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--- 2025-26:winter [2025/07/13 18:37] – created sallin
+++ 2025-26:winter [2025/12/12 14:43] (current) – sallin
@@ Line 1: / Line 1: @@
-====== W25 Project Listings ======
+====== W26 Project Listings ======
 [[:instructions-listings-supervisors|Instructions for Faculty Members]]
@@ Line 8: / Line 8: @@
   * You can **subscribe** to this page in order to receive an email whenever the project listing page updates. Only logged in users have access to the “Manage Subscriptions” page tool.  See [[https://www.dokuwiki.org/subscription]]
   * The Table of Contents can be collapsed/expanded.
+Note also that **LURA/USRA deadlines** also take place in the winter; you will want to be mindful of these if you are intending to apply for a summer subsidized project experience.  Information about the LURA/USRA program can be found [[https://lassonde.yorku.ca/research/lura-and-usra-research-at-lassonde|at this link]].
 /** DO NOT EDIT ABOVE THIS LINE PLEASE **/
 ----
-==== LLM4SE (Large Language Models for Software Engineering) ====
-**[added 2025-04-11]**
+==== Gamification of how we learn Discrete Mathematics ====
-**Course:**  {EECS4070 | EECS4080}
+**[added 2025-25-08]**
-**Supervisor:** Zhen Ming (Jack) Jiang
+**Course:** {EECS4480/4080/4088}
-**Supervisor's email address:** zmjiang@yorku.ca
+**Project Description:** This project explores how game-based learning can make Discrete Mathematics more engaging and enjoyable for students. Topics in this course—such as logic, proofs, sets, functions, and number systems—are often challenging because they feel abstract and disconnected from everyday experience. The goal of this project is to design a learning experience that presents these ideas as a series of interactive “quests,” similar to the levels in an adventure game. Students will move through a visual map where each quest represents a key concept in Discrete Mathematics. For example, one quest may involve solving a puzzle to cross a bridge, while another may involve unlocking a gate by understanding logical statements. The project will focus on how storytelling, rewards, and interactive challenges can help students stay motivated and understand concepts more clearly. A simple digital prototype will be created—using design tools such as Figma or basic web interfaces—to demonstrate how the game experience would look and feel. The goal is to create a clear and engaging representation of how gamification could improve learning in Discrete Mathematics.
-**Project Description:**
+**Required/Recommended skills or prerequisites:** Basic understanding of Discrete Mathematics concepts. Familiarity with simple design tools (e.g., PowerPoint, Figma, or Canva). Interest in UI/UX design or game design. Programming knowledge (e.g., JavaScript or Python). Creativity and interest in storytelling or visual design
-Software engineering data (e.g., source code repositories and bug databases) contain a wealth of information about a project's status and history. With the recent advances of large language models (e.g., GPT and BERT) as well as their applications (e.g., ChatGPT or GitHub Copilot), many software engineering tasks can be automated or optimized. In this project, the student(s) will explore and investigate various software engineering applications which can benefit from the use of LLMs.
-**Required skills or prerequisites:**
+**Application Instructions:** Please email your CV, transcript and statement of interest to vashistp@yorku.ca
-  * Major in Computer Science/Software Engineering/Computer Engineering
-  * Third year and up
-  * At least B+ for EECS 3311
-  * Proficient in Python and Java-based programming
-**Recommended skills or prerequisites:**
+----
-Some knowledge in AI would be preferred but not required
+==== Assessing Vulnerabilities in Consumer Robotics: A Case Study of Amazon Astro ====
+**[added 2025-11-08]**
+**Course:** {EECS4480}
+**Supervisor:** Ruba Al Omari
+**Supervisor's email address:** alomari@yorku.ca
+**Project Description:** This project examines vulnerabilities in consumer robotics, using Amazon Astro as a case study. Autonomous household robots are becoming increasingly integrated into daily life, with Astro marketed as a personal assistant that interacts with cloud-based services and Alexa’s ecosystem. While Astro performs local tasks such as navigation, facial recognition, and spatial mapping, it relies heavily on Amazon’s cloud infrastructure, and by extension, external data sources accessed through Alexa’s services to process user commands, retrieve information, and deliver personalized responses.
+This cloud dependence introduces potential security and privacy challenges. If external data or third-party integrations are manipulated, Astro could inadvertently relay or act upon inaccurate or deceptive information. For example, attackers might exploit data voids, gaps in online information ecosystems, to inject misleading content into services or APIs that Astro indirectly depends on. Such vulnerabilities carry heightened risks, as domestic robots operate within private living spaces and interact directly with household members.
+The project will begin with a baseline assessment of Amazon Astro’s broader vulnerability landscape, examining how its sensors, cloud dependencies, and AI-driven decision modules interact and process data. This phase will identify potential weaknesses in data validation, network communication, and contextual inference that could predispose Astro to misinformation or manipulation. Findings from this stage will inform controlled data void experiments, establishing a clearer link between system-level vulnerabilities and information-based attack vectors.
+Building on the findings, the project will explore how data voids can influence Astro’s behaviour and decision-making. Controlled experiments will simulate attacker-created data environments to test whether Astro or related cloud services can be misled by fabricated or adversarial content.
+**Required skills or prerequisites:**
+  * Proficient in Python and in ML libraries (TensorFlow or PyTorch, scikit-learn)
+  * Good understanding of AI Systems (Reinforcement Learning is an asset)
+  * Completion of either EECS3214 or EECS4482
+  * Familiarity with experimental design and system testing
 **Instructions:**
-Send c.v. and unofficial transcript to the supervisor.
+Please email your CV and unofficial transcript to the supervisor. In your email, briefly explain how your coursework or experience aligns with the required skills.
