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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.
 

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.
VALUE

$10,000

DURATION

4 Months
Jan 17, 2022: Competition Opened

Jan 25, 2022 9am -10am: Information Session

Feb 2, 2022 6pm – 7pm: Information Session

Feb 10, 2022 2pm – 3pm: Information Session

Feb 11, 2022 12.30pm – 2.30pm: Drop-in Session

Feb 16, 2022: Last day for professors to decide which student(s) to support

Feb 18, 2022 12.30pm – 1.30pm: Drop-in Session

Feb 21, 2022: Application Deadline

Late March: Awards announced

May: Summer Undergraduate Research Program Starts

May 3rd, 2022: Orientation Session: Copy of slides are available here and Zoom recording is here

June 29, 2022 2.30pm – 3.30pm: How to Write an Abstract Workshop

July 15, 2022: Abstract Submission Deadline

July 26, 2022 2.30pm – 3.30pm: How to Give a Presentation Workshop

August 15, 2022 9:00 AM: Presentation Submission Deadline

August 16, 2022: 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 of your 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 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 you will hit the ground running with your application the following year. Also, some first-year students do win the 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 a 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 (last updated Feb 15, 2022) for the slides.

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 submitting the application.

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 own 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): globhlth@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 2022. During this time undergraduate students will work full-time with a Lassonde researcher to complete a pre-defined research project
• Students may not enroll 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, other strong applicants will be awarded LURA.
• This year, there is funding to support over 60 student awards.

Information sessions

Please attend:

Jan 25, 2022 9am -10am: Information Session (https://yorku.zoom.us/j/99349619080)

Feb 2, 2022 6pm – 7pm: Information Session (https://yorku.zoom.us/j/91483907990 )

Feb 10, 2022 2pm – 3pm: Information Session (https://yorku.zoom.us/j/93884707194)


Feb 11, 2022 12.30pm – 2.30pm: Drop-in Session (https://yorku.zoom.us/j/93065189324)

Feb 18, 2022 12.30pm – 1.30pm: Drop-in Session (https://yorku.zoom.us/j/94735504439)

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 updated Feb 15, 2022) and the Zoom recording from 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.
Contact us at resday@yorku.ca

Browse Projects:



Timber Structures in Fire

Professor: John Gales
Lab Website: www.yorkufire.com
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: The outcome of COP26 found many engineering consultancy firms pledging to undertake increased sustainability considerations in their future design projects. This summer, as part of an overarching timber research program meant to address these goals, several projects will commence at York University in this theme. The research focus being on timber as a sustainable and resilient material for contemporary infrastructure. The work term for the successful candidate(s) will involve engineering research regarding fire safety sciences with a focus on timber structures and architecture. This work will involve sustainable material development, fire testing of building assemblies, and fires in relation to building science principles. 
Duties and Responsibilities: The successful candidate(s) will participate in hands field work and/or laboratory materials experimentation. Development of analytical skills with compiled data will follow.  Opportunities will be provided for writing and co-authoring academic reports and papers. Networking opportunities will also be available for the successful candidates to interact and engage with professional engineer collaborators.
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Computational skills, creativity, out of box thinking, communication, and writing
Desired Course(s):
Civil and Mechanical Engineering. Entering at least year 3 as of Fall 2022
Other Desired Qualifications: In addition of normal application procedures (CV and Cover letter to jgales@yorku.ca) applicants should submit a well-developed short document on their opinion on the topic of “Enhancing Equity, Inclusion and Diversity in Engineering”.
Contact Info:
Prof. John Gales (jgales@yorku.ca)



Synthesis and characterization of Imipenem-metal complexes
Professor: Satinder K. Brar
Lab Website: https://inzymes.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: The project includes the preparation of Imipenem-metal complexes and characterizing them with standard analytical techniques. (a) UV-visible and vibrational spectroscopy to determine the binding positions. (b) Potentiometric titrations for M: L stoichiometry and stability constants. (c) Thermogravimetric analysis (TGA) for thermal stability and oxidation state of the metal. (e) Kinetics and half-life studies; and (f) Toxicity assays; (d) LC-MS for stoichiometry, metal ion selectivity, fragmentation pattern, and determination of natural metabolites.
Duties and Responsibilities: The student will be required to assist in planning and performing the lab experiment, analyze the data, and attend the weekly group meetings.
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: MS Word, PowerPoint, Excel
Desired Course(s): Chemistry / Bio-chemistry/ Pharma/Environmental engineering
Other Desired Qualifications: Students with lab-experience are preferred.
Contact Info: Prof. Satinder K. Brar (satinder.brar@lassonde.yorku.ca)



Impacts of geothermal heating on bioremediation of BTEX in groundwater sources
Professor: Satinder Kaur Brar
Lab Website: http://inzymes.lab.yorku.ca
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: In Canada, geothermal heating has become a popular option for heating and cooling buildings; however, little is known about the environmental impacts of geothermal heating on the subsurface environment and bioremediation. In comparison to conventional energy resources, Geothermal heat pumps (GHPs) play role in energy saving and greenhouse gas (GHG) emission reductions by achieving 40−70% energy savings and several thousand tons per year saving of CO2 by 2017 worldwide. GHPs may also assist in enhancing in-situ bioremediation of pollutants from groundwater (GW) and soil by warming up the subsurface due to stored heat. This idea coincides with a rising movement towards sustainable and green remediation of subsurface pollution including BTEX and other contaminants. In the present study, the focus will be towards the degradation of BTEX, a pollutant found in the second-highest proportion in Canada after polyaromatic Hydrocarbons and has the potential to cause mutagenic and carcinogenic effects. Hence, GHPs could be a potential tool to solve two major environmental crises of energy demand and pollution by providing renewable energy and assisting bioremediation.
Duties and Responsibilities: Have to assist in laboratory experiments with the Ph.D. student,
Analyzing data,
Planning experiment,
Attending lab meeting
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Computer Usage: Word, Excel, PowerPoint;
Desired Course(s): Environment Engineering, Geotechnical Engineering, Biotechnology
Other Desired Qualifications:
Experience in lab work and responsible
Contact Info: Prof. Satinder Kaur Brar (satinder.brar@lassonde.yorku.ca)



Ensuring point of use water safety in decentralized drinking water systems in Canada
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: Decentralized drinking water systems (DDWS) include trucked water systems, small non-municipal systems like campgrounds and trailer parks, and buildings supplied by private wells, among others. With some notable exceptions, DDWS are generally located outside of major cities and have different water safety issues than centralized municipal systems. DDWS are responsible for a disproportionate number of water safety incidents around the world. Finding effective, workable ways to address water safety hazards in DDWS is key to improving water safety in Canada and in other parts of the world.
The successful applicant will work with Dr. Stephanie Gora, an assistant professor in Civil Engineering at the Lassonde School of Engineering, and her research team on laboratory and field projects related to point of use water safety in DDWS in Canada. The student will also work on an independent desktop project related to the review of water system design and water quality data analysis and visualization for DDWS in Nova Scotia.
Training and support will be provided by Dr. Gora and other members of the research team. Dr. Gora will support the student if they wish to present their work at a local or online conference in the form of a poster or oral presentation.
Duties and Responsibilities: The research assistant will review design drawings and water quality and operational data from decentralized drinking water systems in Canada. They will identify water hazards, information gaps, and potential solutions for each system. The results of this analysis will be summarized in a report and presented at the Lassonde Undergraduate Research Conference in August 2022. The research assistant will also support two graduate students working on drinking water safety projects. Pending pandemic restrictions, this could involve up to 3 days per week in the laboratory or in the field. Travel expenses will be covered for field work activities.
The research assistant will develop skills related to data management, data analysis, data visualization, communication of findings, reviewing design drawings, and reviewing academic and government literature. They will also learn about drinking water safety, drinking water infrastructure, and drinking water management in Canada. Should restrictions permit, the research assistant will also develop basic laboratory and/or field research skills.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Required: Microsoft Office, ability to work independently, strong communication skills, basic understanding of fluid mechanics and environmental chemistry, willingness to conduct lab and/or field work pending pandemic restrictions
Desired Course(s): Civil engineering or other discipline with some experience with hydraulics and environmental chemistry topics
Other Desired Qualifications:
Desired but not required: Knowledge of R or another programming language, familiarity with design drawings, valid Ontario G license or equivalent, prior laboratory experience, prior field experience
Contact Info: Prof. Stephanie Gora (stephanie.gora@lassonde.yorku.ca)



