Electrical Engineering & Computer Science Project Postings

Monitoring and Predictive Tools to Enhance Resilience of Power Systems Against Extreme Weather 
Professor: Afshin Rezaei-Zare  
Contact Info: rezaei@yorku.ca  
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)  
Open Positions: 3 
Project Description: This research project focuses on developing advanced monitoring and predictive tools to enhance the resilience of power systems against natural disasters, including solar storms, wildfires, and ice storms. These events pose significant risks to grid stability and electric vehicle infrastructure, leading to potential power disruptions and mobility constraints. The project is in line with the NSERC CREATE GMD-MSTI program. 
The project aims to design an interactive user interface that integrates real-time data from power system sensors, weather monitoring services, and disaster tracking sources. This system will provide grid operators with actionable insights by visualizing power grid health, transformer conditions, and network stability. Predictive analytics will be incorporated to assess vulnerabilities and forecast potential disruptions, enabling proactive decision-making. 
Students will develop an intuitive UI/UX framework for seamless data visualization and analysis. The system’s effectiveness will be tested using historical disaster data and simulations, ensuring its capability to improve situational awareness and disaster preparedness. This interdisciplinary project will bridge power systems engineering, data science, and software development to support resilient energy and mobility infrastructure. 
Duties and Responsibilities: 
Students will be responsible for designing and implementing a real-time monitoring dashboard that integrates data from power grids and environmental monitoring sources. They will develop predictive analytics features to assess vulnerabilities and provide early warnings. Tasks include UI/UX development, real-time data integration, predictive modeling, and system validation through simulations. The team will also document their work and prepare a final report summarizing findings and recommendations. 
Desired Technical Skills: 
This project offers an opportunity to work on an interdisciplinary challenge that bridges power systems, electrified transportation, and disaster resilience through innovative monitoring solutions. 
Mandatory skills: at least two of the following: 
• Software Development & UI/UX: Proficiency in building desktop or cross-platform GUIs using C/C++ (e.g., Qt, wxWidgets, Dear ImGui) and/or Python (e.g., PyQt/PySide, Tkinter), along with solid UI/UX design principles for interactive dashboards. 
• Power Systems Knowledge: Basic understanding of electrical circuits, power grids, and renewable energy resources. 
• Programming: Strong knowledge of C/C++ and Python for implementing monitoring logic, data handling pipelines, and power-system analytics (including integration of algorithms with the GUI). 
Additional Skills:  
In addition to mandatory skills, priority will be given to candidates with any of the following: 
• Data Visualization (Python/C++ ecosystem): Experience with visualization libraries/tools such as Plotly (Python), Bokeh, PyQtGraph, or equivalent visualization approaches suitable for real-time analytics and interactive plots. 
• Real-Time Data Integration & APIs: Familiarity with consuming and processing real-time data feeds and APIs (e.g., REST/JSON, WebSockets) for weather services, grid infrastructure telemetry, and disaster tracking sources. 
• Machine Learning (Python): Interest in applying AI/ML techniques for predictive modeling of system resilience (e.g., scikit-learn, PyTorch, or similar), including basic feature engineering and model evaluation. 
Desired Course(s): Any course(s) related to Electrical Circuits, C/C++ and Python Programming, Software Development, Introduction to Power Systems, or Machine Learning. 

An LLM-driven Multi-Agent Framework For the Automatic Generation of autonomous Driving Systems Safety Cases
Professor: Alvine Boaye Belle 
Contact Info: alvine.belle@lassonde.yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); 
Open Positions: 2 
Project Description:This project consists of designing, implementing, and testing a novel multi-agent LLM framework to support the generation of the safety cases of autonomous driving systems.
Duties and Responsibilities: 
• Read the literature to become familiar with Agentic AI
• Read the literature to become familiar with the patterns that allow creating the architecture of LLM-based multi-agent systems
• Use suitable patterns to design the architecture of an LLM-driven Multi-Agent Framework ideal for the automatic creation of assurance cases Assess the framework by relying on 2-3 case studies focusing on the safety of ML-enabled autonomous driving systems
• Help write a research paper reporting the findings of the project.
Desired Technical Skills:
• Good oral and written skills
• Excellent programming skills (proficiency in Python is a must)
• Basic knowledge of LLMs and/or LLM-based agents
• Excellent knowledge of architectural patterns and design patterns
Desired Course(s): -Must have completed the EECS 3311 and EECS 4413 courses
Other Desired Qualifications: Be dynamic, proactive and team-oriented

On the Use of Vibe Modeling to Expedite the Verification of Machine Learning–Enabled Autonomous Driving Systems 
Professor: Alvine Boaye Belle  
Contact Info: alvine.belle@lassonde.yorku.ca  
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA);  
Open Positions: 1 
Project Description:  This project focuses on the concept of vibe modeling, a novel approach to integrate the best of both worlds (AI and Model-Driven Engineering) to speed up the development and verification of safe, secure and reliable complex systems.  
Duties and Responsibilities: 
• Explore the scientific literature to become familiar with vibe modeling, vibe coding, and LLM-based technologies in general
• Explore the scientific literature to become familiar with assurance cases
• Design a tool that supports vibe modeling
• Develop a tool that supports vibe modeling
• Work on several case studies focusing on ML-enabled autonomous driving systems to explore the practical use of the tool in the context of assurance cases
• Write a research paper to report the findings of the project.
Desired Technical Skills:  
• Good knowledge of web frameworks  
• Good knowledge of LLM-based technologies 
• Excellent programming skills 
Desired Course(s): -The student must have completed an undergraduate course focusing on the creation of web-based systems (e.g., EECS 4413 course) and an undergraduate course focusing on the design of software systems (e.g., the EECS 3311 course). 
Other Desired Qualifications: 
• Excellent writing skills
• Excellent oral skills
• Good interpersonal skills 

Jailbreak attacks against Vision Language Models
Professor: Alvine Boaye Belle  
Contact Info: alvine.belle@lassonde.yorku.ca  
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA);  
Open Positions: 1 
Project Description:  In the context of Vision Language models (VLMs), jailbreak attacks function as a red-teaming mechanism to circumvent safety guardrails and expose potential risks. A successful jailbreak enables attackers to bypass safety measures, allowing the model to generate content that poses significant safety and ethical risks. Most of the existing jailbreak methods mainly focus on the visual modality, manipulating only image inputs within the prompt to conduct attacks. However, such approaches are often ineffective against aligned models that jointly integrate visual and textual information during the generation process. To tackle this issue, this project will focus on developing a novel jailbreak method that simultaneously optimizes both visual and textual prompts to execute more effective jailbreak attacks.
Duties and Responsibilities: 
• Analyze the literature on vision language models 
• Analyze the literature on jailbreak attacks against vision language models 
• Develop a novel jailbreak method to effectively bypass the safety measures of vision language models 
• Run some experiments to assess the proposed method
• Compare the proposed method to existing methods. 
• Write a research paper reporting the key findings. 
Desired Technical Skills:  
Excellent programming skills 
Desired Course(s): 
• Have completed a software design course (e.g., EECS 3311)
• Have completed a software requirement course course
Other Desired Qualifications: 
• Excellent oral skills
• Excellent writing skills
• Excellent analytical skills
• Be team-oriented
• Be very creative 

Analyzing and Implementing timing predictable directory cache coherence mechanisms 
Professor: Anirudh Kaushik 
Contact Info: kaushika@yorku.ca 
Lab Website: https://anikau31.github.io/ 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open of Positions: 1 
Project Description: Cache coherence mechanism is a key component in today’s multi-core computing systems to facilitate correct and high-performance data communication and synchronization between multiple cores. For safety-critical cyber-physical systems (CPS) that use multi-core computing systems, it is imperative that these mechanisms behave in a timing predictable manner while maintaining their performance advantages. Timing predictability is concerned with the determination of an upper bound or worst-case bound on the execution time of a software application on the hardware compute system. Balancing timing predictability and high-performance is non-trivial and challenging as they are competing design goals.  
This project will explore the analysis and design of directory cache coherence mechanisms deployed in multi and many-core systems used in safety-critical CPS. The analysis will focus on understanding the trade-offs between timing predictability and high-performance, and implement novel hardware mechanisms using the insights from the analysis. The student will perform the analysis and design first using a well-established micro-architecture simulator implemented in C/C++, and then implement a proof-of-concept hardware prototype that can be synthesized on an FPGA. 
Duties & Responsibilities:– 
•  Research review of prior state-of-the-art approaches 
•  Implement state-of-the-art approaches in micro-architecture simulator 
•  Validation and evaluation of implementation 
•  Worst-case analysis of implementation through formal methods 
Desired Technical Skills 
•  Strong C/C++ coding 
•  Strong computer architecture background 
•  Ability to navigate large code base 
•  Strong Verilog/System-Verilog background 
•  Git, Linux 
Desired Course(s):  
•  Must complete a computer architecture course with exposure to multi-core systems.  
•  For YorkU students a minimum of B+ standing in EECS 4201 
•  Good standing in digital logic implementation course; for YorkU students EECS 3201/3215/3216 
Other Desired Qualifications: 
• Self-starter 
• Independent 

Designing and Evaluating Human-AI Interactive Applications 
Professor: Emily Kuang 
Contact Info: ekuang@yorku.ca 
Lab Website: https://emilykuang.github.io/ 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2 
Project Description:  
Human-AI interaction is becoming part of everyday life, from productivity tools to systems that support accessibility and inclusion. In this project, you will work on designing and building interactive applications that combine Human-Computer Interaction (HCI) and AI, with a focus on either: 
1) Accessibility and inclusion, such as supporting intergenerational communication with older adults, or 
2) Design and productivity, such as tools for qualitative analysis or usability testing. 
Duties and Responsibilities:  
Responsibilities may be tailored to your interests and strengths and may include: 
– Designing user experiences through ideation, wireframing, and prototyping 
– Developing interactive web-based applications 
– Planning, conducting, and analyzing user studies 
– Collecting and interpreting quantitative and qualitative data 
Desired Technical Skills:  
Experience in one or more of the following is a plus: 
– Creating wireframes, mockups, or design concepts (e.g., Figma) 
– Web development using JavaScript/TypeScript and frameworks such as React or Node.js 
– Deploying and managing web applications on cloud platforms (e.g., DigitalOcean or similar) 
– Using version control tools (e.g., Git/GitHub) 
– Conducting user studies and administering surveys 
– User data analysis (e.g., statistical significance testing, qualitative coding) 
Desired Course(s): Students who have taken (or are taking) the following courses are especially encouraged to apply: EECS 3461 – User Interfaces, EECS 4441 – Human-Computer Interaction, and EECS 4461 – Multimedia Technology 
Other Desired Qualifications: Strong organizational skills; Ability to work independently and collaboratively in a research team; Curiosity, creativity, and a problem-solving mindset 