+----
+==== Computer Security Projects ====
+** [added 2025-07-21] **
+** Course:**  {EECS4480}
+** Supervisors:**  Various
+** Supervisor's email address: ** Various
+** Project Description: ** This is a course restricted to students in the fourth year of the Computer Security program. Each project is to be supervised by a faculty member who is skilled in the area of security.  Labs specializing in security at York include http://www.cse.yorku.ca/SecRAY/ and https://www.yorku.ca/lassonde/privacy/; faculty associated with these are therefore potential supervisors.  Students are also encouraged to review prior 4480 projects listed in the archive for potential supervisors; see: https://wiki.eecs.yorku.ca/dept/project-courses/projects.
+** Required skills or prerequisites: **
+Major in Computer Security and Fourth Year
+** Instructions:** Reach out to security faculty to see if they have the capacity to supervise this term.  For questions about eligible security projects, contact the CSec Coordinator (Yan Shvartzshnaider).
 ----
-==== FMOps ====
+==== Analysis of gait data for health applications using machine learning ====
-**[added 2025-04-11]**
+** [added 2025-09-04] **
-**Course:**  {EECS4070 | EECS4080}
+** Course:** {EECS4080 | EECS4088 | EECS4090}
-**Supervisor:** Zhen Ming (Jack) Jiang
+**Supervisors:** Gerd Grau, Garrett Melenka
-**Supervisor's email address:** zmjiang@yorku.ca
+**Supervisor's email address:** grau@yorku.ca, gmelenka@yorku.ca
 **Project Description:**
-Artificial Intelligence is gaining rapid popularity in both research and practice, due to the recent advances in machine learning (ML) research and development. Many ML applications (e.g., Tesla’s autonomous vehicle and Apple’s Siri) are already being used widely in people’s everyday lives. McKinsey recently estimated that ML applications have the potential to create between $3.5 and $5.8 trillion in value annually. Foundation models are large AI models trained on a vast quantity of data at scale. FMs can be used to power a wide range of downstream tasks (e.g., chat bots, code assistants, tutors, etc.). However, there remain many challenges in efficiently training, deploying and monitoring such FM infrastructure. In addition, there is a lack of tools and processes to further develop applications or services on top of such FMs. The goal of this project is to develop engineering tools and best practices to support effectively operationalizing FMs.
-**Required skills or prerequisites:**
+//Project Motivation//
-  * Major in Computer Science/Software Engineering/Computer Engineering
-  * Third year and up
-  * At least B+ for EECS 3311
-  * Proficient in Python and Java-based programming
-**Recommended skills or prerequisites:**
+Understanding human gait is crucial for diagnosing and managing conditions such as arthritis, where mobility is often impaired and subtle changes may indicate disease progression. With objective measurement and analysis of walking patterns, clinicians can tailor interventions and monitor the effectiveness of treatments more precisely. This project seeks to uncover relationships between gait data and restriction levels, paving the way for advanced health applications using modern machine learning techniques.
-Some knowledge in AI would be preferred but not required
+//Project Overview//
+A comprehensive dataset of human gait has been collected from healthy subjects in various walking scenarios, with restricted braces simulating different levels of disease-related mobility impairment. The primary objective is to extract meaningful insights about gait characteristics and restriction levels from this dataset. The student will employ machine learning methods such as Long Short-Term Memory (LSTM) networks for time-domain analysis and Principal Component Analysis (PCA) for frequency-domain exploration.
+//Key Tasks and Responsibilities//
+• Preprocess and clean gait data collected under controlled walking conditions, including different restriction levels modelled by braces.
+• Apply time-domain and frequency-domain analysis techniques to identify distinctive features and trends related to restriction and gait quality.
+• Develop, train, and validate machine learning models (e.g., LSTM, PCA) to classify restriction levels and extract clinically relevant patterns.
+• Interpret model results and visualize findings to support health-related insights and recommendations for mobility assessment.
+• Document all steps of the analysis to ensure reproducibility and clarity for future research and clinical use.
+**Required skills or prerequisites:**
+•	Excellent programming skills (preferably in Python)
+•	Some knowledge of machine learning concepts and algorithms
+•	Interest in health applications and biomedical data analysis
 **Instructions:**
-Send c.v. and unofficial transcript to the supervisor.
+Interested students should email Gerd Grau (grau@yorku.ca) and Garrett Melenka (gmelenka@yorku.ca) with:
+- CV
+- Latest transcript
 ----
-==== AI Safety and AI Alignment  ====
+==== Emotion-Aware Analysis of EECS Course Feedback for Instructional Improvement ====
-**[added 2025-04-11]**
+** [added 2025-08-08] **
+** Course:**  {EECS4080}
-**Course:**  { EECS4080 | EECS4070}
+** Supervisors:**  Pooja Vashith
+** Supervisor's email address: ** vashistp@yorku.ca
-**Supervisor:**  Laleh Seyyed-Kalantari
+** Project Description: ** This project aims to uncover meaningful insights from EECS course evaluations by applying natural language processing (NLP) techniques to student feedback. While most universities collect large volumes of student comments in course evaluations, these are typically underused, especially when embedded in PDF files. Qualitative feedback is often reviewed manually or averaged superficially, leaving behind rich emotional and experiential data that could inform course improvement.
-**Supervisor's email address:**  lsk@yorku.ca
+The primary goal is to build a processing pipeline that extracts, cleans, and analyzes this feedback using both basic sentiment analysis tools (e.g., VADER) and advanced emotion classification models (e.g., GoEmotions). The emotional tone expressed in the feedback will be mapped to different course components such as the instructor, teaching assistant, assessments, and course content. NB: These are already separated in the evaluation structure.
-**Topics of Interest:**
+By comparing the expressiveness and usefulness of simple versus fine-grained emotional analysis, this research will help determine which approaches are more effective at surfacing actionable insights. These insights will be visualized to highlight recurring patterns of sentiment or emotion across course components, such as whether students consistently express frustration about assessments or admiration for certain instructors.
-  * AI safety and AI alignment.
-  * Evaluating disparity in care in large GEMINI dataset.