Laboratory and modelling work examining transport of chemicals through an engineered barrier to used nuclear canisters
Professor: Magdalena Krol
Lab Website: https://lassonde.yorku.ca/users/magdalena-krol
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. The successful candidate will work in the Civil Environmental laboratory and conduct diffusion and sorption experiments to identify various parameters under different repository conditions, or simulating transport using COMSOL. This will involve working closely with graduate students in the lab as well as the faculty supervisor, Professor Magdalena Krol.
Duties and Responsibilities: The undergraduate students will work closely with graduate students as well as postdoctoral fellows and the faculty supervisor, Professor Magdalena Krol. The student working in the lab 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 working on the simulations will be given training on the COMSOL multi-physics platform. 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.
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Good foundation in chemistry and interest in environmental issues; good communication skills and a team player; Experience working the lab an asset.
Desired Course(s): Second year or higher. Civil undergraduate students preferred. Course completion requirement: CIVL 2240.
Other Desired Qualifications: Good communication skills, organized, able to follow instructions.
Contact Info: Prof. Magdalena Krol (mkrol@yorku.ca)



Web-based course tools
Professor: Magdalena Krol
Lab Website: https://lassonde.yorku.ca/users/magdalena-krol
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: As York University aims to train top engineering talent in Canada, a key component of this program is to ensure that the students are both: (i) trained in the state-of-the-art digital engineering tools and (ii) have a strong command of the fundamental processes being modelled and coding behind those tools.  As part of this summer project, a student will work on a new set of web-based tools that would aid in teaching of several fundamental environmental engineering courses (e.g. CIVL 2240 and CIVL 4013). These tools would include a calculator and visualizer of pollutant transport through the subsurface, co-boiling points of various chemicals, and other environmental engineering principles.
Duties and Responsibilities: The undergraduate student will work closely with graduate students as well as postdoctoral fellows and the faculty supervisor, Professor Magdalena Krol. The student will learn fundamental principles that govern environmental engineering phenomena. They will apply this knowledge into a web-based platform that includes a visualization tool. Their responsibilities will include the development of the web-based tool, debugging, and testing it for quality assurance purposes. The student will also be involved in report writing, presentations, and group meetings.
Work Setting: Project work is carried out remotely
Desired Technical Skills: Good understanding of web-based coding tools such as python. Understanding of version control and best practice coding techniques. Good foundation in chemistry and/or interest in environmental issues; good communication skills and a team player.
Desired Course(s): Software engineering, computer engineering, computer science or civil engineering (if has previous coding experience)
Other Desired Qualifications: Good communication skills, organized, able to follow instructions.
Contact Info: Prof. Magdalena Krol (mkrol@yorku.ca)



Next-generation bioengineered systems for upcycling of food waste to high-value products in a circular economy approach
Professor: Satinder K Brar and Guneet Kaur
Lab Website: https://lassonde.yorku.ca/users/satinder-brar
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 3
Project Description: Project Goal: The project aims to develop transformative and advanced bioengineered processes for sustainable biomanufacturing of liquid biofuel and bioplastics precursors via upcycling of food waste. Specifically, it is poised to achieve the UN Sustainable Development Goals (SDG) 7, 9, 11 and 13 through advanced food waste biorefinery processes and high-value multi-product chain that will create economic benefits, promote advanced resource recovery and circular economy, and reduce environmental impacts. This will be achieved through bioconversion of food waste to carboxylates, which are high-value building blocks for synthesis of a wide array of currently fossil-fuel derived products such as polymers (plastics), alcohols (e.g., bio-butanol as biofuel), ketones and olefins.
Background: Food waste is a resource. It generates enormous energy, chemical and material potential due to the functionalized molecules stored in it. In Ontario, a staggering amount of 3.7 million tonnes/year of food and organic waste is generated, however, it is currently underutilized, and about 60% of it ends up in landfills, which is not a sustainable option. Food waste can be converted into carboxylates and this conversion occurs from the first two steps i.e., hydrolysis and acidogenesis during anaerobic digestion (AD) of food waste by employing anaerobic reactor microbiomes i.e., mixed cultures. However, their bioconversion is confronted with various challenges. During carboxylate fermentation, different metabolic products such as volatile fatty acids (VFA) e.g., acetate, propionate, butyrate and lactate, alcohols e.g., ethanol, propanol, and gases e.g., H2 and CO2 are formed by various metabolic reactions carried out by a consortium of acidogenic microorganisms. Various operating parameters determine which metabolic reactions will be thermodynamically feasible, which microbes will be enriched and what will be the product spectrum. Further, the VFA pose recovery difficulties while also being toxic for microbial metabolism during acetogenic fermentation. Therefore, the key questions that we are addressing in this project focus on process engineering factors that regulate acid metabolism, control of microbial community dynamics for enhanced VFA production, and efficient extraction and recovery of VFA from acidogenic fermenter for process intensification.
Research approach: Advanced bioprocessing technologies, cutting-edge microbial community analyses and engineering techniques, in-situ product recovery (ISPR) systems and biotransformation strategies for advanced downstream processing will be employed as critical tools for developing high yield carboxylate processes and recovering multiple high-value products.
Duties and Responsibilities: UG students will engage in one of the following research activities:
1) Process development and microbial community studies. UG student will work on bioreactor processes and configurations for carboxylate production and perform metagenomic studies to correlate process conditions with microbiome. They will learn aspects of bioprocess engineering, anaerobic fermentations, physico-chemical characterizations, and microbial genomic techniques.
2) Development of in-situ product recovery (ISPR) techniques and secondary processes & products from carboxylate platform. UG students will develop projects such as selection of VFA extractants and derivatization of secondary bioproducts by chain elongation methods. They will become proficient in bioseparations, sustainable chemistry and biotransformation.
The project will provide unique skillsets and competencies to UG students to address challenges in field of biomanufacturing, waste treatment, microbial engineering, and applications of recovered products, which are all in-demand skills in the rapidly growing clean-tech and biotechnology industry in Canada and across the world.
UG students will work with Masters and PhD students and become proficient in teamwork and collaboration, that are essentially skills in any academic or industry setting.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Bioreactor operation, microbial cultivation, metagenomics, process optimization (statistical/DoE approach preferably), food waste treatment
Desired Course(s): Environmental Engineering, Chemical/Biochemical Engineering or Environmental/Applied Microbiology
Other Desired Qualifications: Knowledge and experience in microbial processing
Contact Info: Prof. Satinder K Brar and Guneet Kaur (satinder.brar@lassonde.yorku.ca)




Sustainable Freight Fluidity on Urban Roadway Network to Support the Resilient Supply Chain Flows

Professor: Peter Park
Lab Website: https://lassonde.yorku.ca/users/peter-park
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 5
Project Description: Research Objectives:
The concept of “freight fluidity” was first popularized by Transport Canada to evaluate the performance of corridors that are used heavily by freight transportation (goods movement). For Transport Canada, the freight fluidity is typically measured using total travel time and travel time reliability of goods along known truck routes. However, there are many other potential measures available that can provide a comprehensive view of supply chain efficiency such as delay per kilometre. Identified measures will help Regional engineers understand bottlenecks in major truck routes. This project will use ATRI/HERE data via SFC’s freight warehouse. This project deliverable will include an electronic map and final report that can be used as a meaningful resource for Regional engineers to minimize traffic constraints on the fluid movement of goods to/from major business centres within the Region.
Resources and Activities:
York University has access to many valuable datasets that will be used for this project. They include:
• HERE Traffic Flow Data: HERE data provide near real-time traffic flow for most roadway segments in the Region. HERE collects traffic flow information (e.g., average travel speed) from a wide range of road sensors, vehicle sensors, smartphones, and other automated data collection methods, and updates the information every few minutes.
• ATRI/SHAW Data: ATRI/SHAW Data provides precise freight movement information based on truck GPS data collected from nearly 50,000+ commercial trucks traveling across North America. The dataset includes billions of GPS traces truck positions annually in North America. The truck traces provide a massive quantity of valuable empirical data on spatial and temporal truck travel activities including truck parking.
• Individual Property Parcels Data: Teranet Enterprises Inc.’s data will be used to identify the layout of major business centres.
• DMTI Enhanced Points of Interest (EPOI) Data: This firm dataset provides GIS friendly information on the location of establishments along with industry codes capable of identifying major business centres.
Anticipated Results:
This study will search and evaluate potential measures for freight fluidity of heavy commercial vehicles. The candidate measures include, but are not limited to, 1) total travel time, 2) travel time reliability such as the travel time index or buffer time index, 3) delay per kilometer, 4) mean, median (50 percentile), and 95 percentile travel time, etc. This study will propose the most suitable measures for the Region and will produce a set of electronic maps (in shapefile format) to visualize the identified measures for each segment on major truck routes in the Region. The produced map can be used to present the varying levels of supply chain flows for key trade routes in the Region.
Duties and Responsibilities: Task 1 – Review State-of-the-Practice Freight Fluidity Studies
The YorkU project team will review published reports and online information about freight fluidity studies and other related topics to identify the most suitable freight fluidity measures for major truck routes in the Region of Peel. This task will focus on existing or on-going studies in North America.
Task 2 – Acquire and Amalgamate Data
The GPS truck travel data required will be obtained from various potential sources including ATRI and/or HERE data and possibly other data such as commercial vehicle survey data collected by the Ministry of Transportation of Ontario.  The dataset will be processed for quality control and will be amalgamated into a single database suitable for analysis with various modelling tools (e.g., ESRI ArcGIS).
Task 3 – Conduct Descriptive Data Analysis of GPS Datasets
The YorkU research team will conduct a temporal and spatial analysis to highlight areas of concern (bottlenecks) where heavy commercial vehicles movement appears to be adversely impacted. For this task, research team will first identify free-flow travel speed on Regional roads using HERE data as well as posted speed limit data. We then estimate various measures including speed difference between free-flow and actual travel speed (delay) observed from ATRI data. The estimation will be conducted for different time periods (e.g., AM-Peak, mid-day, PM-Peak) in a day, different day in a week, and different months in a year. The descriptive data analysis will suggest which truck routes in the Region of Peel appear to be most concerning routes in terms of freight fluidity and most likely to be important routes to helping to move heavy commercial vehicles to/from major business centres.
Task 4 – Develop a Set of Electronic Maps based on the Estimated Freight Fluidity
The YorkU project team will apply parametric statistical tests to compare each of the candidate measures of freight fluidity. The project team will suggest the most suitable measure(s) for the Region based on the test results. Bottlenecks that may be a concern in terms of maintain proper supply chain flows will be visible easily in the maps produced. The map can display the economic costs of delay based on some inputs from a separate Transport Canada study (in regards to the estimated monetary value of goods movement) and the mean value of delay for major truck routes identified in this project.
Task 5 – Prepare Final Report and Presentation
The YorkU project team will prepare a final report that includes all of the findings from Tasks 1 to 5. The final report will present the study’s analysis, results and conclusions. The YorkU project team will also prepare, submit and present an electronic presentation that will describe and summarize the project’s deliverables. The presentation and discussion the major findings may occur in a meeting with the Region or via an on-line meeting.
Work Setting: Project work is carried out remotely
Desired Technical Skills: MS Office,; Strong Communication Skill (especially Good Writing Skill)
Desired Course(s): CIVL3160 and CIVL3260 or equivalent
Other Desired Qualifications: Strong Enthusiasm
Contact Info: Prof. Peter Park (peter.park@lassonde.yorku.ca)