  Contextual Modeling for Multilingual Large Language Models Using Knowledge Graph–Enhanced Retrieval 
Professor: Enas Altarawneh 
Contact Info: enaskt2@yorku.ca 
Lab Website: https://lassonde.yorku.ca/users/enaskt2 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2 
Project Description: Large Language Models (LLMs) have demonstrated impressive performance across many natural language processing tasks; however, their effectiveness in multilingual settings remains uneven, particularly for low-resource languages and culturally diverse contexts. One major limitation is the reliance on unstructured textual context, which can lead to hallucinations, loss of semantic consistency across languages, and poor factual grounding. 
This project investigates contextual modeling for multilingual LLMs using Knowledge Graphs (KGs) integrated into a Retrieval-Augmented Generation (RAG) framework. The central idea is to leverage structured, language-agnostic knowledge representations to improve contextual grounding and cross-lingual consistency in LLM outputs. 
The student will design and implement a prototype system that combines multilingual text retrieval with KG-based context extraction. Given a user query in one or more languages, the system will retrieve relevant documents and structured knowledge graph subgraphs, fuse these sources of context, and generate responses using an LLM. The project will evaluate whether KG-enhanced RAG improves factual accuracy, reduces hallucinations, and yields more consistent outputs across languages compared to standard multilingual RAG baselines. 
The project emphasizes hands-on experimentation, critical analysis of results, and engagement with current research literature. Outcomes include a working research prototype, a written technical report, and oral presentations. This project is suitable for a senior undergraduate computer science student with a background in machine learning and Python programming. 
Duties and Responsibilities:  
The student will be responsible for the following tasks: 
•  Conduct a structured literature review on multilingual LLMs, retrieval-augmented generation, and knowledge graph–based contextual modeling. 
•  Design a research plan and define evaluation criteria for comparing KG-enhanced RAG with standard RAG approaches. 
•  Implement a multilingual RAG pipeline using Python and modern NLP tools. 
•  Construct or adapt a multilingual knowledge graph and perform entity linking and subgraph retrieval. 
•  Integrate structured (knowledge graph) and unstructured (textual) context into LLM prompting. 
•  Run experiments across selected languages and analyze quantitative and qualitative results. 
•  Document system design, experimental findings, and limitations in a formal research report. 
•  Present progress and final results in oral presentations. 
•  Meet regularly with the supervisor and incorporate feedback throughout the project. 
Desired Technical Skills:  
•  Python programming 
•  Basic machine learning concepts 
•  Introductory natural language processing knowledge 
•  Experience with data preprocessing and analysis 
•  Familiarity with probability and statistics 
•  Willingness to read and engage with research papers 
Desired Course(s): Applicants should be enrolled in a Computer Science, Computer Engineering, Software Engineering, or a closely related program. Completion of upper-year coursework in machine learning, data mining, or artificial intelligence is strongly preferred. 
Other Desired Qualifications:  
•  Strong analytical and problem-solving skills 
•  Interest in multilingual or cross-cultural AI systems 
•  Ability to work independently on an open-ended research problem 
•  Good written and oral communication skills 
•  Prior exposure to research projects is an asset but not required 

Lightweight Transformer via Graph Algorithm Unrolling
Professor: Gene Cheung
Contact Info: genec@yorku.ca
Lab Website: https://www.eecs.yorku.ca/~genec/index.html
Position Type: NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: Conventional “black-box” off-the-shelf deep learning models like Convolutional Neural Nets (CNNs) and transformers are huge in size and require large labeled datasets for training. Instead, we propose lightweight interpretable neural nets unrolled from graph-based algorithms that iteratively minimize well-defined mathematical objectives, resulting in drastic reduction in parameter counts and inference complexity. Applications include image restoration, traffic prediction, and EEG signal classification.
Duties and Responsibilities: 
A undergrad student will assist graduate students in pre-processing data for model training, training models for different setups, as well as evaluating different models under different experimental conditions.
Desired Technical Skills:  
Proficiency in Python programming, familiarity in operation systems (Windows and Linux), basic mathematical skills (signal processing theories, linear algebra, convex optimization).
Desired Course(s): 
Signals and Systems, Linear Algebra
Other Desired Qualifications: 
Ability to learn quickly in different disciplines. Ability to work in groups.

Security Engineering with Multi Agent Systems 
Professor: Gias Uddin 
Contact Info: guddin@yorku.ca 
Lab Website: https://giasuddin.ca/ 
Position Type: NSERC Undergraduate Student Research Award (USRA);Lassonde Undergraduate Research Award (LURA) 
Open Positions: 1 
Project Description: Large Language Models (LLMs) have ushered in the area of multi-agent systems (MAS), where autonomous agents work with LLMs to complete a task. While the use of MAS is promising, it is relatively unknown whether and how such multi-agent systems could be used for security engineering tasks, such as code hardening, security engineering virtual pair assistants, to ensure security and privacy in critical domains like healthcare and finance, etc. This project aims at studying the use of MAS to support securiting engineering tasks. We have the following objectives: (1) study the literature to learn how MAS is currently used for security engineering tasks (2) develop an extension of an existing MAS to improve a task for a given domain, such as to make source code more secure. 
Duties and Responsibilities: The student will contribute to the following two tasks: (1) study the literature to learn how MAS is currently used for security engineering tasks (2) develop an extension of an existing MAS to improve a task for a given domain, such as to make source code more secure. 
Desired Technical Skills:  
1. Strong coding background in languages like Python, Rust, and C/C++. 
2. Preliminary knowledge on AI/ML concepts and working knowledge on training and tuning ML/LM models. 
Desired Course(s): EECS 3401 (Introduction to AI and Logic Programming), EECS 3461 (User Interfaces) 
Other Desired Qualifications: N/A 

LLM Agents for Intelligent Facility Energy Optimization  
Professor: Hany Farag 
Contact Info: hefarag@yorku.ca  
Lab Website: https://smartgrid.eecs.yorku.ca  
Position Type: NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2  
Project Description: The increasing deployment of Distributed Energy Resources (DERs) such as solar PV, batteries, and electric vehicle charging loads is making facility-level energy management significantly more complex. Advanced optimization models have proven highly effective for scheduling DERs and participating in demand response; however, these tools remain inaccessible to non-expert operators due to their technical interfaces and lack of intuitive interaction. Recent advances in Large Language Models (LLMs) present an opportunity to bridge this usability gap by enabling natural-language interaction with optimization systems, thereby improving situational awareness, operator trust, and decision transparency. 
This project aims to prototype an LLM-driven assistant for local facility energy management. The assistant will allow users (e.g., operators, energy managers, researchers) to query system states, interpret optimization decisions, explore “what-if” scenarios, and modify scheduling objectives using natural language. The system will interface with existing in-house optimization frameworks developed within the Smart Grid Research Lab. The summer research team will focus on constructing a modular architecture that integrates LLM reasoning, tool-calling, and structured data exchange with energy management models. 
The primary research questions include: (1) how LLM agents can accurately interpret user intent and map it into optimization queries; (2) how to return results in clear and explainable terms; and (3) how to ensure safe operation by enforcing hard physical and operational constraints. Outcomes will include a functional prototype, demonstration use cases, and documentation for future research extensions in Responsible and Explainable AI for EMS. 
Duties and Responsibilities:  
The student researchers will: 
•  Develop a functional natural-language interface for interacting with existing EMS optimization models. 
•  Explore prompting, tool-calling, and API-based integration strategies for LLM-based agents. 
• Design structured intermediate representations for energy management queries and results. 
•  Implement modules for system state queries (e.g., load, costs, BESS SOC, DER availability). 
•  Implement modules for optimization tasks (e.g., day-ahead scheduling, scenario analysis, demand response).  
•  Format outputs into human-readable summaries, schedules, and visualizations. 
Assist in building demonstration scenarios and test cases for validation. 
•  Document code, APIs, workflows, and use cases to support continuity in the research program. 
(Optional) Investigate explainability mechanisms for optimization decisions. 
•  Participate in weekly research meetings and present progress updates. 
Desired Technical Skills: Programming experience (Python required) 
•  Familiarity with API design and modular software development 
•  Exposure to LLM tools, NLP, or generative AI (preferred but can be learned) 
•  Experience with scientific computing libraries (NumPy, Pandas) 
•  Experience with data exchange formats (JSON, CSV) 
(Preferred) Visualization tools (Plotly, Matplotlib, Dash, or equivalent) 
Desired Course(s):  
Students from the following disciplines are suitable: 
• Electrical or Computer Engineering 
• Software Engineering 
• Computer Science 
• Mechanical Engineering with energy systems interest 
• Mechatronics Engineering 
• Upper-year standing (3rd or 4th year preferred) is ideal for readiness. 
Other Desired Qualifications:  
•  Interest in energy systems, optimization, smart grids, or electrification technologies •  •  •  Willingness to learn interdisciplinary material (energy + AI + software) 
•  Strong communication skills for articulating technical concepts 
•  Ability to work independently and in a research team environment 
•  x“Curiosity about research and potential continuation through thesis or graduate studies 

Intelligent Wireless Spectrum Monitoring, Forecasting, and Management 
Professor: Hina Tabassum 
Contact Info: hinat@yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2 
Project Description: Modern wireless systems increasingly operate in crowded, dynamic, and contested spectrum environments, where poor awareness of future spectrum usage can lead to interference, service degradation, or complete communication failure. Spectrum prediction enables networks to anticipate spectrum availability rather than react to interference after it occurs, making it a critical capability for intelligent spectrum access and resilient wireless operation. 
This project will develop a framework for accurate and trustworthy spectrum prediction that supports proactive spectrum management across heterogeneous wireless services. The research will explore learning-based approaches to forecast spectrum usage and high-level metrics such as Spectrum Occupancy Ratio (SOR), with an emphasis on adaptability across environments and operating conditions. A key focus is trustworthy prediction—providing not only point forecasts but also reliable measures of uncertainty. By incorporating distribution-free uncertainty quantification, the proposed framework enables risk-aware spectrum access decisions, allowing systems to trade off performance and reliability based on confidence in predictions. 
The relevance of this work extends beyond commercial networks to wireless defense applications, where communication systems must operate reliably in contested or adversarial spectral environments. In such settings, predictive and uncertainty-aware spectrum awareness can support resilient tactical communications, interference avoidance, and informed electromagnetic spectrum operations. 
Overall, this research aims to advance spectrum prediction from a performance-driven task to a reliable decision-support capability, enabling robust, proactive, and mission-ready wireless systems. 
Duties and Responsibilities:  
This project is ideal for undergraduate computer science students interested in:  
•   Deep learning and foundation models 
•   Signal processing and wireless communications 
•  Real-world AI systems with impact on future networks 
Students will gain hands-on experience working with large-scale data, modern neural architectures, and real-world AI challenges, making this project a strong foundation for research careers, graduate studies, or advanced industry roles in AI and wireless systems. 
Desired Technical Skills:  
•   Basic Python programming Interest in machine learning or data analysis 
•   Familiarity with signals, wireless systems, or time-series data (helpful but not required) 
•   Willingness to learn deep learning tools (e.g., PyTorch) 
•   Comfort with math fundamentals (linear algebra, probability) 
•  Ability to work independently and communicate clearly 
Desired Course(s): Machine learning courses, Communications networks, Digital Communications, 
Mobile Communications 
Other Desired Qualifications: Python 