-**Required skills or prerequisite courses:**
+This project is educational in nature as it equips the student with skills in text analytics, NLP tools, and data visualization while contributing to a broader understanding of how data-driven analysis can support evidence-based teaching and curriculum refinement in academic institutions.
-  * You must have completed a Machine Learning course.  Total GPA over B+ (Preferably A/A+)
+** Required skills or prerequisites: **EECS 4412 or EECS4404
-**Recommended skills or prerequisite courses:**
+Data Analysis, Report Writing, Python programming, web app development, appetite for research
-  * A Deep Learning course is strongly preferred.
-**Instructions:**
+** Instructions:** sen a CV, transcript, statement of interest, and skills to the instructor (Pooja).
-Please fill this form and email me same materials if you are interested: https://docs.google.com/forms/d/e/1FAIpQLSfI_nBfwLKykI0W62J_LJEez-gDrwDxFiSg4RTwNw438v9U1Q/viewform
 ----
+==== Deep Learning and AI in Incident Management ====
+** [added 2025-08-20] **
+** Course:**  {EECS4070 | EECS4080 | EECS4090}
+** Supervisors:**  Marios Fokaefs
+** Supervisor's email address: ** fokaefs@yorku.ca
+** Project Description: ** "Large scale complex software systems generate immense amounts of event data. This creates a significant cognitive and work load for reliability engineers and a number of different challenges. First, the detection of problems becomes problematic and delayed due to the sheer amount of data. When problems are finally detected, their analysis and resolution may take even more time, which translates in loss of revenue. After resolution, the whole cycle must be well-documented, otherwise reproducibility is reduced and unnecessary effort may be invested.
-==== Computer Architecture & Other Topics====
-**[added 2025-04-11]**
+** Required skills or prerequisites: **
-**Course:**  { EECS4080 | EECS4480}
+Student must have:
-**Supervisor:**  Anirudh M Kaushik
+Excellent programming skills (preferably python)
+Good software design skills (must have at least a B+ in EECS3311 or similar courses)
+Some experience with the use of LLM models as a user and as a developer
-**Supervisor's email address:**  kaushika@yorku.ca
-**Topics of Interest:**
+** Instructions:** Interested students must submit to the instructor (Marios):
-Computer architecture, embedded systems, compilers, electronic design automation tools, databases, software analysis
+- CV
+- A statement of interest
+- Latest transcript
+- Other evidence (e.g., software repositories) as proof of skills
+-----
+==== Beyond the Mask: Reimagining Facial Recognition with Deep Transfer Learning ====
+** [added 2025-08-21] **
+** Course:**  {EECS4480}
+** Supervisors:**  Sunila Akbar
+** Supervisor's email address: ** sunila@yorku.ca
+** Project Description: ** "The project involves adapting a state-of-the-art, pretrained deep learning model for facial recognition to accurately identify individuals wearing masks. The student will utilize publicly available datasets and apply data augmentation techniques to simulate mask-wearing scenarios. Transfer learning will be employed to fine-tune the model for this specific task. The performance of the resulting model will be rigorously evaluated against established benchmarks.
+Application Domain: The proposed solution has relevance in environments where mask-wearing is mandatory, such as healthcare facilities, long-term care homes, food service industries, and chemical or pharmaceutical plants. Accurate masked facial recognition can enhance access control, attendance tracking, and safety compliance in these critical settings."
+** Required skills or prerequisites: **
+Python, PyTorch, NumPy, Scikit-learn, OpenCV
+Knowledge of any deep learning model is a plus
+Hyperparameter tuning and optimization
+Understanding of image processing techniques and object detection evaluation metrics
+General interest in computer vision algorithms and applications
+** Instructions:** Send CV, Transcript to the instructor (Sunila).
-**Instructions:**
-Please email the professor.
 ----
-==== Wearable Biomedical Devices ====
-**[added 2025-04-11]**
-**Course:**  {EECS4080 | EECS4070}
+==== Smart Tools for Smarter Brain Scans: Motion Correction in fMRI  ====
-**Supervisor:**  Razieh Salahandish
+**[added 2025-08-08]**
-**Supervisor's email address:**  raziehs@yorku.ca
+**Course:** {EECS4080 | EECS4088}
+**Supervisor:** Sima Soltanpour
-**Instructions:**
+** Supervisor's email address:** simasp@yorku.ca
-Please email the professor.
+** Project Description: ** Functional Magnetic Resonance Imaging (fMRI) is a widely used technique for studying brain function, but its accuracy is often limited by motion caused by head movement during scanning. The artifacts can distort signal measurements and reduce the reliability of data analysis. This project aims to investigate and implement motion correction techniques for fMRI data using both traditional preprocessing pipelines and emerging AI-based approaches. Students will explore how image quality and signal stability can be improved through algorithmic correction. This research-focused project provides an opportunity to gain experience in neuroimaging, signal processing, and the application of machine learning to real-world biomedical data.
+** Recommended skills or prerequisites: **
+  * Python programming
+  * Interest in AI and machine learning for biomedical applications
+** Instructions: ** P Please email your CV and unofficial transcript to the professor (Sima).
 ----
-==== AI-Assisted Biomedical Devices====
-**[added 2025-04-11]**
+==== Fairness and Prediction for Online Algorithms  ====
-**Course:**  {EECS4080 | EEC4070}
+**[added 2025-08-05]**
-**Supervisor:**  Razieh Salahandish
+**Course:** {EECS4080}
-**Supervisor's email address:**  raziehs@yorku.ca
+**Supervisor:** Shahin Kamali
-**Instructions:**
+** Supervisor's email address:** kamalis@yorku.ca
-Please email the professor.
+** Lab Link: ** [[https://sites.google.com/view/shahinkamali/home|here]]
+** Project Description: ** In this course, we will explore recent advances in algorithm design that incorporate fairness considerations. Achieving fairness often requires tools such as randomization and prediction. A typical setting involves scenarios where different groups or agents provide parts of the input, and the goal is to design algorithms that produce solutions that are fair across these groups. Typical applications include data structures (where different groups issue queries) and scheduling and packing problems.
