learning Management System (LMS) – provides instructors with rich data sets for students’ activities and performance. However, while the data comes in bulk, some important information (e.g. course activities) is automatically deleted by the system and replaced with new data each week. This issue prevents instructors from using such data effectively to enhance their teaching. Moreover, the bulk data may prevent professors from taking away useful insights to improve course quality.
Ideas: This project aims to address the main problems below with the following solutions: Retrieve full data set using scripts/integrations/apps that automatically pull course data from Moodle. Provide instructors with useful data visualizations from Moodle’s enormous data to support their teaching. Provide a quick summary and insights from those data sets.
Implementation:
Pull data – quiz statistics, proposal, weekly course activity, new analytics → report, course activity, course grades.
Data visualization: visualize general data, data of assessments (quizzes, assignments,…), summary for all assessments and summary for each assessment and for each question, mean, mode, median, average time overall and for each assessment, average number of attempts, grade distribution, time distribution (when and how long students spend on) → clusters, respondents distribution (based on best attempt and compared to class) for each question, correlation between the number of attempts and accuracy, highlight hard questions and corresponding materials/reading views and engagement, extract question labels/keywords, what are the types of questions (MCQ, pseudocode,…) that students are generally not performing well on, data of exam, mean, mode, median, grade distribution, data of engagement, time distribution (when and how long students spend on) per course material, most and least viewed/engaged materials, average views and engagement per material, is there any correlation between students’ engagement and performance (i.e. do students actually achieve higher scores if they engage with course materials more frequently), visualize individual data, current avg. mark, all marks of that student up to that moment, performance as a graph (quizzes, assignments, test 1, test 2, final), attempts and accuracy per quiz, time spent on each assessment, time a student started an assessment and the last submission timestamp
Proposal 3, questions/topics that each student is doing well/struggling with engagement: what content does this student engage with? how often? when? how long? times opened a document? correlation between engagement and performance? feedback and insights, individual feedback – assess whether a student is at risk or not, if a student does well on assignments, how do they perform on midterms and finals? how about the opposite case? [tentative] predict examination results? interventions, when and how instructors should deploy their interventions to students, based on the results provided? [tentative] option to email/inform those low-performing students?
Deliverables: website to show statistical visualizations (high priority), sign in with Moodle, all students’ data graphs individuals’ data graphs feedback by words | insights for each student, automation on data collection from Moodle (medium priority) instructors will be able to share those results with students (optional – low priority).
Duties and Responsibilities: Deliverables: website to show statistical visualizations (high priority), sign in with Moodle, all students’ data graphs individuals’ data graphs feedback by words | insights for each student, automation on data collection from Moodle (medium priority) instructors will be able to share those results with students (optional – low priority).
Desired Technical Skills: Research and statistics skills; web development (applying OOP design, design pattern, and SOLID principles in developing backend API (using Python Flask)); data analysis: using Python framework and library (pandas, NumPy, seaborn, matplotlib, statsmodel) in analyzing the data; full-stack development: front-end: building user interfaces with JavaScript technologies; back-end: implementing RESTful API and GraphQL API using JavaScript technologies; DBMS: database design and using NoSQL/SQL databases in web development.
Desired Course(s):
The position is open for third and fourth year students in a computer science, statistics, data science, or engineering degree.
Other Desired Qualifications: Good at programming, statistics, research, and has a willingness to explore the unknown.
Contact Info:
Pooja Vashisth (vashistp@yorku.ca)



Development and Testing of an Energy Storage Power Interface with a Battery Simulator
Professor: John Lam
Lab Website:
https://pelser.lab.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Canada is committed to reduce its greenhouse gas (GHG) emissions by 40-45% from 2005 levels by 2030. To meet this aggressive target, it is important to prioritize the development of clean and affordable renewable power generation, such as wind and photovoltaic (PV) energy. While effectively extracting energy is imperative in renewable energy systems, energy storage mechanisms are equally important as they are responsible for managing the power flow within the system to reliably deliver the demanded energy to the load. This research project is to develop an energy storage (such as a battery) power interface for use in microgrids with  high voltage DC distribution. The characteristics and modelling of the battery and the power interface will be investigated in PowerSIM simulation. Hardware experimental validation on the power interface prototype with a battery simulator will also be performed.
Duties and Responsibilities: Using simulation software such as PowerSIM (PSIM) or Simetrix, to study the characteristics of the energy storage power interface and battery model. The student will also engage in weekly meetings with the project supervisor, and performing preliminary hardware validation of the developed research idea.
Desired Technical Skills: Problem solving skills, circuit analysis, and basic programming skills in MATLAB.
Desired Course(s): Electrical engineering students, desired course requirements: LE/EECS 2200 3.00 – Electrical Circuits, LE/EECS 3201 4.00 – Digital Logic Design, and courses related to electronics, and power electronics.
Other Desired Qualifications: Hardworking, willing to work on a team, and strong written and oral communication skills.
Contact Info: John Lam (johnlam@eecs.yorku.ca)