AI-Enabled Multimodal Localization Using GNSS, WiFi, and 5G Signals 
Professor: Hina Tabassum 
Contact Info: hinat@yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: This undergraduate research project focuses on multimodal localization, aiming to accurately estimate user or device location by jointly leveraging satellite-based positioning (GNSS), WiFi, and 5G cellular signals, along with other complementary sensing sources when available (e.g., inertial sensors or maps). The project will explore how different localization techniques, such as time-based methods (e.g., time-of-arrival and time-difference-of-arrival), signal-strength–based methods (e.g., RSS fingerprinting), and angle-based methods—can be combined to overcome the limitations of individual technologies, especially in indoor, urban, or obstructed environments. A key component of the project is the use of artificial intelligence and machine learning to fuse heterogeneous measurements, learn robust location fingerprints, and adapt to dynamic environments where signal conditions change over time. Through hands-on experimentation and data analysis, students will gain practical experience in wireless signals, positioning techniques, and AI-driven data fusion, while contributing to next-generation localization solutions relevant to smart cities, autonomous systems, and future 5G/6G networks. 
Duties and Responsibilities: The student will participate in a research project focused on AI-enabled multimodal localization using signals from GNSS, WiFi, and 5G wireless systems. Under the supervision of the research team, the student will contribute to the development, evaluation, and validation of data-driven localization approaches. 
Key responsibilities include collecting, organizing, and preprocessing multimodal localization data, such as wireless signal measurements and location labels, and assisting with exploratory data analysis. The student will support the implementation of baseline localization techniques, including signal-strength–based methods (e.g., fingerprinting) and time-based positioning approaches, to understand the strengths and limitations of individual sensing modalities. 
The student will assist in designing and training machine learning models for localization and data fusion, using Python-based tools and libraries. This includes experimenting with feature representations, evaluating model performance, and comparing AI-based methods with traditional localization techniques. The student will also help integrate multiple data sources to improve localization accuracy and robustness, particularly in challenging indoor and urban environments 
Additional responsibilities include maintaining well-documented and reproducible code, participating in regular research meetings, and summarizing findings through short technical reports or presentations. The student may also contribute to preparing figures, results, or demonstrations for research dissemination. 
Throughout the project, the student will develop hands-on skills in wireless systems, localization techniques, and applied machine learning, while gaining experience in research problem-solving, teamwork, and technical communication. The project emphasizes learning and mentorship, and responsibilities will be scaled to the student’s background and experience. 
Desired Technical Skills: Basic Python programming and interest in machine learning / data analysis 
Curiosity about wireless signals, localization, or multimodal sensing, with willingness to learn new tools 
Desired Course(s):  
Applicants should be enrolled in an undergraduate degree program in Electrical Engineering, Computer Engineering, Computer Science, Software Engineering, Data •   •  • Science, or a closely related discipline. 
•  Relevant coursework includes (or will include) 
•  Machine Learning or Artificial Intelligence 
•  Introduction to Signal Processing 
•  Wireless Communications or Digital Communications 
•  Probability and Statistics 
•  Linear Algebra 
•  Data Structures and Algorithms 
• Python Programming or Scientific Computing 
Students with coursework or strong interest in time-series analysis, wireless systems, localization, or applied machine learning are especially encouraged to apply. Prior research experience is not required. 
Other Desired Qualifications: N/A 

Bluetooth Low Energy (BLE) Software Development for Cross-Platform Applications 
Professor: Hossein Kassiri 
Contact Info: kassiri@yorku.ca 
Lab Website: neuro-ic.com 
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2 
Project Description: 
This project focuses on developing software for a Bluetooth Low Energy (BLE)-based system used in our brain-implant research platform. The students will develop desktop applications (Windows/macOS) and mobile applications (Android & iOS) that communicate with a BLE device, support reliable connection/data transfer, and provide a clean and usable user interface. The work involves hands-on testing and debugging with real hardware and producing well-documented, maintainable code suitable for research prototypes and demos. Students will work closely and in direct interaction with a graduate student, under direct supervision of the professor. 
Duties and Responsibilities: 
The student will participate in weekly meetings (project meetings and/or broader lab meetings) and collaborate with the graduate student and team members to align software behavior with the BLE device/hardware behavior. The students will typically: 
• Design and implement BLE scanning, connection, and data transfer features within apps 
• Develop a modular, user-friendly UI for desktop and mobile interfaces 
• Test, debug, and improve BLE communication reliability on physical devices (Windows/macOS + Android/iOS) 
• Collaborate with team members to define data formats, device commands, and expected behaviors 
• Document code and contribute to project reports, posters, and/or paper drafts 
• Present progress regularly (e.g., bi-weekly) to the group 
Desired Technical Skills (Required): 
• Strong programming experience in one or more of: C# (.NET / .NET MAUI), Swift, Kotlin/Java, Flutter, React Native 
• Ability to build and debug applications that communicate with Bluetooth Low Energy (BLE) devices 
Desired Technical Skills (Nice to Have): 
• Git / version control experience 
• Links to past apps, GitHub repositories, or a software portfolio 
Desired Course(s): 
• Software Design 
• Object-Oriented Programming 
A grade of at least B+ is preferred in one or more of these courses. 
Other Desired Qualifications (Required): 
 • Strong problem-solving and debugging skills 
• Self-motivated and able to communicate progress clearly 

Ultrasonic Wireless Power & Data Communications (FPGA/Firmware Development 
Professor: Hossein Kassiri 
Contact Info: kassiri@yorku.ca 
Lab Website: neuro-ic.com 
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2 
Project Description: 
This project focuses on FPGA/firmware development for an ultrasonic (US)-based wireless power and data communication system used in our brain-implant research platform. The students will develop and test FPGA logic that controls ultrasonic pulser/driver boards and generates/receives timing and control signals needed for reliable operation. The work includes writing and debugging Verilog/VHDL code, validating behavior on real hardware, and using basic electronic test equipment to diagnose issues during bring-up. Students will work closely and in direct interaction with a graduate student, under the direct supervision of the professor. 
Duties and Responsibilities: 
The student will participate in weekly meetings (project meetings and/or broader lab meetings) and collaborate with the graduate student and team members to align FPGA/firmware behavior with the ultrasonic hardware. The students will typically: 
• Design and implement Verilog/VHDL modules for timing, control, and communication functions 
• Program and debug the FPGA on real hardware connected to ultrasonic pulser boards 
• Use lab equipment (e.g., oscilloscope and logic analyzer) to debug issues that may be in firmware or hardware signaling 
• Document code and experiments and contribute to project reports, posters, and/or paper drafts 
• Present progress regularly (e.g., bi-weekly) to the group 
Desired Technical Skills (Required): 
• Strong Verilog or VHDL coding ability 
• Hands-on FPGA programming and debugging experience 
Desired Technical Skills (Nice to Have): 
• Comfort working in an electronics test lab (oscilloscope/logic analyzer) 
Desired Course(s): 
Digital Logic Design 
Electronic Circuits and Devices 
A grade of at least B+ is preferred in one or more of these courses. 
Other Desired Qualifications (Required): 
• Strong debugging mindset and willingness to learn through hands-on testing 
• Self-motivated and able to communicate progress clearly 

Trade-Offs in Explainability Methods for LLMs 
Professor: Ines Arous 
Contact Info: inesar@yorku.ca 
Lab Website: https://lassonde.yorku.ca/users/inesar 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2 
Project Description: Large Language Models (LLMs) generate answers that are often hard for users to interpret or trust, particularly in high-stakes domains such as medical diagnosis or legal decision-making, where explainability is critical. To meet this need, current LLMs generate natural language explanations or reasoning traces that appear plausible and convincing to users. For instance, when a user asks an LLM about potential problems associated with finger numbness, the LLM might respond: “Finger numbness could be an early sign of a stroke,” followed by a detailed explanation. Such an answer might lead users to panic, especially if the explanation is based on unrelated or loosely associated facts. This highlights a critical issue: it is often unclear whether the LLM’s justifications are faithful—that is, whether they reflect the internal reasoning behind the LLM’s prediction, rather than being generated post-hoc to appear plausible. Existing work on LLMs’ explainability has primarily focused on evaluating their consistency, measuring how explanations change when inputs are perturbed, or on input attribution, which examines how the LLM’s predictions relate to its input processing. Consistency captures only one aspect of what makes an explanation reliable. In practice, reliable explanations must balance multiple, often competing, criteria—such as faithfulness, which reflects how accurately an explanation mirrors the LLM’s internal reasoning; comprehensiveness, which ensures the explanation provides all information to support a prediction; simulatability, where users can anticipate the LLM’s behavior based on its explanation; and ultimately, understandability and usefulness, which ensures the explanation is easy to understand and helpful to users. These criteria interact in complex ways: for example, increasing understandability might come at the cost of faithfulness, while enhancing comprehensiveness could reduce simulatability. Yet, few studies have analyzed these trade-offs or grounded their evaluation frameworks in the needs and expectations of end-users. 
This project aims to analyze the trade-offs between different explanation strategies in terms of their faithfulness to the LLM’s decision-making process and their understandability for end-users. As such, the student will compare recent explainability methods developed for LLMs, such as natural language explanation with chain-of-thought, circuit analysis (e.g., logitlens), and probing methods. This evaluation will be multi-faceted, combining quantitative metrics (e.g., comprehensiveness) and qualitative analysis (e.g., simulatability, usefulness). 
Duties and Responsibilities:  
•   Read recent papers on explainability for LLMs. 
•   Explore relevant benchmark datasets. 
•   Implement LLMs via API calls. 
•   Implement explainability methods (chain of thought, logitlens, probing methods) 
•   Conduct a rigorous evaluation of the different explainability methods. 
Desired Technical Skills:  
Core ML & LLM Fundamentals 
•   Transformer architecture & training dynamics: attention,  MLP blocks, layer norms, logits, sampling/decoding. 
•    Representation learning concepts: embeddings, features, linear separability. 
•   Evaluation basics for NLP: task metrics (accuracy, F1, calibration). 
Programming & Tooling 
•   Python (required) 
•   Deep learning frameworks: PyTorch (preferred) 
•   Hugging Face: transformers, datasets, and tokenizers for loading models/datasets and extracting internals. 
Desired Course(s):  
Desired program: computer science 
Desired specialization: AI or machine learning 
Desired course: machine learning, natural language processing, generative AI, deep learning. 
Other Desired Qualifications:  
Critical thinking & analytical rigor: Ability to question assumptions about faithfulness and plausibility. 
Communication skills: Clear reporting of progress and obstacles. 
Prior experience in research environments for NLP or ML. 