Star Tracker Design for Space Object Tracking

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: Main research objective is to design and characterize a camera payload for upcoming CSA Stratospheric balloon mission (2022 launch) to demonstrate Resident Space Object (RSO) detection using a low-resolution camera. LURA or USRA students will assist in the final testing, assembly and operation of the payload for 2022 August launch and prepare for the post-launch data process.
Duties and Responsibilities: Each RA will be responsible for a series of functional test of the payload (long-form functional, thermal cycling, battery life, etc.) to demonstrate the full functionality of the payload. (S)he will also develop an algorithm to process on-orbit data to detect and characterize objects in space. the team will work closely with Magellan (Industry sponsor) and DRDC (government research partners) to further develop infrastructure (a network of small satellites equipped with proposed payload) to provide security and sustainability in operations in space environment.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Programming (Phython and MATLAB preferred), working knowledge in embedded system, CAD skills
Desired Course(s): Space, Computer, Electrical or Mechanical
Other Desired Qualifications: N/A
Contact Info: Prof. Regina Lee (REGINAL@yorku.ca)



Atmospheric Modelling – Internal Wave Processes
Professor: Gary Klaassen
Lab Website: https://lassonde.yorku.ca/users/gklaass
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: Internal waves generated in the lower atmosphere transport momentum to high altitudes where it is deposited and plays a major role in determining the large-scale circulation of the middle atmosphere. Climate and weather prediction models cannot adequately resolve this process, so they rely on parameterization schemes. This project will investigate wave generation and deposition mechanisms and evaluate the characteristics and performance of different parameterization schemes in order to highlight their strengths and weaknesses, and point the way to better climate models.
Duties and Responsibilities: Computer programming; developing, modifying and running computer models; visualization of model output; literature review; solving mathematical equations; scientific presentations and report writing.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Computer programming experience in Fortran, Matlab or Python is required. Familiarity with atmospheric dynamics or fluid dynamics would be an asset.
Desired Course(s): Completion of MATH 2015, MATH 2271, EECS 1541 or EECS 1540 or EECS 2501 or equivalents. ESSE 1011, ESSE 2012 or other Atmospheric Science courses would assets.
Other Desired Qualifications: TBD
Contact Info: Prof. Gary Klaassen (gklaass@yorku.ca)



Control and Navigation of Autonomous Unmanned Vehicles (AUVs)
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 maneuvering 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, defense 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. Compared to single AUV, systems with multiple AUVs are more effective in various complicated team tasks due to its inherent advantages, including increased accuracy, robustness, flexibility, lower cost, energy efficiency, and the probability of success. With the recent advancements in AUV hardware and networked system theory, more and more missions require the cooperation of multiple AUVs.
This project is to develop control and navigation algorithms for autonomous unmanned vehicles. A multi-vehicle test facility has been developed at SDCNLab and will be used to validate the developed algorithms.
Duties and Responsibilities: The successful student will be working with graduate students and research fellows on (a) programming; (b) hardware development and tests. Through these activities, the student 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.
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: (1) Good programming skills, MATLAB, C, and Linux; (2) Enrolled in engineering degree; (3) Familiar with ROS; (4) Team player.
Desired Course(s): Engineering
Other Desired Qualifications: None
Contact Info: Prof. Jinjun Shan (jjshan@yorku.ca)



Development of a GPS-reflectometry sensor for soil moisture determination

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: 2
Project Description: GPS, and now broadly GNSS (Global Navigation Satellite System), technology is ubiquitous and provides freely-available signals for a multitude of scientific and engineering applications.  One scientific application that York’s GNSS Lab has been investigating is the reception of ground-reflected signals for the purpose of inferring properties of the surface, such as surface soil moisture.  This science is its early days but represents the opportunity for very low-cost remote sensing of global soil moisture.
Students will work on the design, development and testing of our next generation Field Programmable Gate Array (FPGA)-based GNSS-reflectometry receiver that is based on a software-defined radio (SDR) design.  And also, the design, development and integration of supporting payload components: development board, PCB design and fabrication, computational testing, radio front-end testing and assembly, communications, data storage, housing, etc.
Duties and Responsibilities: Working with a team of PhD and MSc students in the design, development and testing of one or two specific payload components of the GNSS receiver for drone flights.  These components include, but are not limited to, signal acquisition and tracking C++ code development, FPGA versions of this code, and payload hardware enclosure design/development/testing.  Candidates may also work with graduate students in the collection of drone data in the field, system testing/debugging/tuning, and GNSS signal data analysis.  This work is globally leading-edge, so there is the high likelihood of conference and journal paper preparation experience as well.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Experience with SDR coding, FPGA design, and/or embedded systems.  Alternatively, experience with space hardware.
Desired Course(s): Current BEng or BSc student in Space Engineering, Electrical Engineering or a related field.  Having taken PHYS 3050 “Electronics I”, PHYS 3150 “Electronics II” or equivalent courses.  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.  Interested in graduate school.
Contact Info: Prof. Sunil Bisnath (sbisnath@yorku.ca)