Machine Learning for Graphene Fabrication
Professor: Gerd Grau
Lab Website: https://www.eecs.yorku.ca/~grau/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: Laser-induced graphene (LIG) is an emerging technology to fabricate microelectronic devices. A polymer substrate is exposed to a scanned laser beam that directly converts the polymer into graphene. The use of LIG offers several advantages over traditional fabrication technology. LIG is a low-cost method to create patterned graphene on polymer substrates for flexible electronics such as sensors or energy storage devices. Whilst this new technology shows great promise, there are still a number of challenges making LIG a very active area of research. Due to the complexity of the laser process that creates LIG, it is difficult to predict the properties of the resulting LIG under different conditions. A potential approach to address this problem is to use machine learning. Building on our previous research, the student will generate experimental data and train a machine learning model to predict the properties of LIG.
Duties and Responsibilities: Train machine learning model, plan, carry out and analyze experiments, learn the required techniques and skills under supervision, follow safe lab procedures, read relevant literature, present research results in regular meetings, write up results to be submitted for publication or to be part of a future publication.
Desired Technical Skills: The student needs to have programming experience and ideally some background in machine learning. Experimental lab skills would be beneficial but can be learned during the summer.
Desired Course(s): Engineering, computer science or physical sciences students.
Other Desired Qualifications: The student needs to be able to work independently as well as effectively in a team of researchers. They need to be enthusiastic to learn new knowledge and apply it to challenging research questions.
Contact Info:
Gerd Grau (grau@yorku.ca)



AI-Enabled Distributed Cloud Systems
Professor: Hamzeh Khazaei
Lab Website: https://pacs.eecs.yorku.ca
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Large-scale distributed systems are becoming more challenging to design, implement, operate, and maintain. In particular, manual run-time management of distributed cloud systems is no longer an option. As a result, we need to leverage AI/ML techniques to help operate these large-scale systems aiming for the highest availability and reliability. This will be even more important when we use these systems in critical sectors such as health. In the PACS lab, we are working on designing, implementing, and prototyping AI/ML techniques to make distributed systems smarter so that they can take care of themselves to a high degree. The two prospective undergraduate students will help develop and prototype such intelligent distributed cloud systems during the summer.
Duties and Responsibilities: Brief background reading will be required, followed by work on the development of new AI techniques to provide smarter cloud-distributed systems.
Desired Technical Skills: Familiarity with cloud technologies (e.g. Docker, Microservices and FaaS), basic knowledge in ML and AI and programming in Python or Go languages.
Desired Course(s): Computer science or software engineering students who have received good grades in LE/EECS 2011 3.00 – Fundamentals of Data Structures and/or LE/EECS 3221 3.00 – Operating System Fundamentals.
Other Desired Qualifications: Interested in large-scale distributed systems, cloud computing and ML/AI.
Contact Info:
Hamzeh Khazaei (hkh@yorku.ca)



Software Development for a Wearable Brain EEG Monitoring Device
Professor: Hossein Kassiri
Lab Website: https://electronics.eecs.yorku.ca/
Position Type:
NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Neurological disorders affect up to 1 billion people worldwide. Currently, three main types of treatment options are available for these disorders, which are pharmacological, surgery, and in some cases neurostimulation. To customize the treatment plan for all these cases, there is a need for long-term monitoring of the patient’s neuronal activities. EEG monitoring is known as the best non-invasive method that provides a high spatial and temporal resolution as well as high spatial coverage of the brain’s neural activities.
The standard way of conducting EEG recording, however, requires a trained technician to conduct the experiment which involves patient preparation, electrodes placement, equipment setup, data collection, and interpretation.
Motivated by this, several wireless wearable EEG recording headsets have been developed over the past few years, aiming to provide a fast, low-cost, and medically relevant alternative to the existing technology (e.g. EEG headsets from Cognionics, Emotiv, and Muse), thus achieving long-term ambulatory EEG recording. The main challenge in designing EEG recording wearable devices is to have a lightweight wearable EEG recording device that hosts a high number of channels while having a long-lasting battery charge. Current devices are only capable of meeting some of the above-mentioned criteria at the same time.
In the integrated Circuits and Systems Lab, we have developed a wearable device that will be used as a low-cost long-term brain monitoring solution which is capable of integrating a high number of recording channels. The device will host a proprietary algorithm for the early detection of epilepsy seizures. It will be used to monitor the real-time brain activity of the patients.
Successful candidates will work closely with a PhD student (who has built the wearable device) to develop and test software that reads the incoming data from the computer’s Bluetooth port, decodes and displays data for the user, and allows the user (ideally, through GUI) to manipulate the data (i.e. run some basic math functions on them).
Duties and Responsibilities: Development and testing of the described software, and development of the GUI for the software user.
Desired Technical Skills: Experienced with C# or Java (or other languages that could be used for the described project)
Desired Course(s): LE/EECS 3451 4.00 – Signals and Systems or similar course content. Computer science or electrical engineering students are preferred.
Other Desired Qualifications: Self-driven and interested in neuro-technology.
Contact Info:
Hossein Kassiri (hossein@eecs.yorku.ca)