The CareChair:  Improving Mobility Assistance in Senior Care Environments 
Professor: James Elder 
Contact Info: jelder@yorku.ca 
Lab Website: elderlab.yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: The CareChair is a semi-autonomous AI wheelchair designed at York that operates semi-automatically in platoon formation, following a human guide.  The technology reduces labour and physical stress required to provide timely mobility assistance in senior care facilities.  In this project, the student will assist the team in improvements to tracking and obstacle avoidance components 
Duties and Responsibilities: The student will work closely with the supervisors to develop and test the improved robot attentive sensor.  The student will have regular meetings with Senior Robotics Engineer Dr. Helio Perroni-Filho and principal investigator Prof. James Elder. 
At the end of the summer the student will will demonstrate the redesigned attentive sensor system, deliver documented software in the form of a Github repository and deliver an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference. 
Desired Technical Skills: • Software skills, control theory and algorithms, systems design 
Desired Course(s):  
Required: Programming 
Desired (but not essential): Computer vision, robotics, machine learning 
Other Desired Qualifications: N/A 

The CareBot:  Optimal control for social intelligence 
Professor: James Elder 
Contact Info: jelder@yorku.ca 
Lab Website: elderlab.yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: When you walk into a party, one of the first things you will probably do is look around to see who is there.  We are developing an attentive social robot called the CareBot that is capable of attentively searching for people in a room, acquiring high-resolution snapshots of their faces and recognizing their identities.  In this project, the student will explore how the navigation path and attentive gaze of the robot can be optimized to maximize the rate at which new information about the people in the environment is obtained.  This will ultimately support social robot applications that require robots to have human levels of social intelligence, e.g., senior care. 
Duties and Responsibilities: The student will work closely with the supervisors to test long-term 
operation of the robot hardware and software.  Problems will be logged, solutions proposed, designed, implemented and evaluated.  The student will have regular meetings with PhD student Nizwa Javed, Senior Robotics Engineer Dr. Helio Perroni-Filho and principal investigator Prof. James Elder. 
At the end of the summer the student will demonstrate path planning improvements made to the robot and deliver an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference. 
Desired Technical Skills: • Software skills, systems design 
Desired Course(s):  
Required: Programming 
Desired (but not essential): Computer vision, robotics, machine learning 
Other Desired Qualifications: N/A 

Improved control of a robot attentive sensor 
Professor: James Elder 
Contact Info: jelder@yorku.ca 
Lab Website: elderlab.yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: To be effective, a social robot must reliably detect and recognize people in all visual directions and in both near and far fields. A major challenge is the resolution/field-of-view tradeoff, for which we have developed a novel attentive sensing solution. Panoramic low-resolution pre-attentive sensing is provided by an array of wide-angle cameras, while attentive sensing is achieved with a high-resolution, narrow field-of-view camera and a mirror-based gaze deflection system. Quantitative evaluation on a novel dataset shows that this attentive sensing strategy can yield good panoramic face recognition performance in the wild out to distances of ~35m. 
Currently the system operates at about 0.3 fixations per second, far slower than the human fixation rate of 2-3 fixations per second.  In this project we will redesign our system to come closer to human performance.  This will involve lightening the payload, updating the motor, improving the control system and updating the attentive camera to allow manual control of focus so that the camera can change focus while making a saccade.   This improved platform will also allow us to integrate smooth pursuit capabilities in order to smoothly track moving object in the visual field.  We will also update the mechanical structure of the attentive head to eliminate occlusion artifacts. 
Duties and Responsibilities: The student will work closely with the supervisors to develop and test the improved robot attentive sensor.  The student will have regular meetings with PhD student Alek Trajcevski, Senior Robotics Engineer Dr. Helio Perroni-Filho and principal investigator Prof. James Elder. 
At the end of the summer the student will demonstrate the redesigned attentive sensor system, deliver documented software in the form of a Github repository and deliver an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference. 
Desired Technical Skills: • Software skills, control theory and algorithms, systems design 
Desired Course(s):  
Required: Programming 
Desired (but not essential): Computer vision, robotics, machine learning 
Other Desired Qualifications: N/A 

LiDAR-free 3D ground-truthing of motor vehicles 
Professor: James Elder 
Contact Info: jelder@yorku.ca 
Lab Website: elderlab.yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: This is a novel symmetry-based framework for single-view 3D ground-truthing of motor vehicles. LiDAR-based 3D ground-truthing is expensive, requires joint calibration of LiDARs and cameras, and may be inaccurate in the far field where LiDAR returns are sparse. We are developing a tool to annotate and ground truth 3D location, pose and shape of motor vehicles from a 2D image based on 3D symmetry cues and a generalized cylinder model of motor vehicles.  The annotation and ground truth will be used to train a deep learning network for 3D object detection and estimation of motor vehicles. 
Duties and Responsibilities: The student will work on statistical integration of the annotations with an online database of vehicle dimensions to improve inference as well as OpenGL code to visualize estimated generalized cylinder models of the annotated vehicles.  The student will have regular meetings with principal investigator Prof. James Elder. 
Through the project the student will learn how to create crowd-sourced labelled training datasets as well as principles of projective geometry.  She will also learn the principles of Bayesian decision theory and apply it to the integration of annotated point cloud estimates with statistical priors of vehicle dimensions. 
At the end of the summer the student will deliver code and labelled data in the form of a GitHub repository as well as an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference. 
Desired Technical Skills:  
• Software – Python, OpenGL. 
• Concepts – Familiarity with computer vision and 3D geometry skills preferred. 
Desired Course(s):  
Required: Programming, probability, statistics 
Desired (but not essential): Computer vision, machine learning 
Other Desired Qualifications: N/A 

Universal mobility analytics platform (UMAP) 
Professor: James Elder 
Contact Info: jelder@yorku.ca 
Lab Website: elderlab.yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: Traffic congestion is a major challenge throughout the world. It affects commute time, mobility, and accessibility and is a driving factor in increasing the harmful gasses that are the main culprits of the greenhouse effect. Congestion can potentially be mitigated over short time scales through improvements to signal timing and rapid detection and resolution of traffic incidents, and over longer time scales through strategic roadway improvements and optimization of public transit systems.  However, all of these mitigations depend critically on an accurate understanding of lane-by-lane traffic density and speed distributions.  Historically, these data are obtained from embedded inductive loops, but these are expensive to maintain.  This motivates the development of fully automated AI-based video analytics systems to accurately measure traffic density and speed and to detect anomalous traffic incidents. 
In the proposed research we will focus on research toward a universal real-time software application that can be used to automatically derive traffic analytics from terrestrial and drone-based video.  Components of this pipeline include:  1) Camera calibration; 2) Object detection and classification; 3) Back-projection to a terrain model; 4) Tracking; 5) Computation of required analytics (e.g., origin-destination, traffic counts, traffic density, traffic speed, traffic volume, turning movement counts, parking occupancy, near misses). 
Many of these components are quite mature, but research is needed to 1) Improve their reliability and speed; 2) Integrate them more cohesively into a single application. 
Duties and Responsibilities: The student will work closely with the supervisors to develop and test algorithms for video-based highway traffic analytics.  The student will have regular meetings with graduate student Kumar Jha, senior engineer Dr. Helio Perroni Filho and principal investigator Prof. James Elder. 
The planned outcome of this research project is a prototype embedded traffic analytics product that can be used for both terrestrial and drone-based video.  Through the project, the student will learn about traffic monitoring applications, AI systems for object detection and tracking, projective geometry methods for geo-location, and real-time embedded system development. 
At the end of the summer the student will will deliver documented software in the form of a Github repository and an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference. 
Desired Technical Skills: • AI, software skills, systems hardware skills 
Desired Course(s):  
Required: Programming 
Desired (but not essential): Computer vision, machine learning 
Other Desired Qualifications: N/A

Monocular mechanisms for surface attitude estimation in natural scenes 
Professor: James Elder 
Contact Info: jelder@yorku.ca 
Lab Website: elderlab.yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: Current deep network models are remarkably able to estimate depth from a single image.  What are the underlying computational principles?  Recent research in our lab has shown that for the built environment, fusion of semantic segmentation, projective geometry and linear perspective cues provides surprisingly accurate estimates of 3D scene layout, competitive with some recent deep learning approaches.  However, these methods break down for natural scenes such as forests, where semantic and linear perspective cues are weak. 
For such scenes, we hypothesize that approximating the texture process within each semantic image region as stationary will allow a local Fourier analysis to generate reasonable estimates of surface attitude.  Furthermore, when integrated with projective geometry constraints, we predict that these local surface estimates can be propagated to recover approximate 3D scene layout. 
In this project, the student will assist the team in testing this theory, implementing it as an image-computable computational model and measuring its performance on standard public datasets.  In parallel, we will conduct psychophysical experiments in which human observers estimate local surface attitudes on these same datasets.  Decision variable correlation of model judgements with human and deep network estimates will allow us to assess the extent to which the model accounts for human and deep learning estimation of 3D surface structure in natural scenes.  This research will thus provide valuable insights into both the computational neuroscience of human 3D perception and into the principles underlying monocular depth perception in recent deep network models. 
Duties and Responsibilities: The student will work closely with the supervisors to develop and test algorithms for video-based highway traffic analytics.  The student will have regular meetings with postdoctoral Fellow David White and principal investigator Prof. James Elder. 
The planned outcome of this research project is a study is a new Fourier-based algorithm for estimating surface orientation from projected texture, an evaluation of this algorithm on ground truth datasets, and a comparison against human judgements of surface orientation for the same scenes. 
At the end of the summer the student will will deliver documented software in the form of a Github repository and an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference. 
Desired Technical Skills:  
• Mathematical skills, including Fourier transforms, linear algebra, probability and statistics 
• Coding skills (Python and/or Matlab) 
• An interest in science 
Desired Course(s):  
Required: Probability, statistics, programming 
Desired (but not essential): Perception, computer vision, computer graphics 
Other Desired Qualifications: N/A 

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

Improving configural processing in deep neural networks 
Professor: James Elder 
Contact Info: jelder@yorku.ca 
Lab Website: elderlab.yorku.ca 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: ImageNet-trained networks fail to develop a configural representation of object shape in the way that humans do.  This could lead to discrepancies between human and AI object judgements and vulnerability of AI systems to adversarial attack.   In this project, the student will explore three possible causes of this failure: 1) training curriculum; 2) task and 3) network architecture.  
To support this investigation, the student will first establish a new database of segmented and labelled object shapes.  To explore the role of training curriculum, the student will examine the effect of lowpass filtering shapes in shape frequency space, in order to emphasize more configural properties, and also train with random occlusions to discourage reliance on specific local features.  To explore task, the student will explore the addition of a secondary segmentation task.  To explore architecture, the student will compare standard convolution and transformer architectures to a novel graph neural network architecture aligned with the topology of the input. 
Duties and Responsibilities: The student will work closely with Prof. Elder.  Through the project, the student will gain expertise in establishing an curating labelled datasets and training and evaluating neural networks on these datasets. 
At the end of the summer the student will deliver documented software in the form of a Github repository and an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference. 
Desired Technical Skills: 
 • Software skills, Python, PyTorch, deep learning 
Desired Course(s):  
Required: Probability, statistics, programming 
Desired (but not essential): Signal processing, computer vision, machine learning 
Other Desired Qualifications: N/A  

Ellipti-linear representations for estimation of the 3D rim of an object from its 2D occluding contour 
Professor: James Elder 
Contact Info: helio@yorku.ca 
Lab Website: 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 ellipti-linear representations of occluding contours.  The student will validate the software and then analyze how these occluding contour representations relate to ellipti-linear approximations of the rim.  Based on this analysis, the student will develop an algorithm to estimate the 3D shape of the rim from the 2D shape of the occluding contour.   The student will have regular meetings with collaborator Yiming Qian (A*Star Singapore) and principal investigator Prof. James Elder. 
At the end of the summer the student will deliver code and labelled data in the form of a GitHub repository, and an engineering report in LaTeX that documents the objectives, methods, results and conclusions of this project, and will present results at the Lassonde Annual Undergraduate Summer Student Research Conference. 
Desired Technical Skills:  
• Aptitude in mathematics and statistics 
• Coding 
Desired Course(s): Required: Calculus, probability, statistics, programming 
Desired (but not essential): Computer vision, computer graphics, machine learning 
Other Desired Qualifications: N/A 

XTune: Reliable and eXplainable Data Systems Tuning via Deep Learning 
Professor: Jarek Szlichta 
Contact Info: szlichta@yorku.ca 
Lab Website: https://www.yorku.ca/lassonde/lab/data-and-ai/
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); 
Open Positions: 1 
Project Description: Modern data systems, such as IBM Db2, have dozens of system configuration parameters, commonly referred to as knobs. These parameters wield significant influence over the performance of business queries. Knobs are responsible for configuring various aspects, including the allocation of working memory, such as the number of pages allocated to the buffer pool and sortheap, the degree of parallelism to be used, and even toggle specific features by setting an optimization level. 