Developing Cityscape Semantic Annotation Dataset for AI
Professor: Gunho Sohn
Lab Website: gunhosohn.me
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: Nature of the research project: This project aims to generate an innovative benchmark dataset used for training deep learning networks enabling semantic segmentation and object detection using mobile mapping datasets acquired over York University’s Keele Campus. In collaboration with Teledyne Optech, we will expect to acquire about 150 points per square metre and imagery with 10 cm resolution over the entire campus area. With a large amount of incoming data, the LURA trainee will closely work with graduate students and research fellows for conducting a literature review, labelling pipeline design, semantic labelling and quality assurance for AI training. The developed labelling pipeline will be scalable and optimized for training deep learning networks to produce 3D semantic city models at a large scale.
Specific research activities the student be engaged in:
– Conducting literature reviews and comparative analysis of the state-of-the-start cityscape semantic labelling benchmarks
– Post-processing the MMS data, including noise removal and terrain surface reconstruction
– Performing statistical analysis, semantic labelling, data exploration and visualization of MMS data acquired and labelling results
– Developing an efficient and interactive semantic labelling pipeline using GIS data and existing labelling tools
Types of research experience the student will receive:
– Enhancing oral and written communication skills, including conference presentation (the Lassonde Undergraduate Research Conference) and technical report
– Learning the dynamic associated with being part of a research team by attending a lab seminar and a project meeting with industrial collaborators regularly
– Providing hands-on research experience with active/passive sensors, data collection, sensor calibration, deep supervised learning and quality assurance
– Designing and troubleshooting a labelling and pre-/post-data processing experiment
– Applying statistical and error analyses, including through programming
Type of training and support that will be provided to the student in carrying out these research activities:
– The LURA student will work closely with the assigned benchmarking research team, which comprises several graduate students, two postdoctoral fellows and one research associate in the lab. The benchmark research team will provide online training sessions for 1) labelling software tools, 2) Esri’s ArcGIS Pro for GIS data processing, 3) programming with Python and Matlab.
– Our industrial partners will provide multiple training sessions for introducing the MMS technologies and ArcGIS products through monthly technical workshops.
Qualification: We are looking for a self-motivated and talented science and undergraduate engineering student (2nd year or above) in geomatics engineering, computer science, software engineering or relevant fields, having a strong interest in computer vision, machine learning, remote sensing, photogrammetry and GIS. The LURA applicant must have the following skillsets:
– Strong oral and written communication skills
– Having experience in conducting a literature review
– A basic understanding of data structure, statistical analysis, and data exploration and visualization
– Having a fundamental programming skill in Python or Matlab
– Having experiences in GIS software, computer vision and machine learning (not mandatory)
Duties and Responsibilities: The LURA students will conduct the following tasks:
– Preparing a weekly progress report summarizing individual working progress, analyzing potential risks and finding potential solutions to resolve issues
– Attending research meetings with the research team and industrial partners/academic collaborators on a regular basis
– Designing, implementing and validating 3D semantic labelling systems
– Designing, implementing and validating an MLSOps pipeline for optimizing AI networks
– Writing a technical report and a journal to summarize the final outcomes of the project
– Presenting the final project outcomes to a public event including the project’s technical workshop, Lassonde UG Conference and relevant academic workshop/conference
Work Setting: Project work is carried out remotely
Desired Technical Skills: Programming (Python, Matlab, C++), Open libraries in Computer Vision, Deep Learning, GIS and Photogrammetry
Desired Course(s): Relevant course in object oriented programming and data structure; prefer to take one of the following courses (computer vision, machine learning, photogrammetry)
Other Desired Qualifications: Certificate or courses in GIS and Remote Sensing, or data base or MLOps
Contact Info: Prof. Gunho Sohn (gsohn@yorku.ca)



Developing Visual Annotation Dataset for Autonomous Train
Professor: Gunho Sohn
Lab Website: gunhosohn.me
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: Nature of the research project: This project aims to generate an innovative benchmark dataset used for developing deep learning-based computer vision algorithms to control an autonomous train. In collaboration with Thales Canada, we are developing innovative deep learning systems enabling the detection of tracks, obstacles, wayside objects and semantic classes using lidar and cameras mounted on a low-speed train for autonomously controlling it. In recent years, deep learning technology has evolved rapidly and achieved astonishing success in many computer vision tasks. However, the success of developing deep learning technologies is largely dependent on the provision of a massive amount of high-quality training samples. We have acquired a large-scale of visual data from Metrolinx and New York City Transit. In this project, the RAY trainee will closely work with graduate students and research fellows for conducting a literature review, labelling pipeline design, semantic labelling and quality assurance for AI training. The developed labelling pipeline will be scalable and optimized for training deep learning networks to produce cutting-edge semantic labelling benchmark for developing autonomous trains.
Types of research experience the student will receive:
– Enhancing oral and written communication skills, including conference presentation (the Lassonde Undergraduate Research Conference) and technical report
– Learning the dynamic associated with being part of a research team by attending a lab seminar and a project meeting with industrial collaborators regularly
– Providing hands-on research experience with active/passive sensors, data collection, sensor calibration, deep supervised learning and quality assurance
– Designing and troubleshooting a labelling and pre-/post-data processing experiment
– Applying statistical and error analyses, including through programming
Type of training and support that will be provided to the student in carrying out these research activities:
– The RAY student will work closely with the assigned benchmarking research team, which comprises several graduate students, two postdoctoral fellows and one research associate in the lab. The benchmark research team will provide online training sessions for 1) labelling software tools, 2) in-house sensor calibration and alignment tools, 3) programming with Python and Matlab.
– Our industrial partners will provide multiple training sessions for introducing the autonomous train navigation, control and simulation technologies through monthly technical workshops.
Qualification: We are looking for a self-motivated and talented science and undergraduate engineering student (2nd year or above) in geomatics engineering, computer science, software engineering or relevant fields, having a strong interest in computer vision, machine learning, remote sensing, photogrammetry and GIS. The RAY applicant must have the following skillsets:
– Strong oral and written communication skills
– Having experience in conducting a literature review
– A basic understanding of data structure, statistical analysis, and data exploration and visualization
– Having a fundamental programming skill in Python or Matlab
– Having experiences in GIS software, computer vision and machine learning (not mandatory)
Duties and Responsibilities: Specific research activities the student be engaged in:
– Conducting literature reviews and comparative analysis of the state-of-the-start cityscape semantic labelling benchmarks
– Post-processing the autonomous visual data, including noise removal, data fusion, and sensor calibration and alignment
– Performing statistical analysis, semantic labelling, data exploration and visualization of autonomous train data acquired and labelling results
– Developing an efficient and interactive semantic labelling pipeline using as-built wayside vectors and existing labelling tools
Work Setting: Project work is carried out remotely
Desired Technical Skills: Programming skills in Python or Matlab or C++; OpenCV, Point Cloud Library
Desired Course(s): Requires to take courses such as object oriented programming and data structure; preferably one of courses in computer vision, machine learning and photogrammetry
Other Desired Qualifications: Certificates in GIS and Remote Sensing or database, MLOps
Contact Info:
Prof. Gunho Sohn (gsohn@yorku.ca)



DEVELOPING MIXED REALITY SANDBOX TO TEACH EARTH SYSTEM ENGINEERING 
Professor: Mojgan Jadidi
Lab Website: https://lassonde.yorku.ca/users/mjadidi 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 3
Project Description: Augmented Reality (AR) and Virtual Reality (VR) technologies, called as Mixed Reality (XR) expand the physical world by adding digital information layers onto what we can see with the naked eye both in an augmented setup and virtual environment. The virtual layers (topography, geology, hydrogeology, and other Earth-related-elements) help students engage with the visualization of 3D problems, something that traditional 2D class material does not readily afford. We adopted an Augmented Reality Sandbox (AR Sandbox) created by UC Davis, which enables students to create 3D topography in real-time using the 3D surface of a physical sandbox, a Microsoft Kinect (depth sensor), and a projector. The AR sandbox allows students to create their desired 3D scene (e.g., mountains, valleys, rivers, and water bodies) within the sandbox, and then a 3D-coloured topographic map and contour lines of the scene are projected, in real-time, onto the sand. In addition, we are extending this system to web-based VR environment at Unity platform, where we developed a virtual sandbox (replica of AR sandbox). The main goal of this project is to embed such technologies and enhance our students’ experiential education from first through to fourth year courses across Lassonde (e.g., common ESSE1012 to ESSE/CIVL programs). We are aiming to study the student satisfaction and measure students’ learning and the role this system with students’ success. Therefore, this came to our attention to revamp our student experience at Lassonde and embed such an XRS sandbox into a stream of courses. To do so we are seeking motivated and passionate undergraduate students to work on three pillars of this project during summer: 

Student 1: Game Developer – able to program at Unity platform, able to devise scenario and turn them to a series of game (Computer Graphics or Software Engineering student) 

Student 2: Develop Use case scenario for Civil courses (Civil Student upper year student) 

Student 3: Expanding AR system, improving pipeline to Unity System (Geomatics, Space, or Software Engineering Student) 
Duties and Responsibilities:
Student 1: Game Developer – programming at Unity platform, able to devise scenario and turn them to a series of game activities, technical report, and presentation  
Student 2: Develop a series of Use case scenario for Civil courses and work with game developer for testing the Sofware, prepare user experience survey and analyse data, technical report, and presentation      
Student 3: Expanding AR system, improving pipeline to XR Unity System, working with two other team members, technical report, and presentation  
Work Setting: Preferred in person but flexible
Desired Technical Skills:
Student 1: Game Developer – familiar with Unity platform and programming, Understand Human-cantered design approach, able to devise scenario and turn them to a series of game 
Student 2: Have strong knowledge of upper year civil Hydrology, water resource management, geology, and geotechnical courses 
Student 3: Python programming, hands-on experience with AR sandbox, ability to work in Linux environment, ability to prepare, clean and process point clouds, experience with sensor integration 
Desired Course(s):
Student 1: Game Developer – Computer Graphics, Digital Media, or Software Engineering student 
Student 2: Civil Engineering  
Student 3: Geomatics, Space, or Software Engineering Student  
Other Desired Qualifications: n/a
Contact Info: Prof. Mojgan A. Jadidi (mjaddi@yorku.ca)