Development and Characterization of Inductive Wireless Power Transfer Links for Brain-Implantable Devices
Professor: Hossein Kassiri
Lab Website: https://electronics.eecs.yorku.ca/
Position Type:
NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: The project involves modelling, design, development, and optimization of multiple inductive wireless power transfer links for miniaturized brain-implantable devices. The modelling and design will be performed using ANSYS HFSS. The project involves building circuits and systems for both transmitter and receiver coils and experimental testing of the links’ power transfer efficiency.
Duties and Responsibilities: Designing and optimizing of the Tx-Rx coils, developing prototypes for the full inductive link, and conducting measurement results and reiterating the design process for PTE optimization.
Desired Technical Skills: Familiar with EM theory, familiar with ANSYS HFSS, solid background in electrical and electronic circuits, familiar with basic electronic test equipment (oscilloscopes, signal generators, etc.), and familiarity with VNAs and Spectrum Analyzers is an asset.
Desired Course(s): LE/EECS 2200 3.00 – Electrical Circuits, LE/EECS 2210 3.00 – Electronic Circuits and Devices, and LE/EECS 3604 4.00 – Electromagnetic Theory and Wave Propagation.
Other Desired Qualifications: Interested in conducting experimental measurements using various electronic test equipment.
Contact Info:
Hossein Kassiri (hossein@eecs.yorku.ca)



Miniaturized PCB Development for a Wearable Device
Professor: Hossein Kassiri
Lab Website: https://electronics.eecs.yorku.ca/
Position Type:
NSERC Undergraduate Student Research Award (USRA)
Open Positions: 1
Project Description: The student will work closely with a PhD student to design, develop, program, and test a miniaturized (roughly 2×2 sq-cm) printed circuit board (PCB) that hosts a microchip (designed by the PhD student) used for recording brain signals from the scalp. In addition to the microchip, the mini-PCB will host an FPGA and several peripheral passive and active components (resistors, capacitors, regulators, crystal oscillators, etc.). Once developed, the student will work with the grad student to test the prototype for human EEG and ECG recording.
Duties and Responsibilities: Designing the PCB circuit together with the PhD student, developing the physical layout of the PCB and submit the design for fabrication, developing firmware for the on-PCB FPGA and microcontroller, and testing the fabricated PCB together with the PhD student.
Desired Technical Skills: Solid understanding of electrical and electronic circuits, the ability to write Verilog or VHDL codes for FPGAs, experience with developing simple codes for microcontrollers.
Desired Course(s): Student must have taken LE/EECS 2210 3.00 – Electronic Circuits and Devices. Having taken LE/EECS 3611 4.00 – Analog Electronics is not required but is considered an asset.
Other Desired Qualifications: Interest in biomedical electronic projects.
Contact Info:
Hossein Kassiri (hossein@eecs.yorku.ca)
Design and Assembly of a Low-Cost Cleanroom for Air-Bearing Microgravity Testbeds
Professor: George Zhu
Lab Website: www.yorku.ca/gzhu
Position Type: NSERC Undergraduate Student Research Award (USRA)
Open Positions:
3
Project Description: Air-bearings allow almost frictionless motion on the table surface to simulate microgravity conditions in two dimensions. The dust on the table surface will deteriorate the frictionless condition. The project is to design and assemble a tent-type cleanroom using PVC tubes and plastic sheets around the air-bearing testbed to achieve ISO 8 or better standards.
Duties and Responsibilities: The students will design the cleanroom frame in Solidworks, select proper air blower and filtration systems, design an air duct system to distribute air into the cleanroom, make a bill of materials, purchase the materials and assemble them into a working condition.
Desired Technical Skills: Solidworks design and drawings, structural design, electrical, and hands on work.
Desired Course(s): Engineering students.
Other Desired Qualifications: Hands-on experiences are desired.
Contact Info:
George Zhu (gzhu@yorku.ca)