Manual configuration tuning experts are a labor-intensive and time-consuming process. Consequently, we propose XTune, a reliable and eXplainable, query-informed tuning system. XTune harnesses deep reinforcement learning (DRL) techniques based on actor-critic neural networks, specifically proximal policy optimization (PPO), to tune system configurations. Notably, the PPO policy is considered state-of-the-art by OpenAI, owing to its stability, sample efficiency, and robustness in addressing various reinforcement learning challenges. It computes updates at each step to minimize the loss function while ensuring minimal deviation from the previous policy. The optimization process includes strategies like introducing back pressure to manage resource utilization in cloud computing for sustainability purposes. It begins with the translation of high-dimensional query execution plans (QEPs) into a lower-dimensional space using embeddings derived from Bidirectional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNN), which then serve as inputs for the DRL models.  
In the context of large-scale machine learning models, their inherent complexity often renders them as “black boxes,” posing challenges for experts to decipher their prediction processes. The lack of interpretability within predictive models undermines the confidence experts place in these models, particularly in scenarios involving critical decisions, such data systems tuning. To tackle this issue and cultivate enhanced interpretability within data systems, our research introduces methods to generate saliency and counterfactual explanations, effectively transforming these black boxes into “glass boxes” that offer individuals insights into their internal mechanisms. Our saliency explanation method for tuning system configurations approximates the importance of model features, such as query subplans. On the other hand, our counterfactual explanations reveal what should have been different in queries and query execution plans (QEPs) in terms of perturbations to observe a diverse or desired outcome. To further enhance our approach, we implement an instance-based counterfactual strategy. This strategy outputs similar QEPs from the workload, rather than using arbitrary perturbations, resulting in a diverse tuning outcome. 

We evaluate our methods over synthetic and real query workloads, quantifying their effectiveness and performance benefits, particularly in the context of data lake-driven workloads. The development of XTune advances the reliability of data systems, while also aligning with the principles of sustainability, resulting in responsible technology usage. Ultimately, the impact of XTune resonates across industries, illustrating how responsible AI can drive positive change. 
Duties and Responsibilities: Students’ duties and responsibilities will include: reviewing related work in automatic knobs tuning for data systems, designing large-scale machine learning-driven approaches to the tuning of configuration parameters, implementing the solution with the deep reinforcement learning model, conducting comprehensive experimental evaluation over synthetic and real-world query workloads, and writing a research paper to be submitted to one of the top-tier conferences in data science, such as VLDB, ACM SIGMOD, IEEE ICDE and EDBT.
Desired Technical Skills: The student should possess algorithmic design and development knowledge, as well as demonstrate strong programming skills. 
Desired Course(s): It is recommended to have completed some of the data science courses such as:
• LE/EECS 3405 3.00 – Fundamentals of Machine Learning
• LE/EECS 3421 3.00 – Introduction to Database Systems
• LE/EECS 4415 3.00 – Big Data Systems
• LE/EECS 4411 3.00 – Database Management Systems
• LE/EECS 4412 3.00 – Data Mining etc. 
Other Desired Qualifications: Other qualifications include good communication skills. 

CORAL: COncept-based Explanations for RAG LLMs 
Professor: Jarek Szlichta 
Contact Info: szlichta@yorku.ca 
Lab Website: https://www.yorku.ca/lassonde/lab/data-and-ai/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA); 
Open Positions: 1 
Project Description: Large language models (LLMs) that use retrieval-augmented generation (RAG) are increasingly deployed to answer broad, open-ended questions. However, when a user asks something like “What is the best treatment for a migraine?”, there is no single correct answer. The response depends on what the user means by “best” (fastest relief, fewest side effects, natural alternatives) and on how the model interprets the question through the documents it retrieves (scientific papers, clinical guidelines, online forums, or patient blogs). Current systems provide none of this structure to the user, and so the underlying variability, reasoning, and dependence on intent or interpretation remain opaque. This project will contribute to CORAL, a proof-of-concept tool designed to make such answer variability transparent by organizing the space of possible LLM outputs into semantic concepts. 

The core idea behind CORAL is to treat answer criteria (user intents) and source interpretations (types of retrieved documents) as concepts that can be combined in different ways. Each combination forms a node in a concept of lattice. At each node, the system prompts the LLM using a reworded version of the original question that specifies that node’s intents or interpretations and records the resulting answer. Navigating the lattice allows a user to visualize how answers vary, understand counterfactuals (“what changes if I care about natural remedies rather than clinical effectiveness?”), and explore the minimal adjustments to the prompt or retrieval sources that yield alternative responses. This conceptual perspective differs from prior work that attempts to explain LLMs mechanically (e.g., tracing facts to neurons) or analyses that only examine how RAG sources influence outputs. Instead, CORAL aims to expose meaningful high-level dimensions along which answers differ. 

Populating a full lattice naively would require many LLM inference calls, so the project explores pruning strategies that reduce computation without losing important variations. One strategy extracts intent automatically from the model’s own reasoning traces, using graph-of-thoughts outputs to identify which answer criteria matter. Another strategy prompts the model separately with each individual intent, asks it to list and rank plausible answers, and uses rank-aggregation techniques to approximate answers for combinations of intents before selectively querying only those combinations likely to differ. A third strategy uses the model’s reasoning to identify which types of sources are likely to shift the answer, allowing selective exploration of alternative interpretations. 

A student working on this project will perform research along these directions, experiment with pruning methods, evaluate how well they capture true answer variation, and improve the interactive interface for exploring answer spaces. The work combines LLM prompting, RAG pipelines, data-structure design, concept lattices, and explainability. The outcome will be an improved prototype and demonstration showing how users can more clearly understand the range of plausible answers an LLM might generate and how intent and interpretation shape those answers, with applications to query refinement, medical information exploration, and safer LLM deployment. 
Duties and Responsibilities: Students’ duties and responsibilities will include: reviewing related work in RAG LLMs, designing large-scale machine learning-driven approaches to explainable AI, implementing the solution, conducting comprehensive experimental evaluation over real-world datasets, and writing a research paper to be submitted to one of the top-tier conferences in data science, such as VLDB, ACM SIGMOD, IEEE ICDE and EDBT. 
Desired Technical Skills: The student should possess algorithmic design and development knowledge, as well as demonstrate strong programming skills. 
Desired Course(s): It is recommended to have completed some of the data science courses such as:
• LE/EECS 3405 3.00 – Fundamentals of Machine Learning
• LE/EECS 3421 3.00 – Introduction to Database Systems
• LE/EECS 4415 3.00 – Big Data Systems
• LE/EECS 4411 3.00 – Database Management Systems
• LE/EECS 4412 3.00 – Data Mining etc. 
Other Desired Qualifications: Other qualifications include good communication skills. 

Thermal behavior and prediction of semiconductor devices in a photovoltaic (PV) modular medium voltage power converter
Professor: John Lam
Contact Info: johnlam@yorku.ca
Lab Website: https://pelser.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA); 
Open Positions: 1
Project Description: To facilitate large-scale photovoltaic (PV) energy power conversion, modular power converters that are connected to the medium voltage distribution grid, are used to extract maximum PV power under varying PV irradiation levels. Maintaining high power conversion efficiency in the PV power converter under a wide range of PV irradiation levels is essential. The power losses in the semiconductor devices of the modular PV converter must be minimized to ensure high power efficiency. These power losses which include conduction and switching power loss of the semiconductor switch, can negatively impact the junction temperature of the semiconductor device. This research project is to investigate and predict the thermal behavior of the silicon carbide (SiC) semiconductor devices that are employed in a modular PV step-up converter with output power balancing feature. The thermal analysis will first be performed with power electronics simulation software, PowerSIM. The power losses associated to all the SiC devices employed in the converter, as well as each device’s junction temperature will be studied and analyzed in PowerSIM, based on the MV full-scale rated power design. Then, a scale-down version of the modular PV power converter will be investigated in both PowerSIM as well as through a proof-of-concept hardware circuit prototype (that is already available in the Advanced Power Electronics Laboratory for Sustainable Energy Research (PELSER)). During the hardware verification stage, hardware experimental testing tools such as thermal camera, digital oscilloscope, PV emulator and high power voltage source will be utilized.
Duties and Responsibilities: 
• understand the electrical and thermal characteristics of silicon-carbide (SiC) devices
• investigate and develop circuit model in power electronics simulation to analyze the thermal behavior of all the semiconductor devices
• analyze and evaluate the thermal performance of various SiC devices under a wide range of PV irradiation levels
• conduct thermal,studies through hardware evaluation on a scaled-down SiC based prototype
• regular weekly meetings with the supervisor or post-doc fellow
Desired Technical Skills:
• good problem-solving skills
• strong mathematical and analytical skills (e.g. complex analysis differential equations)
• circuit analysis
• fundamental knowledge on semiconductor devices
• good programming skills in MATLAB
Desired Course(s): 
• Electrical Circuits (AC & DC circuit analysis)
• Electronics
• Electrical Systems
• Power Electronics
• Introduction to Energy Systems
Other Desired Qualifications: Hardworking, self-motivated, good teamwork, strong written and oral communication skills 
 

Modular dual-input sub-circuits converter for photovoltaic (PV) energy conversion and energy storage in DC microgrid
Professor: John Lam
Contact Info: johnlam@yorku.ca
Lab Website: https://pelser.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA); 
Open Positions: 1
Project Description: Microgrid with high voltage DC distribution (400 – 800V) provides an attractive power architecture for applications such as small-scale renewable energy (e.g PV energy) systems, commercial buildings, and data centers with storage due to: (1) fewer number of power conversion stages, (2) higher overall power efficiency with reduced transmission power loss, (3) the ease of inter-connecting with back-up storage. This research project is to investigate and develop a modular PV converter that utilizes multiple dual-input sub-circuits for interfacing with PV and energy storage in a DC microgrid. The power interface circuit topology and a control scheme for controlling the PV power and managing energy storage will be investigated. In the proposed approach, each dual-input sub-circuit in the developed power converter will be capable of simultaneously supporting PV energy conversion and transferring the power of the energy storage device to the output. The performance of the devised converter system will be verified using power electronics simulation software such as PowerSIM. Preliminary hardware validation on a proof-of-concept prototype will also be performed. This research project will be conducted at the Advanced Power Electronics Laboratory for Sustainable Energy Research. During the hardware verification stage, hardware experimental testing tools such as digital oscilloscope and PV emulator will be utilized.
Duties and Responsibilities: 
• understand the operating principles and voltage gain characteristics of the dual-input power converter 
• develop circuit model in power electronics simulation to analyze the operating principles of the developed converter
• develop a controller to: 1) support PV power control and 2) adjust the overall voltage gain of the converter
• analyze the dual-input converter’s characteristics in MATLAB
• conduct preliminary hardware evaluation on a proof-of-concept prototype
• regular weekly meetings with the supervisor or post-doc fellow
Desired Technical Skills:
• good problem-solving skills
• strong mathematical and analytical skills (e.g. complex analysis differential equations)
• circuit analysis
• fundamental knowledge on semiconductor devices
• good programming skills in MATLAB, DSP, micro-controller
Desired Course(s): 
• Electrical Circuits (AC & DC circuit analysis)
• Electronics
• Digital Logic 
• Power Electronics
• Introduction to Energy Systems
Other Desired Qualifications: Hardworking, self-motivated, good teamwork, strong written and oral communication skills 
 