Elliptilinear 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 shape 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 shape from the occluding contour.   To explore this conjecture, we partition the problem into two parts:  1) Estimation of the 3D rim from the 2D occluding contour, and 2) 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 elliptilinear representations of occluding contours.  The student will validate the software and then analyze how these occluding contour representations relate to elliptilinear 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 postdoctoral fellow Yiming as well as tri-weekly meetings with principal investigator Prof. James Elder. 
Work Setting: Project work is carried out remotely
Desired Technical Skills: MATLAB; Aptitude in mathematics and statistics
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact Info: Prof. James Elder (jelder@yorku.ca; Anna Kajor <akajor@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: Supervisor: Maria Koshkina
Tracking players in sports videos 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.
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. 
Work Setting: Project work is carried out remotely
Desired Technical Skills: Understanding of deep learning methodology; Python programming experience
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact Info: Prof. James Elder (jelder@yorku.ca; 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: Supervisor: Sajjad Savoji
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. 
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 to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder. 
Work Setting: Project work is carried out remotely
Desired Technical Skills: Software – Python, MATLAB; Concepts – Familiarity with computer vision and 3D geometry skills preferred.
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact Info: Prof. James Elder (jelder@yorku.ca; 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: Supervisor: Thao Tran
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 project will involve developing a GUI environment with 2D and 3D widgets that accepts user input through mouse interactions on a 2D image.   The annotation and ground truth will be used to train a deep learning network for 3D object detection and estimation of motor vehicles.
Duties and Responsibilities: Develop and maintain a software tool using Python, PyQt, OpenGL (pyopengl), and OpenCV. The student will work with OpenGL 3D and 2D environments and interface with back-end functions.  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. 
Work Setting: Project work is carried out remotely
Desired Technical Skills: Software – Python, OpenGL; Concepts – Familiarity with computer vision and 3D geometry skills preferred.
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact Info: Prof. James Elder (jelder@yorku.ca; Anna Kajor<akajor@yorku.ca>)



Attentive Sensing for Vulnerable Road Users
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: Supervisors: Kartikeya Bhargava, Nizwa Javed
A current issue for video-based traffic analytics is to understand vulnerable road user behaviour as they step off the curb. In addition, it may be important to get detailed information about these road users to assess vulnerability.  For example, is this person walking with a cane?  Are they pushing a stroller?  Are they distracted by a cell phone?  Answering these questions requires high-resolution video data, and we have developed and deployed a specialized attentive sensor at an intersection in the GTA that can provide the resolution needed.
We will assess to what degree this system improves the accuracy of detection and tracking of these road users.  We will also explore the use of this higher-resolution data stream to automatically estimate the vulnerability of each road user.  To this end, we will first mine the literature on factors that contribute to vulnerability.  This could include mode of transportation (bicycle, wheelchair, motorcycle),  age, motor disability, visual disability, distraction (e.g., with a cellphone), inebriation, encumbrance (e.g., with a stroller) etc.  From the literature we hope to extract a quantitative model that predicts risk based upon these factors.  This will then lead to a set of target factors for which manual ground-truth datasets are constructed.  Classifiers will then be trained to estimate these factors, which can then be used to predict vulnerability for each road user.  A second task will be to predict the time required to cross the intersection based on these factors.
Duties and Responsibilities: The student will work closely with the supervisors to collect and label video data, and to train and evaluate classifiers and road-crossing time estimators. The student will have daily meetings with Software Engineer Kartikeya Bhargava and PhD student Nizwa Javed to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder. 
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Machine learning, software
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact Info: Prof. James Elder (jelder@yorku.ca; Anna Kajor <akajor@yorku.ca>)



Attentive Sensing for Mobile Robotics
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: Supervisors: Kartikeya Bhargava, Nizwa Javed
It is often useful for mobile robots to have panoramic (360 deg) vision, but this limits resolution needed to make finer-scale judgements (e.g., face, gender, expression recognition).  In this project, we explore the use of attentive sensing to address this problem.  On our mobile robot test platform, four Intel RealSense cameras provide a panoramic field-of-view, allowing events of interest to be detected and localized.  A fifth camera with a long lens, coupled with an oblique mirror mounted on a rotational motor will serve as the attentive sensor.  By spinning the mirror, the attentive sensor can be rapidly deployed in any direction, providing high resolution sensing to support detailed analysis. 
Duties and Responsibilities: The student will work closely with the supervisors to design, build and validate this attentive sensing system.  The student will then work with the team to evaluate the sensor on several fine-scale benchmark tasks.  The student will have daily meetings with Software Engineer Kartikeya Bhargava and PhD student Nizwa Javed to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder. 
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: SolidWorks, Camera systems, Raspberry Pi, NVIDA Jetson, motor control
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact Info: Prof. James Elder (jelder@yorku.ca; Anna Kajor <akajor@yorku.ca>)



Cleanbot
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: Supervisors: Kartikeya Bhargava, Nizwa Javed
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. 
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 Software Engineer Kartikeya Bhargava and PhD student Nizwa Javed to discuss progress, as well as tri-weekly meetings with principal investigator Prof. James Elder.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: ROS, control algorithms, software skills, 3D geometry
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact Info: Prof. James Elder (jelder@yorku.ca; Anna Kajor <akajor@yorku.ca>)





Image Processing for Software Engineering
Professor: Maleknaz Nayebi
Lab Website: http://www.maleknazn.com/
Position Type: NSERC Undergraduate Student Research Award (USRA);Lassonde Undergraduate Research Award (LURA);
Open Positions: 2
Project Description: Developers are increasingly sharing images in social coding environments alongside the growth in visual interactions within social networks. The analysis of the ratio between the textual and visual content of Mozilla’s change requests and in Q/As of StackOverflow programming revealed a steady increase in sharing images over the past five years. Developers’ shared images are meaningful and are providing complementary information compared to their associated text. Often, the shared images are essential in understanding the change requests, questions, or the responses submitted. Relying on these observations, we delve into the potential of automatic completion of textual software artifacts with visual content.
gathering and automatically mining these images is part of the project to be conducted in a team with other researchers.
Duties and Responsibilities: – gathering data
– pre-processing and mining the metadata
– pre-processing and mining images
– auto captioning
– operating databases
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: python programming, image processing
Desired Course(s): EECS 3311
Other Desired Qualifications: team player, communication skills
Contact Info: Prof. Maleknaz Nayebi (mnayebi@yorku.ca)



Audio-Video Scene Recognition
Professor: Rick Wildes
Lab Website: https://vision.eecs.yorku.ca/main/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: Scene recognition in videos refers to the task of leveraging a temporal sequence of images to identify scenes (e.g., flowing river, 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 research assistant will gather audio-video test data from the web, curate it and test classification algorithms developed in our lab.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: equivalent of 3rd year major computer science skills
Desired Course(s): Disciplines: computer science, computer engineering or electrical engineering; desired course work: signal processing, computer vision
Other Desired Qualifications: Ability to work in a small team.
Contact Info: Prof. Rick Wildes (wildes@cse.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: Electroencephalography (EEG) is known as the best non-invasive method for high-resolution real-time monitoring of brain 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, thus achieving long-term ambulatory EEG recording. Successful development of such technology has a significant positive impact on many diagnostics, treatment, rehabilitation, and communication applications.
In the integrated Circuits and Systems Lab, we have developed a wearable wireless device that is designed to be used as a low-cost long-term brain monitoring solution capable of integrating a high number of recording channels. The device hosts a proprietary algorithm for the early detection of epilepsy seizures.
The main objective of this project is to design, develop, and test windows-based software (or an android app) that interacts with this wearable technology. The software/app is required to collect, store, and display the data received from the wearable device.
Duties and Responsibilities: The successful candidate will work closely with a PhD student to develop and test the first prototype of the above-mentioned wearable EEG recording device. The main responsibility of the student will be the development and testing of the computer software.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills:
– Basic understanding of signals and systems, experience in development of software that can interact with the Bluetooth port of a computer or cellphone
Desired Course(s): N/A
Other Desired Qualifications: N/A
Contact info: Prof. Hossein Kassiri (hossein@eecs.yorku.ca)



Design and Development of a 3D-Printed Headset for a Wearable EEG Monitoring Device