Experimental Validation of Spacecraft Formation Fly Control Algorithms Using Air-Bearing Microgravity Testbeds
Professor: George Zhu
Lab Website: www.yorku.ca/gzhu
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Ground testing, such as air-bearing-based spacecraft simulators, is widely used to develop and validate the design and control techniques of spacecraft formation flying. This project will test attitude control algorithms in spacecraft formation flying using the planar air-bearing microgravity simulators in the Petrie Building for autonomous spacecraft tracking and rendezvous. The test system includes a pseudo-galactic star tracking system for the pose determination and navigation of spacecraft simulators. A second optical tracking module was also developed to determine the pose of a target spacecraft relative to other spacecrafts. Path planning and PD trajectory tracking algorithms were developed for spacecraft tracking and rendezvous.
Duties and Responsibilities: The students will write B-dot control algorithms, upgrade reaction wheels, design and fabricate a magnetorquer, and implement attitude control and position control algorithms in two spacecraft simulators. They will also validate the algorithm.
Desired Technical Skills: Simulation, Matlab/Labview programming, electronic and mechanical hardware, and experimental experience.
Desired Course(s): Engineering students.
Other Desired Qualifications: Self-motivated and can work with minimal supervision.
Contact Info:
George Zhu (gzhu@yorku.ca)



Additive Manufacturing in Space with Robotics
Professor: George Zhu
Lab Website: www.yorku.ca/gzhu
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: The current AM technology is developed for Earth, where the gravity and the atmosphere allow for constant environment parameters. In space these conditions are non-existent resulting in the microgravity conditions altering the fused material deposition process and interfacial bonding formation. The lack of external pressure in space will also change the fused material shape in the deposition process compared with parts made on Earth. Additionally, the vacuum will also cause heating and cooling management issues as heat can only be dissipated by radiation in a vacuum.
This project will modify and program a robotic arm in the lab to 3D-print parts in space. This project will allow the group to investigate how zero gravity affects the integrity and bonding of the layers of a 3D-part.
Duties and Responsibilities: Operation of the 6DOF robotic arm with G-code programming using a PC. Mount and control the 3D printer head to the end effector of the robotic arm. Control the 3D printing head by a standalone microcontroller. Control the robotic motion and microcontroller by PC.
Desired Technical Skills: Robotic control with G-code programming, C coding, knowledge of mechatronics and electrical systems pertaining to robotics.
Desired Course(s): Engineering students.
Other Desired Qualifications: Self-motivated and can work under minimal supervision.
Contact Info:
George Zhu (gzhu@yorku.ca)



Microfluidic Sensors for Disease Diagnostics and Environmental Monitoring
Professor: Pouya Rezai
Lab Website: https://acute.apps01.yorku.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 2
Project Description: We develop miniaturized fluidic devices to test multi-phase fluids and detect analytes of interest in them. Examples include on-site detection of biological, chemical, and physical contaminants, pollutants, and pathogens in air, water, food, and human body fluid (Point of Need Detection). These technologies are useful in environmental, health, and food safety monitoring.
We have projects available for two outstanding undergraduates aligned with the research directions above. We are looking for students interested in designing microfluidic devices while being able to learn and use biological materials in them for testing. We are looking for hard-working individuals with high academic standing who are interested in gaining exceptional opportunities to do research and publish scientific papers. Details of projects can be discussed in arranged interviews with Dr. Rezai. To start the process, send your CV and transcripts to prezai@yorku.ca.
Duties and Responsibilities: Students must work daily with senior graduate student mentors in Dr. Rezai’s lab (in the Bergeron Centre of Engineering Excellence). They should also meet with Dr. Rezai weekly and report on progress and future plans. Students will learn how to design and fabricate microfluidic devices using photolithography and 3D printing. They will also learn to test these devices with various analytical tools like microscopes and electric source-meters, while most probably using biological materials like safe bacteria, bacteriophages, and inactivated viruses in their devices.
Knowledge of fluid mechanics and materials is an asset and knowing basic biology is also considered as an applicable skill. Applicants should be good at working in teams and willing to put extra effort into research and innovation, during the summer and also stay after throughout the year to continue their research. For example, past LURA and USRA students in our lab have continued their work for years with Dr. Rezai and his team, published conference and journal papers, and joined Harvard and Cornell for graduate studies.
Desired Technical Skills: Knowledge: One or multiple of these areas: Fluid mechanics knowledge or interest to learn: Biology, Chemistry, Electronics, and/or Electrochemistry.
Desired Course(s): Mechanical, chemical, biological, biophysics, and electrical students.
Other Desired Qualifications: Punctuality, high interest to publishing, hard working, critical thinking, data collection, meticulousness, and report writing skills.
Contact Info:
Pouya Rezai (prezai@yorku.ca)