Gallium Nitride (GaN) based grid-connected AC/DC converter with a reduced input filter 
Professor: John Lam 
Contact Info: johnlam@yorku.ca 
Lab Website: https://pelser.lab.yorku.ca/  
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA); 
Open Positions: 1 
Project Description: Due to the intermittent nature of renewable sources (such as wind or solar), grid support is essential in a distributed renewable energy network to provide reliable and effective power transfer. To facilitate power conversion with minimal amount of current harmonics being drawn from the grid, a high power-efficient AC/DC converter with power factor correction (PFC) is required. 
With the inherent fast switching characteristics of the emerging wide bandgap switching devices, such as Gallium Nitride (GaN) FETs, very high frequency (in MHz range) power conversion is becoming feasible. This research project is to develop a GaN based AC/DC converter with very high frequency operation for interfacing with the grid. In particular, interleaved boost converter modules will be utilized in this design so that the input current ripple of the AC/DC converter can be reduced. As a result, the required input filter components (inductor and capacitor) needed on the grid side can be substantially reduced. The performance of the devised AC/DC converter system will be verified first using power electronics simulation software such as PowerSIM (PSIM). A controller will be designed in PSIM to support the interleaved operating modes of the boost circuit modules. Then, proof-of-concept hardware prototype will be developed at the Advanced Power Electronics Laboratory for Sustainable Energy Research. Preliminary hardware validation will then be performed. During the hardware verification stage, hardware experimental testing tools such as digital oscilloscope, AC voltage source, thermal camera and impedance analyzer will be utilized. 
Duties and Resposibilities:  
•  understand the operating principles and the basic control scheme of interleaved boost converters  
•  develop circuit model in power electronics simulation to analyze the operating principles of the developed AC/DC converter  
•  develop a control scheme to activate the minimum number of interleaved modules based on the current power condition 
•  analyze the characteristics of the achieved grid current in MATLAB 
•  conduct preliminary hardware evaluation on a proof-of-concept prototype  
•  regular weekly meetings with the supervisor or post-doc fellow 
Desired Technical Skills: 
•  good problem solving skills 
• strong mathematical and analytical skills (e.g. complex analysis, differential equations) 
•  solid knowledge on circuit analysis, electronics & control 
•  fundamental knowledge on semiconductor devices 
•  good programming skills in MATLAB, DSP, micro-controller 
Desired Course(s):  
•  Electrical Circuits (AC & DC circuit analysis) 
• Electronics 
•  Digital Logic 
•  Linear Control 
•  Power Electronics 
Other Desired Qualifications: 
•  hardworking 
•  self-motivated 
•  good teamwork 
•  strong written and oral communication skills 
 

Developing python-based labs and projects for undergraduate computer programming and mechatronics course  
Professor: Kai Zhuang 
Contact Info: kai.zhuang@lassonde.yorku.ca  
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: 
Project Overview:
This summer project aims to convert the laboratories and projects of EECS1011, an undergraduate introductory course in computer programming and mechatronics for engineers, from MATLAB to Python. EECS1011 currently uses MATLAB as the primary programming language; however, Python is increasingly a more appropriate choice for introductory engineering education.

Rationale
Python offers several advantages over MATLAB for this course context. It is widely regarded as beginner-friendly, has a clear and readable syntax, and is broadly used across engineering, data science, robotics, and industry practice. Importantly, Python provides mathematical and numerical capabilities comparable to MATLAB through libraries such as NumPy, SciPy, and Matplotlib.

Recent developments in MATLAB’s IDE—particularly the deep integration of AI-assisted coding tools—raise concerns about its suitability for introductory undergraduate education, where foundational programming skills, conceptual understanding, and deliberate practice are core learning goals. A transition to Python allows for a more pedagogically controlled learning environment.

In addition, the course has been experimenting with a hands-on Lab Kit platform (M5Stack), which offers native support for MicroPython. Aligning the course language with the hardware platform significantly reduces instructional overhead, software licensing constraints, and technical complexity, while strengthening the connection between programming concepts and physical systems.
Project Scope

The student-researcher will:
• Translate existing EECS1011 MATLAB labs and projects into Python equivalents.
• Ensure functional and pedagogical equivalence, preserving learning outcomes while leveraging Pythonic best practices.
• Adapt code for compatibility with both desktop Python environments and MicroPython where appropriate.
• Test and validate the converted materials for correctness, usability, and instructional clarity.
• Document conversion decisions and provide instructor-facing notes to support future course delivery.

Expected Outcomes
The project will result in a Python-based version of EECS1011 labs and projects that:
• Supports introductory programming and mechatronics learning more effectively.
• Aligns with modern engineering practice and tooling.
• Reduces dependency on proprietary software and licensing.
• Improves integration with physical computing platforms such as M5Stack.
This work will directly support curriculum modernization while providing the undergraduate student with meaningful experience in educational software development, testing, and engineering pedagogy.
The student-researcher will be responsible for:
• Reviewing existing EECS1011 MATLAB-based laboratory exercises and project materials.
• Translating MATLAB code and workflows into Python, using appropriate libraries (e.g., NumPy, SciPy, Matplotlib).
• Adapting programming activities to follow beginner-friendly, Pythonic coding practices.
• Supporting compatibility with both standard Python environments and MicroPython where appropriate.
• Testing and debugging converted labs and projects to ensure functional correctness and conceptual alignment with learning objectives.
• Comparing MATLAB and Python implementations to ensure pedagogical equivalence and clarity.
• Documenting code, assumptions, and instructional decisions to support instructors and future teaching assistants.
• Assisting with minor revisions to lab instructions, starter code, and sample solutions to reflect the Python-based workflow.
• Participating in regular meetings with the project supervisor to report progress, challenges, and findings.
• Incorporating feedback from instructors and pilot testing into revised materials.
• Following best practices in version control (e.g., Git) and maintaining an organized project repository.
Desired Technical Skills:
• Basic to intermediate proficiency in Python, including familiarity with core syntax, functions, and control structures.
• Prior experience with MATLAB, or the ability to quickly read and understand MATLAB code.
• Understanding of fundamental programming concepts (e.g., variables, loops, conditionals, functions).
• Familiarity with numerical and scientific computing concepts (arrays, matrices, plotting, basic signal or data processing).
• Experience with or willingness to learn NumPy and Matplotlib (or equivalent Python libraries).
• Basic experience with version control systems (e.g., Git) or openness to learning collaborative code workflows.
• Ability to debug, test, and validate code across different environments.
• Familiarity with microcontrollers, embedded systems, or physical computing platforms (e.g., Arduino, M5Stack, MicroPython) is an asset but not required.
• Comfort working across different programming environments and IDEs.
• Ability to read technical documentation and translate concepts between programming languages.
Experience with EECS1011 and demonstrated excellence in EECS1011 is a significant advantage.
Desired Course(s):
Computational Thinking and Computer Programming (procedural programming)
Mechatronics (Arduino platform specifically)
Other Desired Qualifications:
Demonstrated capacity for self-management
Reliability
Track record of innovation (particularly in class projects)

Generative AI for Software Engineering tasks 
Professor: Marios Fokaefs 
Contact Info: fokaefs@yorku.ca 
Lab Website: https://fokaefs.github.io/research/projects.html 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 3 
Project Description: The project will explore the use of novel AI models, including deep learning and large language models, in software engineering tasks that are characterized either by highly cognitive tasks, such as software architecture and design, or by massive amounts of data, such as incident management in distributed and heterogeneous systems. The student will investigate the capabilities and limitations of tools like ChatGPT, Llama, Gemini, to a) understand software requirements in natural text and produce formal artifacts, like diagrams and deployment scripts, and b) collect and summarize large amounts of data to reduce the cognitive workload for reliability engineers. The student will learn to evaluate and judge AI-generated artifacts to avoid “hallucinations” and ensure the highest quality of the final products. 
Duties and Responsibilities: The student will participate in weekly meetings, include project meetings with the participation of other students (graduate and undergraduate) working on similar topics as well as lab meetings where they will be exposed to a broader collection of research topics. The supervisor maintains a Slack space for all students enabling instant communication with the professor and the group. 
The student will generally be involved in literature reviews, follow tutorials to learn about the employed technologies, develop or contribute to tools pertaining to the research topic, and conduct empirical studies to evaluate the quality of the tools or to test tools and methods provided by other researchers. The student will be asked to present their progress at least once every two weeks to the entire group. The student will also contribute to academic writing tasks, including but not limited to, conference/journal paper writing, posters and presentations. 
Desired Technical Skills: 
• Good programming skills, preferably Python. 
• Basic understanding of AI and LLMs, experienced user of LLM tools. 
• Good writing and presentation skills 
Desired Course(s): Software Design, Software Tools, Software Testing and Quality, Object-Oriented Programming 
A grade of at least B+ is expected in any of these courses. 
Other Desired Qualifications: Student should be at a level to understand academic papers, take initiatives, be ready to experiment and work with new tools with minimal guidance, gain experience and present tutorials to train other students. 

Intelligent Social Robots for Healthy Aging 
Professor: Meiying Qin (Point of contact), James Elder, Michael Jenkin, Lauren E Sergio, Michael Bazzocchi, Susan J. E. Murtha, Taylor Cleworth, Elham Dolatabadi, Michael Kalu, Kiemute Oyibo, Joseph FX DeSouza, R. Shayna Rosenbaum, Aijun An, Vijay Mago, Jonathan Obar 
Contact Info: mqin@yorku.ca 
Lab Website: https://lassonde.yorku.ca/users/mqin 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 5 
Project Description: This project is part of the Co-creating Intelligent Neuro-Technologies for Healthy Aging (CINTHeA) initiative, which aims to develop socially assistive robots that help older adults maintain mobility, cognitive health, and social engagement. The student will work on the design and evaluation of a social robot prototype that interacts naturally with older adults, provides non-contact assistance, and promotes cognitive stimulation and social connectedness. The work will involve participating in co-design sessions with older adults and caregivers to understand user needs and preferences, assisting in research on robot perception and communication, and exploring natural language interaction and emotional intelligence features to improve user experience. The student will also contribute to integrating conversational features and serious games that support cognitive health and reduce loneliness, as well as conducting user testing and analyzing feedback to refine the robot’s design and interaction strategies. This project offers hands-on experience in human-robot interaction, AI-driven communication, and inclusive technology design, while addressing real-world challenges in aging and healthcare. 
Duties and Responsibilities: The student will play an active role in the research and development of a socially assistive robot designed to support healthy aging. Their primary responsibility will be to assist in the technical implementation and evaluation of the robot’s interactive features. This includes contributing to the integration of speech recognition and conversational AI components, testing natural language interaction capabilities, and exploring methods for detecting emotional cues through voice and facial expressions. 
The student will also be responsible for preparing and running controlled user interaction trials to assess usability and acceptance of the robot among older adults. They will collect and organize observational and survey data, analyze results, and summarize findings to inform iterative improvements to the robot’s design. In addition, the student will document all development and testing activities, maintain version control for code and design assets, and prepare progress reports or support publications. 
Desired Technical Skills: Students may contribute to different aspects of the project and do not need expertise in both hardware and software. Depending on their interests and background, they may work on areas such as: 
– Programming: Experience with Python or C++ for robotics applications, familiarity with ROS (Robot Operating System) 
– Software: Basic knowledge of AI or machine learning for natural language processing and perception tasks. 
– Hardware and Prototyping: Basic skills in electronics, sensors, or mechanical design for robot integration; experience with 3D printing or CAD tools is helpful but not required. 
– Data Analysis: Ability to process and analyze experimental data using Python, MATLAB, or similar tools; knowledge of statistical methods for usability studies is an advantage. 
Desired Course(s):  
EECS 3311 – Advanced Object-Oriented Programming or EECS EECS 1022 – Introduction to Object-Oriented Programming  
At least one of the courses (or prior research experiences in robotics/computer vision/natural language processing/artificial intelligence/data science): 
• EECS 4421 – Introduction to Robotics 
• EECS 4422 – Computer Vision 
• EECS 3401 – Introduction to Artificial Intelligence and Logic Programming 
• EECS 3404 – Applied Machine Learning 
Other Desired Qualifications: The ideal candidate should demonstrate curiosity and enthusiasm for interdisciplinary research that combines technology and human-centered design. Strong problem-solving skills, attention to detail, and the ability to work independently while collaborating effectively within a team are highly valued. Good communication skills are essential, as the student will engage with older adults and caregivers during co-design and evaluation activities. 