+** Recommended skills or prerequisites: **
+  * Online Computation and Competitive Analysis (Allan Borodin, Ran El-Yaniv)
+** Instructions: ** Please email your CV and unofficial transcript to the supervisor (Shahin).
 ----
-==== Image processing for Software Engineering ====
+====  Seeing Code: Image Processing for Software Engineering  ====
-**[added 2025-04-16]**
+**[added 2025-08-09]**
-**Course:**  {EECS4080}
-**Supervisor:**  Maleknaz Nayebi
+**Course:**  {EECS4088/4080}
+**Supervisor:**  Maleknaz Nayebi (Research Faculty/Associate Director of CIFAL York)
 **Supervisor's email address:**  mnayebi@yorku.ca
@@ Line 161: / Line 275: @@
 Understanding of Machine Learning and Image Processing
+** Project Description: ** Software development is no longer just about text-based code. Developers increasingly share screenshots, diagrams, whiteboard sketches, and UI mockups in forums, documentation, and collaborative tools. But while humans can glance at an image and instantly understand what’s there, most software engineering tools ignore this visual goldmine. This project will explore how image processing and computer vision can be applied to help developers work smarter. Imagine tools that can:
+(i) Automatically read and interpret code snippets from screenshots on Stack Overflow or GitHub issues
+(ii) Detect UI elements and workflows from mobile app screenshots for automated testing
+(iii) Extract architecture diagrams from PDFs and turn them into editable models
+(iv) Identify errors, warnings, or environment details from IDE screenshots to improve bug reports
+You’ll work with a small dataset of real-world images from developer communities, apply OCR (Optical Character Recognition), object detection, and layout analysis, and experiment with AI techniques to transform images into structured, machine-readable insights.
+**Why This is Cool:**
+(a) You’ll be working at the intersection of computer vision and software engineering — an emerging research frontier.
+(b) You will work along with MSc and PhD students who were starting from where you are right now ... being my undergrad student for 4080/4088
+(c) The project is grounded in real developer problems and could lead to tools that people actually use, and you may get to work with some of our industry partners.
+(d) You’ll gain experience with image processing libraries (like OpenCV, Tesseract), Python-based pipelines, and possibly even fine-tuning vision-language models.
+(e) There’s potential for research publication or open-source release if results are promising.
 **Instructions:**
-Please email your CV and Transcripts to the professor.
+Please email your CV and Transcripts to the professor (Maleknaz).
 ----
@@ Line 169: / Line 296: @@
 ==== Using Generative AI for Compliance Analysis in Health Care ====
-**[added 2025-04-16]**
+**[added 2025-08-09]**
-**Course:**  {EECS4080}
-**Supervisor:**  Maleknaz Nayebi
+**Course:**  {EECS4080/4088}
+**Supervisor:**  Maleknaz Nayebi (Research Faculty/Associate Director of CIFAL York)
 **Supervisor's email address:**  mnayebi@yorku.ca
@@ Line 181: / Line 309: @@
 **Recommended skills or prerequisites:**
-Understanding of Machine Learning and Image Processing
+Understanding of Machine Learning, prompt engineering, and GenAI
+**Project Description:** Health care is one of the most highly regulated industries in the world. Every new medical device, digital health tool, or clinical process must comply with complex rules and standards — from privacy laws like HIPAA to advertising regulations and medical ethics guidelines. The challenge? These rules are buried in long, dense, and ever-changing documents that are hard for humans to keep up with. This project will explore how Generative AI can act as an intelligent assistant for compliance analysis. Imagine a system that can:
+(i) Read hundreds of pages of regulatory text and highlight the exact rules relevant to a given health care product or service
+(ii) Compare a draft document or ad campaign against regulatory requirements to spot potential violations
+(iii) Provide plain-language summaries of compliance risks for non-experts in health care teams
+(iv) Learn from feedback to improve over time
+You’ll work with real-world health care regulations and guidance documents, build AI pipelines that integrate text extraction, retrieval-augmented generation (RAG), and natural language understanding, and evaluate how well AI can assist compliance officers and health care innovators.
+**Why This is Cool:**
+(a) You’ll be applying AI to a real-world, high-impact domain where mistakes can affect patient safety and legal outcomes
+(b) You’ll learn to work with state-of-the-art Generative AI tools (like OpenAI, Hugging Face models) for specialized, high-stakes tasks
+(c) The project bridges machine learning, information retrieval, and domain-specific knowledge — skills that are highly sought after in industry
+(d) Your work could inform research papers, prototypes, and real tools that help make health care safer and more efficient
 **Instructions:**
-Please email your CV and Transcripts to the professor.
+Please email your CV and Transcripts to the professor (Maleknaz).
 ----
+==== The impact of quantity and quality of feedback on RLHF  ====
+**[added 2025-08-08]**
+**Course:** {EECS4080}
+**Supervisor:** Ines Arous
+** Supervisor's email address:** inesar@yorku.ca
+** Lab Link:** [[https://inesarous.github.io/|here]]
+** Project Description: ** Reinforcement learning with human feedback (RLHF) has become widely used to enhance the performance of large language models. These methods rely heavily on the availability of large amounts of high-quality human feedback. Yet, it is unclear how the quantity and quality of feedback influence the performance of language models. This project aims to address these gaps by analyzing the relationship between the properties of human feedback and the framework of RLHF, with a particular focus on its core component—the reward model. The student will conduct an empirical evaluation on a summarization task, exploring how different quantities and qualities of feedback impact the effectiveness of the reward model in RLHF. The student will also investigate various sampling strategies to identify the minimum feedback needed for comparable performance with a reward model trained on a large dataset. To examine the impact of feedback quality, the student will simulate scenarios where the feedback is noisy and evaluate the reward model's accuracy as the quality of annotations is varied.