Professor: Hossein Kassiri
Lab Website: https://electronics.eecs.yorku.ca/
Position Type: NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: Electroencephalography (EEG) is known as the best non-invasive method for high-resolution real-time monitoring of brain 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, thus achieving long-term ambulatory EEG recording. Successful development of such technology has a significant positive impact on many diagnostics, treatment, rehabilitation, and communication applications.
In the integrated Circuits and Systems Lab, we have developed a wearable wireless device that is designed to be used as a low-cost long-term brain monitoring solution capable of integrating a high number of recording channels. The device hosts a proprietary algorithm for the early detection of epilepsy seizures.
The main objective of this project is to design, develop, and optimize a 3D-printed structure that is used as the frame of the wearable EEG headset. The structure will be designed to host both the EEG recording dry electrodes and electronics and should allow for adjustable positioning of electrodes for different skull shapes.
Duties and Responsibilities: The successful candidate will work closely with a PhD student to develop and test the first prototype of the above-mentioned wearable EEG recording device. The main responsibility of the student will be the design and development of the EEG headset’s frame through 3D printing.
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Previous experience in 3D printing, interest in health technologies
Desired Course(s): All Engineering students who meet the technical requirements are welcome to apply.
Other Desired Qualifications: N/A
Contact Info: Prof. Hossein Kassiri (hossein@eecs.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 center 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 privacy-sensitive than ever. Secondly, transferring data generated by a large number of nodes for processing burdens the network and becomes the bottleneck of overall performance. Thirdly, the centric fashion involves long propagation delay and incurs unacceptable latency, which is unbearable for many applications with instantaneous decision making. Therefore, a natural question arises with the concerns mentioned above: how to 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 system heterogeneity, an optimal strategy for resource allocation needs to be developed to maximize the efficiency of FL. Another challenge is communication cost during global model training. Compared with traditional distributed machine learning, where several computational centers 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, under the guidance of the supervisor and senior graduate students.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Good at coding. Have basic knowledge of machine learning.
Desired Course(s): students from computer science are preferred.
Other Desired Qualifications: Good GPA; Self-motivated.
Contact Info: Prof. Ping Wang (pingw@yorku.ca)



Gallium-Nitride Based Multi-MHz Bidirectional Power Interface for Integrated Energy Storage in a DC Microgrid
Professor: John Lam
Lab Website: https://pelser.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);Dr. James Wu Scholarship;
Open Positions: 1
Project Description: The Information and Communication Technology (ICT) data centers electricity consumption in Canada has already reached more than 90 peta-joules in 2020.  In addition, the COVID-19 pandemic has led to an explosive increase in digital communications and as a result, the data center energy consumption is rising at an alarming rate.  PV-powered datacenters with high voltage battery storage that utilizes a DC microgrid architecture is an attractive solution.  A new PV energy optimizer structure with integrated energy storage is currently under development.   Unlike the conventional centralized storage power architecture, the proposed design consists of an integrated energy storage power interface that stores extra extracted power or deliver required power directly.  To significantly reduce the physical size of the proposed system, latest surface-mount gallium-nitride (GaN) switching devices with ultra-small footprints will be employed in the devised circuit.  In this project, the student will be responsible for investigating and designing a GaN-based high frequency AC/DC energy storage bidirectional power interface circuit for use as the energy storage power interface in a DC microgrid.  A power controller that supports the devised bidirectional power circuit will also be investigated.
Duties and Responsibilities: – investigate state-of-the-art GaN based power topologies, develop GaN AC/DC bidirectional converter
– perform power electronic circuits simulation in Powersim
– analysis in MATLAB
– devise controller in Powersim/DSP
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Strong math and circuit analysis background, good presentation skills
Desired Course(s): electronics, electric circuits, power electronics, analog electronics
Other Desired Qualifications: Hardware development experience
Contact Info: Prof. John Lam (johnlam@eecs.yorku.ca)



Assurance of systematic mapping studies in software engineering
Professor: Alvine BOAYE BELLE
Lab Website: https://lassonde.yorku.ca/users/alvinebelle
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: Systematic mapping studies are used to classify existing literature on a given research area in order to structure that area. They have become increasingly popular since reading them notably allows becoming more familiar with a research area. Steps to carry out a systematic mapping study typically consists in searching the literature that is relevant for a research area, selecting that literature using a set of criteria, and analyzing and reporting the data retrieved from the selected literature. However, to the best of our knowledge, no approach has been proposed to assure that a mapping study is systematic i.e. that it sufficiently covers all the features embodied by the guidelines proposed to carry out systematic mapping studies.
The main objective of this project is therefore to develop and assess assurance cases that demonstrate that a mapping study carried out in the software engineering field is systematic.
Duties and Responsibilities: 1. Survey existing measures used to assess systematic mapping studies carried out in software engineering and assess the limitations of these measures
2. Survey the different guidelines and/or steps that have been proposed to create systematic mapping studies.
1) Survey the different categories of evidence that can be used to assure that a mapping study is systematic
2) Rely on the surveyed evidence and guidelines to create and implement an assurance case arguing that a mapping study is systematic
3) Propose a new confidence measure that assesses the so-created assurance case to quantify the degree of systematicity of a mapping study carried out in the software engineering field.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Equivalent of 3rd year major computer science skills, math skills
Desired Course(s):  Disciplines: computer science
Other Desired Qualifications: Analytical mind, team player, excellent writing skills
Contact Info: Prof. Alvine BOAYE BELLE (alvine.belle@lassonde.yorku.ca )



In-memory Vector-Matrix Multiplication Hardware Programming for Machine Learning Applications
Professor: Amirali Amirsoleimani
Lab Website: https://lassonde.yorku.ca/users/amirsol
Position Type: NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: In the Lab for Computing Research and Innovation (LCRAIN), we are aiming to design efficient and versatile systems for the next generation of machine learning hardware. Our goal is to propose scalable, flexible, and innovative strategies for the implementation of the synaptic weight and the associated Multiply and Accumulate (MAC) operation, which are the most demanding resources for efficient Machine Learning (ML) hardware. We capitalize on emerging multi-technology platforms such as high-performance CMOS and high-density memory devices to introduce efficient solutions to existing techniques. In this specific project, we are aiming for a candidate to specifically on programming the FPGA for custom-designed PCB and chip to run vector-matrix multiplication on memory arrays. We are investigating novel writing and reading schemes on memory crossbars to implement more efficient operations for in-memory computing platforms.
Duties and Responsibilities: Here is the summary of the USRA student tasks:
1. Student will study different CMOS-RRAM architecture operation modes (READ, WRITE, …)
2. Student will develop models for different modes of operation on Python and the platform model.
3. Student will start implementing the datapath for the custom-designed PCB and chip on FPGA and testing different READ and Write techniques.
4. The final step of the implementation is testing and developing appropriate test benches for the system.
5. The findings on novel READ and WRITE schemes will be documented for the publuication.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: • Good Machine Learning Skills, acceptable hardware knowledge • Proficiency in Python, SPICE and HDL language like Verilog
Desired Course(s): The student should already be knowledgeable on the course e.g. Digital logic design, Electrical circuits, Electronics1&2, Machine learning, Computer Organization.
Other Desired Qualifications: The student should be passionate about working on a challenging project with hybrid technologies, and also should be interested on AI hardware. Prior research experience on AI hardware will be an asset.
Contact Info: Prof. Amirali Amirsoleimani (amirsol@yorku.ca)



HUMAN-COMPUTER INTERACTION IN VIRTUAL REALITY
Professor: Robert Allison
Lab Website: https://percept.eecs.yorku.ca/
Position Type: NSERC Undergraduate Student Research Award (USRA);Lassonde Undergraduate Research Award (LURA);
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 apparatus to study human perception in computer-mediated worlds including a new and unique fully immersive virtual environment display. The student would 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 would model 3D environments, render them in a 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
• Modeling and Data analysis
• Preparation of reports, graphics and presentations
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
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
Other Desired Qualifications: Students should be self-directed and work well in a team environment.
Contact Info: Prof. Robert Allison (allison@eecs.yorku.ca)










Defect Detection During Metal 3D printing Using Supervised Machine Learning (Python Programming) 
Professor: Solomon Boakye-Yiadom
Lab Website: https://www.pspp-of-materials.com/
Position Type: NSERC Undergraduate Student Research Award (USRA);Lassonde Undergraduate Research Award (LURA)
Open Positions: 2
Project Description: Some of the major advantages of using Metal-based additive manufacturing (metal-AM) such as Laser-Powder-Bed Fusion (LPBF) techniques include its ability to create parts with complex geometric shapes, the flexibility of design and lightweight structures. However, gaps in performance metrics, standards to improve the accuracy of AM parts, and a lack of consensus on the properties of fabricated parts have prevented the widespread application of metal-AM for direct part production. Produced parts and components have many ensuing defects and discontinuities, including rough surface finishes, high residual stresses, porosities, cracks, texture and elemental enrichment/depletion. These defects are formed due to the laser metal-powder-bed interactions which include spatter/splatter, repeated thermal cycles, large temperature gradients and relatively high cooling rates during solidification. Recently, there have been large investments in the use of Artificial Intelligence (AI) and Machine Learning (ML) to expedite the discovery and prevention of defects in parts and structures during metal-AM. However, fundamental data from in-situ process monitoring are required in training and developing these AI/ML algorithms to avoid defects and flaws in components and establish relationships between process parameters and part quality during 3D printing. The extensive lack of fundamental data needed for developing sophisticated AI/ML algorithms has prevented realizing this goal. It has been hypothesized that a critical in-situ process monitoring of the interaction of the laser with the metal powder bed including processing mechanisms that result in induced defects can provide invaluable data that can be used to develop sophisticated AI/ML algorithms. These AI/ML algorithms can be used to detect and prevent defects/flaws in components during printing including establishing better relationships between process parameters and part quality. The main goal of this study is to monitor and track in-situ the melt pool morphological evolution, spatter/splatter behavior, unmelted/over melted regions during rapid processing of parts including how multi-elemental combinations and associated compositions affect defect formation to develop advanced machine learning algorithms that can predict defect generation during metal additive manufacturing.
Duties and Responsibilities: The student’s task is to help in developing a neural network model using Python and datasets, from a high-speed camera and a photodiode. The responsibilities are as follows:
1. Review supervised ML models used in-situ monitoring techniques with data from high-speed and photodiodes
2. Pre-processing of data from high-speed camera and photodiodes in the appropriate format for an Artificial Neural network model
3. Build/Repurpose an existing ANN model to accept pre-processed data from camera and diode for defect classification
4. Train ANN model using data in the literature (Database will be provided)
Work Setting: Project work is carried out remotely
Desired Technical Skills: Programming (Python preferred) , Metal 3D printing, Use of High Speed Cameras
Desired Course(s): Software Engineering, Mechanical, Electrical Engineering, Computer Engineering, Earth and Space Science
Other Desired Qualifications: Interests in Machine Learning and Artificial Intelligence
Contact Info: Prof. Solomon Boakye-Yiadom (sboakyey@yorku.ca)