SARIT Use Cases
Professor: Andrew Maxwell
Lab Website: https://lassonde.yorku.ca/users/andrew-maxwell
Position Type:
Lassonde Undergraduate Research Award (LURA)
Open Positions: 2
Project Description: This project will take the SARIT vehicles and find new ways to use them. These applications could be mail delivery, food delivery, etc. We would like to diversify the vehicles as much as possible.
Duties and Responsibilities: The students will have to come up with different ways to use the vehicle. They will then modify the vehicle with secondary parts to achieve the designed use cases.
Desired Technical Skills: The students should be familiar with the use of tools (Drills, Screwdrivers, etc.) It would be a great asset to have knowledge in either coding (Ex. python) or design softwares (ex. SolidWorks).
Desired Course(s): Mechanical engineering students are preferred and mechanical engineering course experiences are an asset.
Other Desired Qualifications: The students should be creative and problem solvers. They need to have the skills to work independently and in groups as they will work under the graduate students who will act as project managers that oversees the project and have the capabilities to help them.
Contact Info:
Victoria Horvath (vik23@yorku.ca)



Development of an Autonomous Mobile 3D Bioprinting System for Regenerative Medicine
Professor: Alex Czekanski
Lab Website: http://www.idea-lab.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: The main responsibility includes supporting the development of the robotic bio printer at the IDEA-LAB at York University. The robotic arm is equipped with a filament extrusion and a visual tracking module. Everyday tasks will include: assisting in upgrading the current hardware and electrical design of the printing system, developing electrical enclosures and 3D printed modules for the robotic arm, and developing strategies to validate the accuracy of the robotic visual tracking system and printing performance. This project has various needs and can be tailored to fit expertise of the selected candidates. Labview and 3D printing experience will come in handy.
Duties and Responsibilities: Supporting the development of the robotic bio printer at the IDEA-LAB at York University.
Desired Technical Skills: Mechatronics, and material characterization.
Desired Course(s): Courses related to mechanical, mechatronics, and solid mechanics.
Other Desired Qualifications: Skills related to mechanical, mechatronics, and solid mechanics.
Contact Info:. Alex Czekanski (alex.czekanski@lassonde.yorku.ca)



Development of a 4D Printed Hydrogel for Tissue Engineering
Professor: Alex Czekanski
Lab Website: www.idea-lab.ca
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: This research aims to assist the development of artificial blood vessels by producing a thermo-responsible 4D printed bio-hydrogel. The most recent advances we made involve the development of a printer volume reducer device to avoid material waste, and the definition of printing parameters such as printing temperature, exposure time, and ultraviolet light intensity. This research will adapt an additive manufacturing process and define parameters to achieve the desired hydrogel properties as well as designing a 4D geometry that responds in a manipulatable way to a given thermal stimulus.
Duties and Responsibilities: Assist in additive manufacturing process and define parameters to achieve the desired hydrogel proprieties, and design a 4D geometry that responds in a manipulatable way to a given thermal stimulus.
Desired Technical Skills: Material science and engineering, and advanced manufacturing.
Desired Course(s): Mechanical engineering courses and LE/MECH 3502 3.00 – Solid Mechanics and Materials Laboratory.
Other Desired Qualifications:
Material science and engineering, and advanced manufacturing.
Contact Info: Alex Czekanski (alex.czekanski@lassonde,yorku.ca)



SHAD
Professor: Andrew Maxwell
Lab Website: https://lassonde.yorku.ca/users/andrew-maxwell
Position Type:
Lassonde Undergraduate Research Award (LURA)
Open Positions: 2
Project Description: The project will look to take existing content from previous learning experiences and modify them for the upcoming SHAD event in the summer. This project will also require to be at some of the events for SHAD.
Duties and Responsibilities:
The duties for the students will be providing content, developing material for extensive interaction, merging the UNHack and Capstone material into the summer program. All of this is done by leveraging our existing activities and experiential learning experiences.
Desired Technical Skills: No technical skills are required.
Desired Course(s): It would be an asset if the students have taken LE/CIVL/ENG/ESSE 4000 6.00 – Engineering Project (Capstone)
Other Desired Qualifications: It would be an asset if the students have participated in either UNHack or the Start-up Experience.
Contact Info:
Victoria Horvath (vik23@yorku.ca)