+** Required skills or prerequisites: **
+  * Major in Computer Science/Software Engineering/Computer Engineering
+  * Third year and up
+  * You must have completed a Machine Learning/ Artificial Intelligence course.
+  * Total GPA over B+ (Preferably A/A+)
+** Instructions: ** Please email your CV and Transcripts to the professor (Ines).
+----
+==== Guidelines for Human Evaluation of Generated Answers by LLMs  ====
+**[added 2025-08-05]**
+**Course:** {EECS4080}
+**Supervisor:** Ines Arous
+** Supervisor's email address:** inesar@yorku.ca
+** Lab Link:** [[https://inesarous.github.io/|here]]
+** Project Description: ** The project will use theories from behavioral science and psychology to derive guidelines for human evaluation of generated answers by LLMs. The goal is to leverage theories such as the power analysis to quantify the number of participants. Other theories, such as construct validity (measuring intended personalization traits), content validity (ensuring coverage of relevant personalization dimensions), and ecological validity (reflecting real-world use cases), will be explored.
+** Required skills or prerequisites: **
+  * You must have completed a Machine Learning/NLP course.
+  * Total GPA over B+ (Preferably A/A+)
+** Instructions: ** Please send your CV, transcript and statement of interest to the professor (Ines).
+----
+==== Comparison of LLM personalization techniques on domain specific applications  ====
+**[added 2025-08-05]**
+**Course:** {EECS4088}
+**Supervisor:** Ines Arous
+** Supervisor's email address:** inesar@yorku.ca
+** Lab Link:** [[https://inesarous.github.io/|here]]
+** Project Description: ** The project will compare between current LLM personalization techniques, such as chain of thought prompting, retrieval augmented generation (RAG), and reinforcement learning with human feedback (RLHF), on domain-specific tasks using existing datasets.
+** Required skills or prerequisites: **
+  * You must have completed a Machine Learning or a deep learning course.
+  * Total GPA over B+ (Preferably A/A+)
+** Instructions: ** Send your CV, transcripts, and previous ML-related code to the professor (Ines).
+----
+==== Vision Transformer-Based Pipelines for Biomedical Image Analysis and Secure Data Collection  ====
+**[added 2025-08-05]**
+**Course:** {EECS4080 | EECS4090}
+**Supervisor:** Navid Mohaghegh
+** Supervisor's email address:** navidmo@yorku.ca
+** Project Description: ** This project focuses on applying state-of-the-art deep learning models such as Vision Transformers (ViT) and hybrid CNN-transformer architectures (e.g., OWL-ViT) to biomedical image datasets (e.g., retinal scans, ultrasound). The student will build a scalable inference pipeline and benchmark performance against traditional CNN baselines.
+** Recommended skills or prerequisites: **
+  * Python, PyTorch, OpenCV
+  * Experience with deep learning models and image processing such as YOLO
+  * Interest in biomedical applications of AI
+  * Interest in privacy aware and secure data collection and processing
+** Instructions: ** Please email your CV and unofficial transcript to the supervisor (Navid).
+----
+==== AI-Driven Next-Generation Firewall and Network Anomaly Detection  ====
+**[added 2025-08-05]**
+**Course:** {EECS4080 | EECS4090}
+**Supervisor:** Navid Mohaghegh
+** Supervisor's email address:** navidmo@yorku.ca
+** Project Description: ** This project involves the design and implementation of a prototype next-generation network and application level firewalls and API gateways that leverages AI models for real-time anomaly detection across diverse network and application traffic sources. The system will integrate stream processing, feature extraction, and transformer-based models for behavioural analysis. Students will implement components such as custom packet inspection, classification pipelines, and zero-day attack detection using labeled datasets for supervised learning and simulated traffic along with unsupervised learning methods. We also develop lightweight federated learning framework to detect distributed attacks such as coordinated port scans, botnet behaviour, and insider threats. It will involve experimenting with decentralized model training, edge device simulations, and privacy-preserving protocols (e.g., differential privacy or homomorphic encryption).
+** Recommended skills or prerequisites: **
+  * Python, Scapy, Wireshark, Zeek, Suricata, hands-on Linux and FreeBSD
+  * ML libraries (PyTorch, TensorFlow, scikit-learn and R)
+  * Experience with networking or cybersecurity is a plus
+  * Familiarity with FL frameworks (Flower, FedML, or similar)
+  * Interest in privacy-preserving ML or cyber defense
+** Instructions: ** Please email your CV and unofficial transcript to the supervisor (Navid).
+----
+==== Fair or Fake? Toward Building Fair and Explainable AI Models for Fake Content Detection ====
+**[added 2025-08-05]**
+**Course:**  {EECS4080}
+**Supervisor:** Mona Nasery
+** Supervisor's email address:** monan@yorku.ca
+** Project Description: ** This project focuses on building and analyzing AI models for misinformation detection, with a particular emphasis on bias and explainability. Depending on the student’s interest, the project may focus on:
+  * Text-based fake news detection using transformer models,
+  * Deepfake detection (image/video) using vision-language or video-based models.
+In both cases, the goal is to examine whether the AI behaves differently across content types or user traits (e.g., political leaning, race, gender) and to use explainability tools (e.g., SHAP, attention maps, visual saliency) to understand and potentially improve model behavior.
+The student will prototype a system that not only makes predictions, but also provides interpretable insights and evaluates fairness, contributing toward the development of more responsible AI systems for misinformation detection.
+** Required skills or prerequisites: **
+  * Good Python programming skills
+  * Understanding of machine learning and deep learning (You must have completed a Machine Learning course)
+** Recommended skills or prerequisites: **
+  * Familiarity with frameworks like PyTorch, TensorFlow, or HuggingFace Transformers
+  * Comfort working with real-world datasets and performing data preprocessing
+  * Some background in NLP (for fake news) or computer vision (for deepfakes) is an asset"
+** Instructions: ** Please email your CV and unofficial transcript to the supervisor (Mona).