Technology in Computer Science Education| 
Professor: Meiying Qin, Sonya Allin 
Contact Info: mqin@yorku.ca, sallin@yorku.ca 
Lab Website: https://lassonde.yorku.ca/users/mqin, https://lassonde.yorku.ca/users/sallin 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 1 
Project Description: This project investigates creative ways to enhance computer science education through emerging technologies such as games, virtual reality (VR), socially assistive robots, and artificial intelligence. The goal is to design and evaluate interactive learning experiences that make computing concepts more engaging and accessible for diverse learners. In addition to exploring immersive environments and gamification strategies, the project will examine how AI tools and intelligent systems influence learning outcomes, motivation, and problem-solving skills. Students will apply user-centered design principles to develop prototypes and conduct evaluations, ensuring that these technologies promote inclusion, adaptability, and meaningful learning in computer science education. 
Duties and Responsibilities: The student will assist in creating and testing technology-based learning tools for computer science education. They will help design interactive prototypes using platforms such as games, VR, or AI-driven applications, and adapt these tools for classroom use or in-person testing. Responsibilities include programming features, configuring hardware or software environments, and ensuring usability through iterative improvements. The student will also support user studies by preparing materials, collecting data, and analyzing results to evaluate learning outcomes. Clear documentation of all work and active participation in team meetings are expected. By the end of the project, the student should demonstrate practical skills in educational technology development and evaluation. 
Desired Technical Skills: Strong programming skills; Strong problem-solving skills and willingness to learn new tools are essential; 
Familiarity with game development or VR platforms is an asset. 
Desired Course(s): EECS 3311 – Advanced Object-Oriented Programming or EECS EECS 1022 – Introduction to Object-Oriented Programming 
Other Desired Qualifications: The student should demonstrate creativity and interest in applying technology to education. Familiarity with user-centered design principles or experience conducting usability studies is an asset. A genuine interest in improving learning experiences and openness to interdisciplinary approaches will be highly valued. 

Generative-AI–Enabled Semantic Communication Networks
Professor: Ping Wang
Contact Info: pingw@yorku.ca
Lab Website: https://lassonde.yorku.ca/users/pingw
Position Type: NSERC Undergraduate Student Research Award (USRA)
Open Positions: 2 
Project Description: Future communication networks face increasing pressure from limited bandwidth, unreliable wireless channels, and the explosive growth of data-intensive applications. Semantic communication networks have emerged as a promising paradigm to address these challenges by transmitting meaning rather than raw data, leveraging artificial intelligence to improve communication efficiency and robustness. However, existing semantic communication architectures remain limited by their lack of context reasoning and insufficient background knowledge provisioning, which restricts their performance in complex and dynamic environments.
This undergraduate research project aims to design and evaluate AI-enabled semantic communication techniques that enhance robustness, efficiency, and semantic fidelity in wireless networks. Specifically, the student will explore the use of generative AI (GAI) models, such as transformer-based architectures, to construct and utilize source, task, and channel knowledge datasets that provide rich contextual and semantic information. These models will be used to generate diverse, personalized, and multimodal semantic representations, enabling more reliable communication even under noisy or bandwidth-limited conditions. 
A key focus of the project is the development of semantic-level joint source–channel encoding and decoding schemes using deep learning. Unlike conventional systems that separately perform source coding and channel coding, the proposed approach jointly exploits data semantic diversity and channel characteristics to improve end-to-end performance. This design is particularly advantageous in low signal-to-noise ratio (SNR) and limited bandwidth scenarios, where traditional communication schemes suffer significant performance degradation. By tolerating controlled distortion during transmission while preserving semantic meaning, the system can achieve greater robustness over severe wireless channels. The project will also investigate the integration of pre-trained semantic encoders and decoders with background knowledge models, enabling the receiver to correctly infer the intended meaning even when the transmitted signal is partially corrupted. Practical considerations such as model convergence time, memory complexity, and computational efficiency will be considered to ensure feasibility for real-world communication systems. This project will provide the student with hands-on experience in AI-driven wireless communications, deep learning model development, and experimental research, while contributing to an emerging and impactful research direction at the intersection of communication theory and artificial intelligence. 
Duties and Responsibilities: 
Over the project period, the student will:
• Study foundational concepts in semantic communications and generative AI;
• Implement and experiment with transformer-based semantic encoding/decoding models;
• Design and evaluate joint source–channel learning frameworks;
• Conduct simulation-based performance evaluation in terms of efficiency, reliability, and latency.
Desired Technical Skills: Good at programming using Python.
Desired Course(s): Have background knowledge of communications, signal and systems, and machine learning.
Other Desired Qualifications: Good GPA; Self-motivated.

Human-Computer Interaction in Virtual Reality 
Professor: Robert Allison 
Contact Info: rallison@yorku.ca 
Lab Website: https://percept.eecs.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA); 
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.  
Duties and Responsibilities: 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. 
Desired Technical Skills: Programming, experimental design, computer graphics, HCI, statistics, presentations and technical writing 
Desired Course(s): Programming, computer graphics, HCI, statistics 
Other Desired Qualifications: Ability to work well in a team environment. 

Physical Actions in Video-based Vision Language Models  
Professor: Dr. Shweta Mahajan 
Contact Info: shwemaha@yorku.ca 
Lab Website: https://lassonde.yorku.ca/users/shwemaha 
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2 
Project Description: Recent advances in vision–language models (VLMs) have enabled systems to reason jointly about images and natural language, supporting tasks such as captioning, question answering, and instruction following. However, most existing VLMs reason over static images, limiting their ability to model physical actions, which are inherently dynamic and unfold over time.   

This project builds on recent work, “Do-Undo: Generating and Reversing Physical Actions in Vision–Language Models,” which studies how image-based VLMs understand physical actions and their reversibility (e.g., opening vs. closing, picking up vs. putting down). The goal of this summer project is to extend these ideas to video-based vision–language models, enabling richer reasoning about actions, temporal structure, and physical state changes.   
The student will investigate how modern video-language models represent and reason about physical actions across time.

Key questions include:   
How well do video-based VLMs understand actions and their reversals?   
Can models generate or recognize temporally consistent “do” and “undo” action sequences in video?   
How does temporal information improve (or fail to improve) physical reasoning compared to single-image models?  
Duties and Responsibilities: Surveying and experimenting with state-of-the-art video-language models  
Designing prompts and evaluation protocols for action understanding and reversal in videos  
Creating or adapting small video datasets focused on physical actions  
Analyzing model behavior, failures, and temporal reasoning capabilities  
Implementing extensions to existing Do-Undo benchmarks for video input 
Desired Technical Skills: Background in machine learning or computer vision  
Familiarity with Python and deep learning frameworks (e.g., PyTorch) 
Desired Course(s): Introduction to robotics, Deep Learning for Computer Vision, Linear Algebra, Probabilistic methods, Computer Vision 
Other Desired Qualifications: Interest in multimodal reasoning and AI systems that interact with the physical world Prior experience with GPU training of vision models 

Using Graphene as a Material for Tritium and Deuterium Separation in Water 
Professor: Simone Pisana 
Contact Info: pisana@yorku.ca 
Lab Website: https://pisana.lab.yorku.ca/
Position Type: Lassonde Undergraduate Research Award (LURA);NSERC Undergraduate Student Research Award (USRA); 
Open Positions: 2 
Project Description: Students will help our group’s effort to devise a way to efficiently separate deuterium and tritium in water. Deuterium is a heavy form of hydrogen, it is naturally occurring and is an important resource used in the nuclear, medical, and chemical industries. Tritium is a rare radioactive isotope of hydrogen and is considered a waste product from nuclear generation stations but an element of strategic importance for future fusion power generation programs. Therefore, devising ways to economically separate deuterium and tritium-containing water molecules from water mixtures is of important for clean and economical power generation, among other applications. The work is in partnership with a local company, so there are opportunities to interact and share progress. 
Duties and Responsibilities: Depending on the student’s aptitudes and interest, the responsibilities include: isotopic testing of water samples (nuclear magnetic resonance, mass spectroscopy or infrared spectroscopy), fabrication of graphene-based water filters, characterization of filter samples, assisting graduate students with their projects, conducting studies and analyzing the results.  
Desired Technical Skills: Familiarity with the structure of materials (crystals, defects, bonding), basic chemistry techniques. 
Desired Course(s): Introductory courses in materials science (i.e. CHEM 1100 Chemistry and Materials Science for Engineers), chemistry (i.e. CHEM 1001 Chemical Dynamics, CHEM 2011 Introduction to Thermodynamics), physical electronics or solid state physics (i.e. EECS 3610 Semiconductor Physics and Devices), optics (i.e. EECS 4614 Electro-Optics). 
Other Desired Qualifications: Great organizational skill, record-keeping, hands-on skill, ability to work well both alone or as part of a team, resourcefulness, creativity, and problem-solving skills. 