Fabrication and Characterization of Photonic Crystal Optical Filters for Thermophotovoltaics
Professor: Paul O’Brien
Lab Website: https://am-set-lab.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 1
Project Description: Thermophotovoltaics (TPV) is a versatile technology that can convert radiant heat into electric power. TPV has many applications including portable generators, uninterruptable power supplies, self-powered heating devices and power generation in space. Recently, the important role of TPV in transitioning to clean energy has been recognized, as research has increasingly been directed toward the application of TPV systems for industrial waste heat recovery, solar thermophotovoltaics, and thermal energy grid storage.
The main components in a TPV system are the photovoltaic (PV) cell and the emitter. Radiation from the emitter is incident onto a PV cell with a relatively low band-gap such as InGaAs, Ge, or GaSb PV cells. Incident photons that have an energy that is greater than the band-gap of the PV cell (which are referred to as in-band photons) can be converted to electric power. Incident photons with energy less than the band-gap of the PV cell (referred to as out-of-band photons) do not contribute to the electric power output from the system. These photons can be reflected back to the emitter to increase the efficiency of the system.
The objective of this project is to fabricate a one-dimensional photonic crystal optical filter. This optical filter will be placed between the emitter and PV cell in a TPV system. The filter will transmit in-band photons to the PV cell while reflecting out-of-band photons back to the emitter. Out-of-band photons reflected back to the emitter are “recycled” in the sense that they are absorbed by the emitter and their energy is converted to heat which raises the emitter temperature. The efficiency of TPV systems generally increases within increasing emitter temperature. The composition of the filter and methods that will be used to fabricate and characterize the one-dimensional optical filter are provided in the section below.
Duties and Responsibilities: One dimensional photonic crystal filters will be made of alternating layers of sputtered oxide and spin-coated nanoparticle films. The student will be responsible for cleaning substrates, preparing solutions to deposit nanoparticle films (the nanoparticles solutions will be purchased, but must be diluted and prepared appropriately), spin-coating nanoparticle films, fabricating sputtered oxide films, and characterizing the photonic crystal filters.
Spin-coating will be conducted in the Advanced Materials for Sustainable Energy Technologies Laboratory (AM-SET-Lab) which is located on the fourth floor of the Bergeron Centre for Engineering Excellence. Oxide films will be fabricated using the sputter deposition system in the York University Microfabrication Facility (YMF) located in RM037, Bergeron Centre. Training will be provided for sputtering thin oxide films and spin-coating nanoparticle films.
Optical characterisation of the films will be carried out by measuring their reflectance and transmittance using a UV-Vis spectrophotometer (Shimadzu 2600i) and an FTIR spectrometer (VERTEX 70). The thicknesses of the sputtered oxide and spin-coated nanoparticle films will be estimate using cross-sectional Scanning Electron Microscopy (SEM) imaging techniques. The SEM at the Advanced Light and Electron Microscopy Facility at York University will be used for this purpose. Training will be provided for using the UV-Vis, FTIR, and SEM
Desired Technical Skills: Experience working in a lab, written and oral communication skills, and critical thinking skills.
Desired Course(s):
Mechanical engineering, electrical engineering, physics, or chemistry student or a studies in a related field.
Other Desired Qualifications: The student should be industrious and, given proper training, and is capable of working independently.
Contact Info:
Paul O’Brien (paul.obrien@lassonde.yorku.ca)