+----
+==== Understanding Vibe Coding: UX Perspectives on AI-Driven Software Generation ====
+**[added 2025-07-22]**
+**Course:**  {EECS4080}
+**Supervisor:** Emily Kuang
+** Supervisor's email address:** emily.kuang@lassonde.yorku.ca
+** Project Description: ** Vibe coding is a new paradigm in software development where users describe what they want, and AI tools generate the code. This “prompt-to-code” workflow is part of a growing shift toward low-code/no-code platforms, making it easier and faster to prototype software. This project investigates how UX professionals engage with vibe coding tools, a perspective that has been largely overlooked in favour of software developer-focused studies. The main tasks include:
+  * Recruiting and running the study with UX professionals
+  * Collecting and analyzing study data
+** Required skills or prerequisites: **
+  * Completed TCPS 2: CORE-2022 (Course on Research Ethics)
+  * Ability to conduct user studies and administer surveys
+  * Data collection and basic data analysis (e.g., interpreting SUS scores, coding qualitative responses)
+** Recommended skills or prerequisites: **
+  * Experience with web design tools and languages (e.g., Figma, JavaScript, HTML/CSS)
+  * Familiarity with AI-assisted development tools (e.g., Replit, Anima, GitHub Copilot)
+  * EECS 3461 and EECS 4441 or equivalent
+** Instructions: ** Email your CV and unofficial transcript to the professor. Put “EECS 4080 Inquiry” in the subject line.
+----
+==== Using Mixed Reality to Support Programming in CS1 ====
+**[added 2025-08-05]**
+**Course:**  {EECS4080 | EECS4088}
+**Supervisor:** Meiying Qin
+** Supervisor's email address:** mqin@yorku.ca
+** Project Description: ** Debugging is one of the most important skills for computer science students. However, first-year students are usually not comfortable with working with a debugger. In order to help ease the process for first-year students, we plan to write an application that can visualize the process by animating the variable manipulated, either on a screen or using mixed reality. In this project, students will have the opportunity to gain hands-on experience in both designing and implementing a software application. Students will gain experience in mixed reality.
+** Required skills or prerequisites: **
+  * Familiarity with C#
+** Instructions: ** Please email Meiying (mqin@yorku.ca) your CV and transcript, with a statement of why you are interested in the project.
+----
+==== Building Robots Tutors ====
+**[added 2025-08-05]**
+**Course:**  {EECS4080 | EECS4088}
+**Supervisor:** Meiying Qin
+** Supervisor's email address:** mqin@yorku.ca
+** Project Description: ** The research is to innovate cost-effective robot tutors that are accessible on a broader scale, fostering inclusive and impactful learning experiences. Robot tutors have demonstrated effectiveness in aiding students, yet thier widespread adoption faces hurdles due to high costs and limited scalability. Current robot tutors are often impractical for widespread use in universities due to their expenses. This project seeks to overcome these limitations by developing an affordable robot tutor. The objective is to create a solution that meets the education needs of university students without imposing financial constraints.
+** Required skills or prerequisites: **
+  * You may choose work with hardware or software; skills in either domain in recommended.
+** Instructions: ** Please email Meiying (mqin@yorku.ca) your CV and transcript, with a statement of why you are interested in the project.
+----
+==== Sims for University Life ====
+**[added 2025-08-05]**
+**Course:**  {EECS4080 | EECS4088}
+**Supervisor:** Meiying Qin
+** Supervisor's email address:** mqin@yorku.ca
+** Project Description: ** One of the biggest challenges that first-year students face is the transition from high school to university. This is expected to be more pronounced once the York Markham campus opens, as all courses will use the flipped-class model. In this model, students are required to be more active in learning and preview the content before each class in order to stay on track. In order to assist first-year students in making a smoother transition even before school starts, we plan to release a game that simulates the life of a computer science student at the Markham campus to provide students with a preview of university life. In this project, students have the opportunity to gain hands-on experience in both designing and implementing a game.
+** Required skills or prerequisites: **
+  * Familiar with game development and Unity
+** Instructions: ** Please email Meiying (mqin@yorku.ca) your CV and transcript, with a statement of why you are interested in the project.
+----
+==== Make An Accessible Role Playing Game ====
+**[added 2025-08-05]**
+**Course:**  {EECS4080 }
+**Supervisor:** Sonya Allin
+** Supervisor's email address:** sallin@yorku.ca
+** Project Description: ** This project requires development of a modular, extensible role playing game with accessibility features designed for blind users and users with low vision. Preliminary prototyping work exists in Java; you are welcome to build a clone in Python. Desirable accessiblity features include directional sound (via the openAL library), a customizable map and customizable voices for RPG narration. The goal of this project is to create reusable modules that others can use to develop inclusive games.
+** Required skills or prerequisites: **
+  * Proficiency in Java and/or Python and associated frameworks (e.g., JUnit, OpenAL)
+  * Familiarity with version control (e.g., Git/GitHub)
+  * Strong debugging and testing skills
+** Recommended skills or prerequisites: **
+  * Familiarity with Human-Centered Design or Accessibility Principles (e.g., WCAG, Universal Design)
+  * EECS 3461 and EECS 4441 or equivalent
+  * Experience with API integration
+** Instructions: ** Please email Sonya (sallin@yorku.ca) your CV and transcript, with a statement of why you are interested in the project.  Use the subject line "**[EECS4080] Independent Project Inquiry**".
+----
+==== Make An Accessible Arcade Game ====
+**[added 2025-08-05]**
+**Course:**  {EECS4080}
+**Supervisor:** Sonya Allin
+** Supervisor's email address:** sallin@yorku.ca
+** Project Description: ** This project requires development of an arcade game with accessibility features designed for blind users and users with low vision. Preliminary prototyping work exists in Java; you are welcome to build a clone in Python. Accessiblity features include directional sound and a haptic game controller. The goal of this project is to create reusable modules that others can use to develop inclusive games.