Design-by-Contract for Vibe Coding: A Reliability Framework for LLM-Driven Software Development 
Professor: Song Wang 
Contact Info: wangsong@yorku.ca 
Lab Website: https://www.eecs.yorku.ca/~wangsong/ 
Position Type: NSERC Undergraduate Student Research Award (USRA) 
Open Positions: 2 
Project Description: This project aims to build a Design by Contract (DbC) framework for vibe coding, a programming paradigm in which developers rely on large language models (LLMs) to generate and evolve code through natural language interaction. While vibe coding significantly improves productivity, it also introduces reliability challenges such as semantic drift, implicit assumptions, and hard-to-detect logic errors. To address these issues, the proposed framework embeds explicit, machine-checkable contracts, including preconditions, postconditions, and invariants, into the LLM-driven development workflow. The framework enables contracts to be specified by developers, inferred from natural language prompts, existing code, or tests, and represented in a unified, executable form. During code generation and modification, LLMs are guided by these contracts to produce contract-compliant implementations. Generated code is then automatically validated using static and dynamic contract checking, and any detected violations are translated into structured feedback for iterative LLM-based refinement. The project will implement a prototype integrated with modern development environments and evaluate it on real world vibe coding scenarios, measuring reductions in bugs, contract violations, and developer effort. Overall, this work seeks to provide a principled reliability foundation for AI-assisted programming, enabling developers to benefit from vibe coding without sacrificing correctness, maintainability, or trust. 
Duties and Responsibilities: The student will be responsible for conducting research and development activities related to the design and implementation of the proposed Design by Contract framework for vibe coding. Specifically, the student will survey and analyze existing work on program specification, contract systems, and LLM-assisted software development to establish a solid theoretical foundation. The student will design contract representations and workflows suitable for LLM-driven code generation, and implement core components of the framework, including contract extraction, contract-aware prompting, and contract validation mechanisms. The student will integrate the framework with modern development environments and LLM backends, and develop experimental pipelines for evaluation. In addition, the student will curate benchmark programs and real-world vibe coding scenarios, conduct empirical studies to assess effectiveness in terms of correctness, bug reduction, and developer effort, and analyze the resulting data. The student will also be responsible for documenting the framework, maintaining the research prototype, preparing research papers and technical reports, and presenting results in group meetings and academic venues. 
Desired Technical Skills:  
•  Strong programming skills in Python 
•  Familiarity with software engineering principles, including program analysis, testing, and • debugging 
•  Experience with large language models and prompt-based or agent-based code generation 
•  Knowledge of formal specifications, program invariants, or Design by Contract concepts 
•  Experience with static or dynamic program analysis techniques 
•  Experience with software tooling or IDE plugin development (e.g., VS Code) is desirable 
•  Ability to design and conduct empirical evaluations and analyze experimental results 
Desired Course(s):  
EECS 3311 3.00: Software Design 
EECS 4313: Software Engineering Testing 
EECS2030: Advanced Object Oriented Programming 
EECS2031- Software Tools 
Other Desired Qualifications: N/A 

Deep Reinforcement Learning for Adaptive Resource Management in Cognitive Radar 
Professor: Sunila Akbar 
Contact Info: sunila@yorku.ca 
Lab Website: https://lassonde.yorku.ca/users/sunila 
Position Type: Lassonde Undergraduate Research Award (LURA) 
Open Positions: 1 
Project Description: Modern radar systems operate in highly dynamic environments where sensing and tracking demands evolve continuously. Efficient allocation of limited radar resources under such conditions requires adaptive decision-making algorithms that can learn from interaction with complex environments. This project investigates deep reinforcement learning (DRL) approaches for adaptive radar scheduling in cognitive multifunction radar systems 

The research uses a high-fidelity simulation environment that models realistic radar physics, three-dimensional maneuvering targets, and coupled search-and-track task generation. The student will design and evaluate DRL-based scheduling policies that learn to balance competing sensing objectives under varying target densities and system load conditions. Performance will be assessed against classical heuristic schedulers using metrics such as task delay, track loss, and overall system cost. 

As the project progresses, the student will explore advanced DRL architectures capable of reasoning over longer time horizons, including transformer-based models, and investigate the use of rich, multimodal state representations that combine task information, target dynamics, and sensor states. 
This project provides hands-on experience in deep reinforcement learning, sequential decision-making, and adaptive sensing systems, and contributes to research relevant to next-generation intelligent radar and sensing technologies. 
Duties and Responsibilities: The student will contribute to a research project on deep reinforcement learning for adaptive radar scheduling. The student will begin by reviewing background material on radar systems, scheduling problems, and reinforcement learning for sequential decision-making. 

The student will assist in implementing and evaluating learning-based scheduling policies within a high-fidelity radar simulation environment. This includes working with state representations that capture task information, target dynamics, and sensor states, as well as configuring experiments under varying operating conditions such as different target densities and system load levels. 

A core responsibility will be to run simulation experiments, collect and analyze performance metrics (e.g., task delay, tracking performance, and system cost), and compare learning-based methods with classical heuristic schedulers. The student will help interpret results and identify trends related to adaptability and robustness. 
Depending on progress, the student may explore more advanced learning architectures that capture longer-term dependencies or integrate multiple sources of information. Throughout the project, the student will maintain organized code, document their work, and participate in regular meetings with the supervisor. 
The student will contribute to summarizing findings in a short technical report or presentation, gaining practical experience in machine learning research, simulation-based experimentation, and technical communication. 
Desired Technical Skills:  
•  Proficiency in Python programming  
•  Basic understanding of machine learning concepts (e.g., supervised learning, model evaluation) 
•  Strong analytical and problem-solving skills 
Desired Course(s):  
•  Calculus 
•  Object-Oriented Programming (in any programming language) 
Other Desired Qualifications:  
•  Interest in machine learning, artificial intelligence, or signal processing 
•  Ability to work independently while engaging in regular supervisory meetings 
•  Willingness to learn new tools and concepts through self-directed study 
•  Prior experience with data analysis, numerical computing, or scientific programming is an asset but not required 

Adaptive AI for Recognizing Unknown Wireless Signals 
Professor: Sunila Akbar 
Contact Info: sunila@yorku.ca 
Lab Website: https://lassonde.yorku.ca/users/sunila 
Position Type: Lassonde Undergraduate Research Award (LURA) 
Open Positions: 1 
Project Description: Modern wireless technologies such as 5G and emerging 6G systems increasingly rely on advanced AI to understand and manage complex signal environments. A key challenge is enabling machine learning models to recognize familiar signal patterns while also detecting new and previously unseen ones. Traditional models trained on fixed datasets struggle in these settings because real-world data is dynamic, noisy, and constantly evolving. 
This project introduces students to state-of-the-art machine learning techniques used to address these challenges. The student will work with representation learning and contrastive learning to extract meaningful features from raw data, distance-based and embedding-space novelty detection methods to identify unfamiliar patterns, and incremental (continual) learning algorithms that allow models to update over time without forgetting prior knowledge. These approaches are widely used in modern AI systems for open-world learning and adaptive decision-making. 
The student will implement and evaluate these methods using publicly available datasets, focusing on hands-on coding, experimentation, and visualization. No prior knowledge of wireless communication or modulation theory is required. Through this project, the student will gain practical experience with cutting-edge AI techniques that are directly relevant to future 5G/6G wireless systems, as well as broader applications in intelligent sensing and adaptive AI systems. 
Duties and Responsibilities: The student will carry out the technical and experimental components of the project under the guidance of the faculty supervisor. The student will begin by reviewing foundational concepts in machine learning and wireless signal processing relevant to automatic modulation classification, including representation learning, novelty detection, and incremental learning. This phase will establish the theoretical background needed to engage with the research problem. 
The student will implement machine learning models using Python and standard deep learning frameworks. Responsibilities include developing signal preprocessing pipelines, training baseline classifiers, and extending them with open-set and adaptive learning components. The student will implement and compare different feature representations, distance-based novelty detection techniques, and incremental learning strategies. 
A central responsibility will be designing and conducting experiments using benchmark wireless signal datasets. The student will evaluate model performance under varying conditions, such as changes in signal-to-noise ratio, class similarity, and the gradual introduction of previously unseen signal types. This includes selecting evaluation metrics, organizing experimental results, and analyzing performance trends. 
The student will also visualize learned feature representations and model behavior to support qualitative interpretation of results. Throughout the project, the student will maintain clear documentation of code and experiments to ensure reproducibility. Regular meetings with the supervisor will be held to discuss progress, address challenges, and refine the research direction. The student will prepare a final written summary of the project and, where appropriate, contribute to a poster or short report for undergraduate research dissemination. 
Desired Technical Skills:  
•  Proficiency in Python programming 
•  Basic understanding of machine learning concepts (e.g., supervised learning, model evaluation) 
•  Strong analytical and problem-solving skills 
Desired Course(s):  
•  Calculus (single-variable and/or multivariable calculus) 
•  Object-Oriented Programming (in any programming language) 
Other Desired Qualifications:  
•  Interest in machine learning, artificial intelligence, or signal processing 
•  Ability to work independently while engaging in regular supervisory meetings 
•  Willingness to learn new tools and concepts through self-directed study 
• Prior experience with data analysis, numerical computing, or scientific programming is an asset but not required 

Privacy Analysis of Large Language Models (LLM) 
Professor:  Yan Shvartzshnaider   
Contact Info: rhythm.lab@yorku.ca  
Lab Website: https://www.yorku.ca/lassonde/privacy/  
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)  
Open Positions: 2  
Project Description:  The rapid shift toward digital platforms has introduced new privacy and security challenges across various sectors, including workplaces, healthcare, and education. These technologies collect and share vast amounts of data about users and their environments. However, our collective understanding of privacy expectations often lags the rapid advancements in technology and information-handling practices. Researchers worldwide are working to develop new methods to systematically analyze the ethical and privacy implications of these tools, aiming to prevent potential societal harm.  
These issues are particularly pressing with the emergence of Large Language Models (LLMs), which require training on enormous datasets, raising significant concerns about data privacy.  
To address this, the project will explore the ability of LLMs to adhere to context-specific privacy norms, using the theory of Contextual Integrity.  
Looking for students in 3^rd year or higher. 
Duties and Responsibilities:    
Students will assist in analyzing various LLM models.  
Specific tasks include:  
• conducting a comprehensive literature review of existing privacy-related LLM methodologies   
• investigating the model’s properties, such as capacity, alignment, few-shot prompting, and chain-of-thought prompting, with a focus on aligning these models with privacy norms and expectations;  
• contributing to the development of a framework aimed at improving LLM alignment by fine-tuning the models to better align with existing policies and expectations.  
See this paper for reference: https://arxiv.org/abs/2409.03735.  
Desired Technical Skills:   
• Proven programming and data analysis skills  
• Experience working with ML and LLM models.  
Desired Course(s):  Course on ML and LLM, Data analysis.   
Other Desired Qualifications:    
• Experience with data analysis using Jupyter and/or R  
• Interest in usable privacy, critical analysis of systems, and of privacy-related regulations  
• Ability to work independently  

Examination of Privacy Practices in Apps and Digital Platforms 
Professor:  Yan Shvartzshnaider   
Contact Info: rhythm.lab@yorku.ca  
Lab Website: https://www.yorku.ca/lassonde/privacy/  
Position Type: Lassonde Undergraduate Research Award (LURA); NSERC Undergraduate Student Research Award (USRA)  
Open Positions: 2  
Project Description:  Modern sociotechnical systems share and collect vast amounts of information. These systems violate users’ privacy by ignoring the context in which the information is shared and by implementing privacy models that fail to incorporate contextual information norms. Using techniques in natural language processing, machine learning, network, and data analysis, this project is set to explore the privacy implications of mobile apps, online platforms, and other systems in different social contexts/settings.  
To tackle this challenge, the project will operationalize a cutting-edge privacy theory and methodologies to conduct an analysis of existing technologies and design privacy-enhancing tools used in the educational context.  
Looking for students in 3rd year or higher. 
Duties and Responsibilities:  Students will develop privacy-enhancing mechanisms that ensure that information flows in accordance with users’ expectations and established societal norms and design. Specific tasks include: comprehensive literature review of existing methodologies and tools, analysis of privacy policies and regulations, visualization of information collection practices, and design of a web-based interface for analyzing extracted privacy statements to identify vague, misleading, or incomplete privacy statements.  
Desired Technical Skills:  Experience with machine learning, natural language processing techniques, Good programming skills overall, and experience in using Jupyter and/or R for data analysis.  
Desired Course(s):  Software engineering, computer science, and information science students. Note: students with diverse backgrounds, including in technical fields, social sciences and humanities are encouraged to apply.   
Other Desired Qualifications:  HCI design and web development. interest in usable privacy, critical analysis of privacy policies and privacy related regulation.