Light and Heat Management in Greenhouses Using Luminescent Solar Concentrators
Professor: Paul O’Brien
Lab Website: https://am-set-lab.ca/
Position Type:
Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); Women in Engineering Co-op Stream
Open Positions: 1
Project Description: Exports from the Canadian greenhouse sector amounted to ~$1.4 Billion in 2020 and 71% of production in Canadian greenhouses occurs in Ontario. The greenhouse sector in Ontario is experiencing rapid growth. From 2016 to 2020 the harvested area of greenhouse vegetables grew by almost 20% and the amount of greenhouse vegetables produced increased from 4.2×105 to 4.8×105 metric tons in Ontario. Research in the greenhouse sector is also advancing and includes a diverse range of topics such as sensors, artificial intelligence, robotics, and advanced optical materials for controlling sunlight and overheating in greenhouses. Growth and technological advancements in the Canadian greenhouse sector have also led to increased demands for electric power for lighting and natural gas for heating. Presently, these increasing demands are limiting the rate of growth of the greenhouse sector in Ontario.
A promising technology for energy efficient lighting in greenhouses is luminescent solar concentrators (LSCs). Crops use only a small portion of the green light available in sunlight for photosynthesis. Crops prefer red light, which efficiently drives photosynthesis. In greenhouse applications LSCs absorb green light from sunlight and emit red light. Furthermore, it has recently been shown in the literature that the concentrated light energy at the edges of LSCs can be used to power photovoltaic cells, or to provide heat. The objective of this research project is to build and test a lab-scale prototype of a greenhouse façade comprising LSCs that is capable of harnessing sunlight and using this energy to reduce energy consumption in greenhouses. This will be accomplished by converting sunlight energy into light that has a spectrum that is more favourable for crop production and by managing the heat generated when sunlight is absorbed by the façade. The façade should be capable of transferring this heat to the external environment outside the greenhouse if the temperature of the greenhouse is high. The façade should also be able to store heat if needed in the greenhouse at a later time, such as when sunlight is not available.
Duties and Responsibilities: The student will be responsible for designing, fabricating, and characterizing a small-scale prototype (with a size on the order of about 0.5 m2) of a greenhouse façade equipped with luminescent solar concentrators. The student will perform tests in the Advanced Materials for Sustainable Energy Technologies Laboratory (AM-SET-Lab) which is located on the fourth floor of the Bergeron Centre for Engineering Excellence. Tests will involve subjecting the façade to solar-simulated light and measuring the light spectra emitted by the façade.
In one design of the façade the LSC will be immersed in water. The temperature profile of the water will be measured as a function of the intensity of the incident solar-simulated light and the duration for which the light is on. In an alternate design the façade will be equipped with phase change materials for latent heat storage. The amount of thermal energy stored within the façade using different mediums (such as sensible heat storage in water and latent heat storage in phase change materials) will be quantified using the measurements collected from the experiments.
Students will be responsible for fabricating the prototype, performing the experiments, plotting and analyzing the resulting data, and preparing a final presentation and report.
Desired Technical Skills: Experience working in a lab, written and oral communication skills, and critical thinking skills.
Desired Course(s): Mechanical engineering, electrical engineering, physics, or chemistry student or studies in a related field.
Other Desired Qualifications: The student should be industrious and, given proper training, capable of working independently.
Contact Info:
Paul O’Brien (paul.obrien@lassonde.yorku.ca)



Two-Phase Heat Transfer for Additively Manufactured Electronics Cooling Technologies
Professor: Roger Kempers
Lab Website:
www.tf-lab.ca
Position Type:
NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Additive manufacturing (AM) affords the ability to create complex and geometrical structures which can be optimized to increase heat transfer performance in a variety of two-phase heat transfer scenarios. One popular two-phase heat transport loop which can benefit is the looped thermosyphon in which boiling occurs at high heat fluxes it the evaporator.
          
This project addresses the experimental characterization of two-phase heat transfer for AM evaporator devices including pool boiling and flow boiling.  The experimental results will be used to develop an improved understanding of two-phase heat transfer dynamics, develop predictive models to design and enhance two-phase looped thermosyphons devices for specific industrial applications.
           The experimental research program builds upon existing experimental apparatuses used for fundamental two-phase heat transfer research and extends into specific device-level applications such as thermosyphons or other industrial application-based test setups.
Students will develop CAD models, perform engineering design calculations and simulations, fabricate and assemble hardware and instrumentation. They will communicate their findings orally during weekly meetings and will author a final paper which for submission to a conference or a journal at the end of their project.
Duties and Responsibilities: Hands-on experimental fabrication and testing, data acquisition and instrumentation setup, CAD and simulations, data collection and analysis, and technical writing
Desired Technical Skills: Good working knowledge of mechanical engineering and hands-on ability, ability to fabricate and test components, experimental data collection and analysis, and SolidWorks and MATLAB experience.
Desired Course(s): Mechanical engineering students.
Other Desired Qualifications: Good verbal, written and presentation communication skills, and the ability to self-motivate and work well with limited direction.
Contact Info:
Roger Kempers (kempers@yorku.ca)