+** Required skills or prerequisites: **
+  * Proficiency in Java and/or Python and associated frameworks (e.g., JUnit, OpenAL)
+  * Familiarity with version control (e.g., Git/GitHub)
+  * Strong debugging and testing skills
+** Recommended skills or prerequisites: **
+  * Familiarity with Human-Centered Design or Accessibility Principles (e.g., WCAG, Universal Design)
+  * EECS 3461 and EECS 4441 or equivalent
+  * Experience with API integration
+** Instructions: ** Please email Sonya (sallin@yorku.ca) your CV and transcript, with a statement of why you are interested in the project. Use the subject line "**[EECS4080] Independent Project Inquiry**".
+----
+==== Understanding Vibe Coding: UX Perspectives on AI-Driven Software Generation ====
+**[added 2025-07-22]**
+**Course:**  {EECS4080}
+**Supervisor:** Emily Kuang
+** Supervisor's email address:** emily.kuang@lassonde.yorku.ca
+** Project Description: ** Vibe coding is a new paradigm in software development where users describe what they want, and AI tools generate the code. This “prompt-to-code” workflow is part of a growing shift toward low-code/no-code platforms, making it easier and faster to prototype software. This project investigates how UX professionals engage with vibe coding tools, a perspective that has been largely overlooked in favour of software developer-focused studies. The main tasks include:
+  * Recruiting and running the study with UX professionals
+  * Collecting and analyzing study data
+** Required skills or prerequisites: **
+  * Completed TCPS 2: CORE-2022 (Course on Research Ethics)
+  * Ability to conduct user studies and administer surveys
+  * Data collection and basic data analysis (e.g., interpreting SUS scores, coding qualitative responses)
+** Recommended skills or prerequisites: **
+  * Experience with web design tools and languages (e.g., Figma, JavaScript, HTML/CSS)
+  * Familiarity with AI-assisted development tools (e.g., Replit, Anima, GitHub Copilot)
+  * EECS 3461 and EECS 4441 or equivalent
+** Instructions: ** Email your CV and unofficial transcript to the professor. Put “EECS 4080 Inquiry” in the subject line.
+----
+==== Enhancing Usability Testing Through Human-AI Collaboration ====
+**[added 2025-07-22]**
+**Course:**  {EECS4080}
+**Supervisor:** Emily Kuang
+** Supervisor's email address:** emily.kuang@lassonde.yorku.ca
+** Project Description: ** Usability testing is a critical part of designing user-friendly interactive systems. Yet, analyzing usability test videos is often time-consuming and labor-intensive. With recent advancements in AI, there is growing interest in supporting usability analysis through AI-generated insights delivered via natural language. This project focuses on extending the functionality of an existing web-based tool that supports usability testing through on-demand AI-generated suggestions. The main tasks are:
+  * Designing and implementing new features that improve how users interact with and interpret AI feedback
+  * Testing and refining the tool to ensure it supports effective human-AI collaboration
+** Required skills or prerequisites: **
+  * Proficiency in JavaScript and TypeScript and web development frameworks (e.g., React, Node.js)
+  * Ability to deploy and manage web applications on cloud services (e.g., DigitalOcean or similar)
+  * Familiarity with version control (e.g., Git/GitHub)
+  * Strong debugging and testing skills
+** Recommended skills or prerequisites: **
+  * Understanding of usability testing and UX principles
+  * Experience with API integration (e.g., for AI models or server communication)
+  * EECS 3461 and EECS 4441 or equivalent
+** Instructions: ** Email your CV and unofficial transcript to the professor. Put “EECS 4080 Inquiry” in the subject line.
+----
+==== Envisioning Inclusive Communication Tools for People with Speech Impairments ====
+**[added 2025-07-22]**
+**Course:**  {EECS4080}
+**Supervisor:** Emily Kuang
+** Supervisor's email address:** emily.kuang@lassonde.yorku.ca
+** Project Description: ** This project explores the future of assistive technologies that support individuals with speech impairments, such as those caused by cerebral palsy, multiple sclerosis, or autism. While Augmentative and Alternative Communication (AAC) tools exist, they are often limited by high costs, rigid interfaces, or a lack of adaptability to individual needs. The goal of this project is to investigate current communication technologies, identify their limitations, and generate ideas for more inclusive and flexible mobile applications. Emphasis will be placed on how emerging technologies, such as intelligent interfaces, context-aware design, and customizable user workflows, can improve the communication experience for users with speech impairments. The main tasks include:
+  * Literature and Technology Review: Analyze existing AAC systems and related research, identify common barriers and usability challenges.
+  * Ideation and Concept Development: Propose improvements or new interaction designs.
+  * Design Artifacts: Create mockups or design sketches to illustrate key ideas, reflect on future implementation pathways.
+** Required skills or prerequisites: **
+  * Literature review
+  * Ability to create wireframes, mockups, or design concepts using tools like Figma
+** Recommended skills or prerequisites: **
+  * Human-Centered Design or Accessibility Principles (e.g., WCAG, Universal Design)
+  * Understanding of Context-Aware Systems: how devices use environmental data (e.g., GPS, cameras, or sensor input) to adapt behaviour
+  * EECS 3461 and EECS 4441 or equivalent
+** Instructions: ** Email your CV and unofficial transcript to the professor. Put “EECS 4080 Inquiry” in the subject line.
+----
+==== Comparison of Two Single-Switch Scanning Methods for Target Selection ====
+**[added 2025-07-21]**
+**Course:**  {EECS4080}
+**Supervisor:**  Scott MacKenzie
+**Supervisor's email address:**  mack@yorku.ca
+**Project Description:** This project is an empirical research investigation (a user study)
+of two methods for target selection using single-switch selection.  The work
+involves doing a literature review, configuring the experiment apparatus
+(provided), doing a user study, analysing data, and writing a research report. The
+domain is accessible computing.  The input methods are "hand pressure" (squeezing a
+rubber bulb) and "foot switch".  The software is mostly written (in Java) but may
+require some modifications.  The task is modeled after Fitts' law.  This work
+extends previous research.  See https://www.yorku.ca/mack/icchp2024a.html for details.