AI-enhanced Control of collaborative robotic manipulator
Professor: George Zhu
Lab Website: www.yorku.ca/gzhu
Position Type: NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: This project is to install a Kuka collaborative robotic manipulator and develop control software to capture a target in a clustered environment with computer-vision and artificial intelligence without collision with obstacles in the enviornment.
Duties and Responsibilities: The student will (i) setup the robotic to working condition (install the robot to the base table, connect to electrical power and control box, connect computer to control box to control the robot), (ii) develop software to control the robot motion and path planning, (iii) integrate computer vision software and 3D camera into control software for obstacle avoidance in path planning. The student is expected to work with PhD students.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Electronic hardware, mechanical hardware, Matlab programming (must), Labview (optional), control basics
Desired Course(s): Mechanical, space, geomatics and computer engineering students are welcome
Other Desired Qualifications: Some lab experiences
Contact Info: Prof. George Zhu (gzhu@yorku.ca)



AI-Powered Autonomous Robotics for COVID-19 Disinfection
Professor: George Zhu
Lab Website: www.yorku.ca/gzhu
Position Type: NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: Ultraviolet-C (UV-C) light is known to be effective for surface disinfection against pathogens such as COVID-19. This project is to test a prototype of fully autonomous, AI-driven robot to deliver UV-C LED light to areas hard to reach by normal robotic system. The prototype will include a 6-DOF robotic manipulator with a UV-C LED light source at the end. It will autonomously detect the hidden areas and disinfect the areas by moving the UV-C light with collision with surrounding objectives.
Duties and Responsibilities:
(i) setup a commercial mini robotic manipulator with an end-effector to working condition (mount the robot to desktop, connect the robot to a computer and test the demo version of control software). (ii) integrate computer vision system (camera and software) with AI for the end-effector to reach desired areas without collision with  objects.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Robotics, Simulation, Matlab/Labview programming, electronic and mechanical hardware, experiments.
Desired Course(s): all engineering students
Other Desired Qualifications: Previous RA experiences or hands-on experiences are desired.
Contact Info: Prof. George Zhu (gzhu@yorku.ca)



Microfluidic Technologies for Health and Safety
Professor: Pouya Rezai
Lab Website: https://acute.apps01.yorku.ca/
Position Type: NSERC Undergraduate Student Research Award (USRA);
Open Positions: 2
Project Description: We develop miniaturized fluidic devices to test multi-phase fluids and detect analytes of interest in them. Examples include detecting bacteria in the food, viruses in the air, and microplastics in the water, all at the site of sample acquisition (Point of Need Detection). We also use very small biological model organisms of human disease and develop lab-on-a-chip devices for testing their cell-to-behaviour processes in response to various stimuli from chemicals to electrical signals. These technologies help resolve health and safety challenges in the water, food, and environment sectors.
We have projects available for 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 BRG). They should also meet with Dr. Rezai weekly and report on progress and plan. 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, nematodes, flies, and fishes 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. As 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 Oxford and Cornell for graduate studies.
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Fluid Mechanics, Materials, Basic Biology
Desired Course(s): Mechanical Engineering, Electrical Engineering, Biology, Biophysics, Chemistry
Other Desired Qualifications: Hard working; Research mindset; Teamwork
Contact Info: Prof. Pouya Rezai (prezai@yorku.ca)



COVID-19 Aerosol Transmission and HVAC Design
Professor: Prof. Marina Freire-Gormaly
Lab Website: https://freire-gormaly.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: The goal is to develop a practical tool to minimize computational time for CFD of aerosol transmission modeling.
Duties and Responsibilities: Create a computational tool to minimize the computational time for CFD models
Work Setting: Project work is carried out remotely
Desired Technical Skills: Experience in coding in Python, Matlab, or Java is an asset. Understanding of CFD modeling also considered an asset
Desired Course(s): Any undergraduate program at York University
Other Desired Qualifications: Willingness to learn, to be a team player, to be enthusiastic, hardworking, energetic, curious to learn.
Contact Info: Prof. Prof. Marina Freire-Gormaly (marina.freire-gormaly@lassonde.yorku.ca)



Development of an Apparatus for the Thermal and Electrical Characterization of Thermal Interface Materials
Professor: Roger Kempers
Lab Website:
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: The objective of this project is to re-design, develop and construct an extremely accurate apparatus used for the mechanical, thermal and electrical characterization of thermal interface materials and other conductive materials.
This will involve the re-design and calibration of critical components, apparatus assembly and control, setup of data acquisition hardware, the development of a temperature-controlled water-cooling loop, and the development of IR thermal instrumentation.  The work will culminate with the testing of a new kind of metal-based thermal interface material. 
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
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Good working knowledge of Mechanical Engineering and hands-on ability; MATLAB  Ability to fabricate and test components Experimental data collection and analysis
Desired Course(s): Mechanical Engineering
Other Desired Qualifications: Good verbal, written and presentation communication skills Able to self-motivate and work well with limited direction
Contact Info: Prof. Roger Kempers (kempers@yorku.ca)



Modelling and Characterization of Additively Manufactured Heat Exchangers

Professor: Roger Kempers
Lab Website:
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: The objective of this project will be to design, fabricate and experimentally characterize the thermal performance of novel AM heat exchangers for applications ranging including EV battery thermal management, electronics cooling and waste heat recovery.
They will develop technical drawings, perform engineering design calculations and simulations, fabricate heat exchanger components, and characterize and assess the performance of these heat exchangers under single and two-phase operation. 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
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Good working knowledge of Mechanical Engineering and hands-on ability; MATLAB Ability to fabricate and test components Experimental data collection and analysis
Desired Course(s): Mechanical Engineering
Other Desired Qualifications: Good verbal, written and presentation communication skills Able to self-motivate and work well with limited direction
Contact Info: Prof. Roger Kempers (kempers@yorku.ca)



Characterization and Analysis of Automotive Composites
Professor: Reza Rizvi
Lab Website: https://pixel.lab.yorku.ca/york/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description: Improved fuel economy and sustainability goals are pushing the drive towards light-weighting today’s automotive. Typical steels and Aluminum (Al) alloys are increasingly being replaced by automotive composites driven by light-weighting trends. The mechanical properties (strength, creep, fatigue) of legacy materials such as steel and Al alloys are well-understood and predictable within the industry. However, the use of composite materials within the automotive design process introduces two  significant challenges. The first is that by their nature, use of composites introduces a wide range of composition design space. The second is that polymers by their nature, are highly sensitive to processing, environmental, and testing conditions. These two challenges combined brings about an almost endless possibilities of structure-property-process relationships that could require an endless testing program and hence an endless design cycle. As part of a broader effort with an automotive partner to reduce the design cycle, this project will seek to characterize the strength, creep and fatigue of certain automotive composites and analyze these within the a broader context of supplier provided composite materials data. The ultimate goal of the broader effort will be to feed these results in a Machine Learning based classification routine that is capable of predicting the properties (stress-strain, creep behavior relations) for a broad range of compositions, processing, environmental and testing conditions.
Duties and Responsibilities: Engineering Design, CAD, Materials and Component Procurement, Fabrication and Testing, Mechanical Characterization (Static, & Creep), Data Collection and Analysis.
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Good working knowledge of mechanical engineering principles, Good verbal, written and presentation skills, Must be hands-on person, Some programming (python preferred) skills or willing to learn.
Desired Course(s): Mechanical Engineering, Civil and Space Engineering might be suitable also
Other Desired Qualifications: N/A
Contact Info: Prof. Reza Rizvi (rrizvi@yorku.ca)