Metal 3D Printing and Machine Learning: Development of Next-Generation Advanced Materials
Professor: Solomon Boakye-Yiadom
Lab Website: https://pspp-of-materials.apps01.yorku.ca/
Position Type:
NSERC Undergraduate Student Research Award (USRA)
Open Positions:
3
Project Description: A fundamentally new idea where alloys have no single dominant element has gained traction in advance alloy development and materials discovery. These new alloys, with multi-principal elemental combinations and compositions, specifically, Complex Concentrated Alloys (CCAs) including High Entropy Alloys (HEAs), possess properties superior to those of conventional alloys. Despite the substantial progress in this area and their desirable properties, these new alloys are challenging to develop and manufacture because of their multi-elemental combinations and associated compositions. Artificial intelligence, specifically, sophisticated machine learning algorithms, using high throughput screening and advanced manufacturing technology have great potential in accelerating the discovery and development of new and advanced CCAs. However, there is an extensive lack of fundamental data needed for the rapid screening of the possible elemental combinations and compositions including developing sophisticated AI/ML algorithms which are required to expedite the discovery and development of new CCAs. Also, data on the effect of process parameters on the multi-elemental combinations and associated compositions including how the processing parameters influence their chemistry, structure and properties are lacking. This research project aims to: accelerate the screening and identification of new CCAs including the development of machine learning algorithms for high throughput screening of advanced materials; rapidly process formulated CCAs with different elemental combinations and compositions to understand the effect of the printing parameters on the multi-elemental combinations and associated compositions as well as establish relationships between process parameters, chemistry, microstructure, mechanical properties and part quality; and monitor and track in-situ meltpools, spatter/splatter, unmelted/over melted regions during rapid processing of CCAs including how the multi-elemental combinations and associated compositions affect defect formation in order to develop advanced machine learning algorithms that can predict defect generation during metal additive manufacturing of CCAs. The goal is to rapidly identify the possible elemental combinations and compositions of new CCAs (high throughput screening) and rapidly manufacture them using advanced manufacturing technologies such as metal Additive Manufacturing (AM).
Duties and Responsibilities: Development of machine learning algorithms for advanced materials discovery and defect detection during metal 3D printing.
Desired Technical Skills: Programming, machine learning, and AI.
Desired Course(s): Courses related to programming, software engineering, mechanical engineering, electrical engineering and computer engineering.
Other Desired Qualifications: Interests in machine learning, AI and advanced manufacturing technology.
Contact Info:
Solomon Boakye-Yiadom (sboakyey@yorku.ca)



Arduino-Based Sensing Platform for Rapid, Low-Cost, and High Sensitivity Detection and Quantification of Analytes in Fluidic Samples
Professor: Nima Tabatabaei
Lab Website: http://hbo.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2
Project Description: Lateral flow assays (LFAs; aka. Rapid Tests) are inexpensive paper-based devices for rapid and specific detection of analyte of interest (e.g. COVID virus) in fluidic samples. Areas of application of LFAs cover a broad spectrum, spanning from agriculture to food/water safety, to point-of-care medical testing, and most recently, to detection of COVID-19 infection. While these low-cost and rapid tests are specific to the target analyte, their sensitivity and limit of detection are far inferior to their laboratory-based counterparts. In addition, rapid tests normally cannot quantify the concentration of target analyte and only provide qualitative/binary detection.
We are currently developing a low-cost, end-user sensing platform that significantly improves the sensitivity of rapid tests. The developed platform is based on Arduino and utilizes low-cost far infrared, single-element detectors to offer sensitive and semi-quantitative results from commercially available rapid tests. The sensing paradigm integrated to the low-cost device is based on radiometric detection of photothermal responses of rapid tests in the frequency-domain when exposed to modulated laser excitation.
We are looking for motivated summer interns that are interested in the project with past experience and hands-on skills in instrumentation, Arduino-based programming, and/or mechanical design. Hired individuals will be mentored by professor Tabatabaei and will work on a daily basis with graduate students.
Duties and Responsibilities:
Student 1 will design and develop Arduino-based programs for data acquisition, processing, and reporting, and assist graduate students with experimentation and data processing. Student 2 will design and manufacturing of mechanical housing of device using 3D printing and perform validation tests, and assist graduate students with experimentation and data processing.
Desired Technical Skills: Student 1 should have past experience and hands-on skills in instrumentation and Arduino-based programming and have excellent work ethics and teamwork. Student 2 should have past experience and hands-on skills in instrumentation, mechanical design, and 3D printing and have excellent work ethics and teamwork.
Desired Course(s): Student 1 should be in Computer Engineering, Computer Science, Mechanical Engineering, or other programs with demonstrated relevant experience. Student 2 should be Mechanical Engineering or other programs with demonstrated relevant experience.
Other Desired Qualifications: Student should have excellent work ethics and work well in a team.
Contact Info:
Nima Tabatabaei (nimatab@yorku.ca)