Development of Autonomous Mobile  3D Bioprinting System for Regenerative Medicine

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: The main responsibility includes supporting the development of robotic bio printer at IDEA-LAB York University.
The robotic arm is equipped with filament extrusion and 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.
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 candidate.
Labview and 3D printing experience will come in handy.
Duties and Responsibilities: Supporting the development of robotic bio printer at IDEA-LAB York University
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Mechatronics, Material characterization
Desired Course(s): Mechanical / Mechatronics as well as Solid Mechanics
Other Desired Qualifications: Mechanical / Mechatronics as well as Solid Mechanics
Contact Info: Prof. Alex Czekanski (alex.czekanski@lassonde.yorku.ca)



Development of a 4D Printed Hydorgel for Tissue Engineering
Professor: Prof. 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, 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 proprieties 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.
Design a 4D geometry that responds in a manipulatable way to a given thermal stimulus.
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Material Science and Engineering, Advanced Manufacturing
Desired Course(s): Mechanical Engineering / Solid Mechanics Laboraory
Other Desired Qualifications: Material Science and Engineering, Advanced Manufacturing
Contact Info: Prof. Prof. Czekanski (alex.czekanski@lassonde,yorku.ca)



Droplets on Levitated Membranes
Professor: Alidad Amirfazli
Lab Website: https://amirfazli.apps01.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA);
Open Positions: 1
Project Description:     An object can be levitated in the air against gravity using acoustic pressure. This technique is called “acoustic levitation”. You can use this technique to levitate some small objects such as a foam ball, an ant or a droplet. As an early work for this project, we have achieved the levitation of membranes. We wish to exploit this technique to study the droplet behavior on levitated membranes. The premise of this research is that the surface tension of a droplet can deform a soft membrane (e.g., a tissue paper) as the droplet is placed on the membrane. However, such surface tension-induced deformation of a levitated membranes in the air remains unknown, so we wish to explore this with you.
    In this undergraduate research project, you can have opportunities to get hands-on experience in fabricating smart materials, building an acoustic levitator, high-speed imaging, and other interesting techniques. Furthermore, you can get strong support from graduate students and the professor for your research. In this way, you can not only learn important research skills, but also build a competitive CV which is extremely important for the job searching and/or postgraduate education.
    The first task is conducting a complete literature review of acoustic levitation. Then, students need to purchase parts and build a new acoustic levitator with graduate students. Finally, students can conduct experimental studies of the droplet behavior on levitated membranes.
Duties and Responsibilities: Student tasks and responsibilities:
– Literature review of acoustic levitation
– Building a new acoustic levitator
– Levitating soft membranes stably on the levitator
– Attending individual meetings as often as schedule permits
– Discussing analysis methods
– Discussing and interpreting analyzed data
– Presenting results in one lab meeting
Work Setting: Project work must be carried out on campus (as permitted by COVID-19 regulations)
Desired Technical Skills: Execution of research projects, Writing scientific papers
Desired Course(s):
No special requirements
Other Desired Qualifications: Experience in Arduino or similar products
Contact Info: Prof. Alidad Amirfazli (alidad2@yorku.ca)



Carbon Trends: There’s Plenty of Room at the Bottom

Professor: Cuiying Jian
Lab Website: https://jian.info.yorku.ca
Position Type: NSERC Undergraduate Student Research Award (USRA);Lassonde Undergraduate Research Award (LURA);
Open Positions: 2
Project Description: The following is quoted from Richard Feynman’s talk on December 29, 1959 at Caltech: “But I am not afraid to consider the final question as to whether, ultimately – in the great future – we can arrange the atoms the way we want; the very atoms, all the way down! What would happen if we could arrange the atoms one by one the way we want them (within reason, of course; you can’t put them so that they are chemically unstable, for example).”
In my group, the question often being asked is whether it is possible to create/design mechanically robust materials by a bottom-up approach. Such a bottom-up approach would involve manipulating atoms/molecules at the nanometer/angstrom scales. From the experimental side, microscopies are tools that are often used to achieve these small scales, by addressing the challenge of finding appropriate “fingers” to arrange atoms/molecules. The story seems to be much simpler from the computational side, where the positions of atoms/molecules are reflected by their coordinates. However, when a structure/material is proposed, we need to assess its stability based on compositions and geometries, before recommending it for manufacturing. In this context, there are diverse parameters that can be explored in the design space to tune materials properties for applications in renewable energy, desalination, automotive, electronics, etc. For us, we are particularly interested in creating/designing materials/structures, based on earth-abundant carbon element, for wastewater management and automotive industries. Of course, graphene may immediately come to our minds due to its superior properties (but what will happen if we replace certain carbon atoms with nitrogen atoms?). To enable a high-throughput screening of design parameters, a multiscale model in conjunction with machine learning is being developed in my group. Along with those lines, we pair with manufacturing sectors to produce hierarchy structures from the bottom.
Duties and Responsibilities: The undergraduate researchers are expected to: 1) perform literature reviews in relevant areas (wastewater or automotive), 2) have preliminary knowledge of modellings/computations, 3) setup a toy simulation, subsequently run the simulation, and process output data to evaluate the stabilities and properties of the designed materials/structures. Furthermore, the students will participate in disseminating our research outcomes through conference presentations and manuscript writings.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Data processing skills are preferred but not mandatory.
Desired Course(s): Students enrolled in engineering, physics, or chemistry are all welcome.
Other Desired Qualifications: Nothing but please be prepared to read/learn a lot.
Contact Info: Prof. Cuiying Jian  (cuiying.jian@ lassonde.yorku.ca )








Micromobility
Professor: Andrew Maxwell
Lab Website: www.bestlassonde.ca
Position Type: Lassonde Undergraduate Research Award (LURA);
Open Positions: 6
Project Description: Funded by Frank Stronach we are developing a living laboratory on campus to develop test and adapt the next generation of micro-mobility SARIT electric vehicles to be manufactured in Ontario in 2023.  This is directly linked to UN sustainability initiatives at York.
Duties and Responsibilities: There will be a range of projects on this initiative in the following areas: autonomous vehicles, usability, improved power/braking performance, improved battery management and customized vehicle applications.
Work Setting: Project work is carried out   on campus, but can be shifted to remote work if necessary
Desired Technical Skills: Sound mechanical, electrical, software or design skills.
Desired Course(s): None.
Other Desired Qualifications: Likely interested in pursuing BEST certificate
Contact Info:
Prof. Andrew Maxwell (andrew.maxwell@lassonde.yorku.ca)



Developing Hackathons around UN Sustainability
Professor: Andrew Maxwell
Lab Website: www.bestlassonde.ca
Position Type: Lassonde Undergraduate Research Award (LURA);
Open Positions: 3
Project Description: Developing a next generation of online resources for hackathons, courses and programs, that follow a Design Thinking approach, build on the success of UNHack and SHAD, and can be incorporated into new courses at Lassonde, across York and through global partners. The whole process is framed around addressing UN SDG challenges and empowering students to develop their innovation and creativity skills. Please note that participants in our programs can address any of the UN SDGs so all are essentially applicable, however the development of the framework and tool is mode linked to some, which are highlighted.
Duties and Responsibilities: Students (upto three) can work on a variety of projects from testing and evaluating current tools in a live workshop setting, to collaborating with international partners to access and embed a range of world-class tools. We are expecting this project will receive additional external funding before the summer.
Work Setting: Project work is carried out remotely
Desired Technical Skills: Background in design thinking and familiarity with software tools (such as Miro, Discord, zoom, powerpoint).  Video editing capability would be an asset
Desired Course(s): None.
Other Desired Qualifications: Preference given to students planning to complete BEST Certificate.
Contact Info: Prof. Andrew Maxwell (andrew.maxwell@lassonde.yorku.ca)



Creating the Global Classroom: Enhancing the commercialization of University Research
Professor: Andrew Maxwell
Lab Website: www.bestlassonde.ca
Position Type: Lassonde Undergraduate Research Award (LURA);
Open positions: 3
Project Description: Based on the success of my graduate course in technology commercialization, and the impact it is already having in translating technical research into market adoption (especially around UN sustainability challenges) we have been asked to expand it into a global classroom (in line with York International mandate). 
Duties and Responsibilities: We are looking for three students to develop tools, resources, bots, programs and other resources that can allow the development of a global classroom in ten universities around the world in January 2023.
Work Setting: Project work is carried out remotely
Desired Technical Skills: Good written and verbal communication skills, understanding of design thinking,
Desired Course(s): Preference will be given to students who have already completed relevant design courses and ENTR courses.
Other Desired Qualifications: Interest in increasing the success rate of commercializing technology from university research, and excited to share perspectives from multiple disciplines and campuses.
Contact Info: Prof. Andrew Maxwell (andrew.maxwell@lassonde.yorku.ca)