+**Required skills or prerequisites:**
+EECS 4080 prerequisites;
+EECS 4441 or equivalent
+**Instructions: **
+Email your CV and transcript to the professor.  Put "EECS 4080 Inquiry" in the subject-line.
+----
+==== Scalable ML Inference with Serverless Computing ====
+**[added 2025-07-17]**
+**Course:**  {EECS4088 | EECS4070 | EECS4080}
+**Supervisor:**  Hamzeh Khazaei
+**Supervisor's email address:**  hkh@yorku.ca
+**Project Description:**
+This project examines how to efficiently serve large machine learning models using modern cloud technologies.
+Students on this project will focus on serverless computing to build fast, scalable, and cost-effective model-serving pipelines using serverless platforms (e.g., AWS Lambda, Cloud Run).
+This is a hands-on opportunity to work with real-world ML systems and cutting-edge cloud technologies.
+**Required skills or prerequisites:**
+   - Good understanding of Machine Learning Concepts
+   - Good understanding of Computing Systems (OS and Cloud)
+   - Good command of the Python language
+**Recommended skills or prerequisites:**
+   - Familiarity with Serverless Computing, Microservices Architecture, Distributed Computing, and Virtualization, in general, is a plus.
+**Instructions:**
+Please email your CV and most recent transcripts to the supervisor.
+----
+==== Distributed Training of ML Models on Cloud ====
+**[added 2025-07-17]**
+**Course:**  {EECS4088 | EECS4070 | EECS4080}
+**Supervisor:**  Hamzeh Khazaei
+**Supervisor's email address:**  hkh@yorku.ca
+**Project Description:**
+This project examines the efficient training of large machine learning models using modern cloud technologies.
+Students on this project will investigate how to leverage distributed training techniques and cloud infrastructure to train large models in an energy-efficient and performant way.
+This will require training using cloud GPUs/TPUs with a focus on performance and green computing.
+This is a hands-on opportunity to work with real-world ML systems and cutting-edge cloud technologies.
+**Required skills or prerequisites:**
+   - Good understanding of Machine Learning Concepts
+   - Good understanding of Computing Systems (OS and Cloud)
+   - Good command of the Python language
+**Recommended skills or prerequisites:**
+   - Familiarity with Serverless Computing, Microservices Architecture, Distributed Computing, and Virtualization, in general, is a plus.
+**Instructions:**
+Please email your CV and most recent transcripts to the supervisor.
+----
+==== LLM4SE (Large Language Models for Software Engineering) ====
+**[added 2025-07-15]**
+**Course:**  {EECS4070 | EECS4080}
+**Supervisor:** Zhen Ming (Jack) Jiang
+**Supervisor's email address:** zmjiang@yorku.ca
+**Project Description:**
+Software engineering data (e.g., source code repositories and bug databases) contain a wealth of information about a project's status and history. With the recent advances of large language models (e.g., GPT and BERT) as well as their applications (e.g., ChatGPT or GitHub Copilot), many software engineering tasks can be automated or optimized. In this project, the student(s) will explore and investigate various software engineering applications which can benefit from the use of LLMs.
+**Required skills or prerequisites:**
+  * Major in Computer Science/Software Engineering/Computer Engineering
+  * Third year and up
+  * At least B+ for EECS 3311
+  * Proficient in Python and Java-based programming
+**Recommended skills or prerequisites:**
+Some knowledge in AI would be preferred but not required
+**Instructions:**
+Send c.v. and unofficial transcript to the supervisor.
+----
+==== FMOps ====
+**[added 2025-07-15]**
+**Course:**  {EECS4070 | EECS4080}
+**Supervisor:** Zhen Ming (Jack) Jiang
+**Supervisor's email address:** zmjiang@yorku.ca
+**Project Description:**
+Artificial Intelligence is gaining rapid popularity in both research and practice, due to the recent advances in machine learning (ML) research and development. Many ML applications (e.g., Tesla’s autonomous vehicle and Apple’s Siri) are already being used widely in people’s everyday lives. McKinsey recently estimated that ML applications have the potential to create between $3.5 and $5.8 trillion in value annually. Foundation models are large AI models trained on a vast quantity of data at scale. FMs can be used to power a wide range of downstream tasks (e.g., chat bots, code assistants, tutors, etc.). However, there remain many challenges in efficiently training, deploying and monitoring such FM infrastructure. In addition, there is a lack of tools and processes to further develop applications or services on top of such FMs. The goal of this project is to develop engineering tools and best practices to support effectively operationalizing FMs.
+**Required skills or prerequisites:**
+  * Major in Computer Science/Software Engineering/Computer Engineering
+  * Third year and up
+  * At least B+ for EECS 3311
+  * Proficient in Python and Java-based programming
+**Recommended skills or prerequisites:**
+Some knowledge in AI would be preferred but not required
+**Instructions:**
+Send c.v. and unofficial transcript to the supervisor.
+----
+==== AI Safety and AI Alignment  ====
+**[added 2025-07-15]**
+**Course:**  { EECS4080 | EECS4070}
+**Supervisor:**  Laleh Seyyed-Kalantari
+**Supervisor's email address:**  lsk@yorku.ca
+**Topics of Interest:**
+  * AI safety and AI alignment.
+  * Evaluating disparity in care in large GEMINI dataset.
+**Required skills or prerequisite courses:**
+  * You must have completed a Machine Learning course.  Total GPA over B+ (Preferably A/A+)
+**Recommended skills or prerequisite courses:**
+  * A Deep Learning course is strongly preferred.
+**Instructions:**
+(To be updated ASAP!)
+----
 ==== LLM-augmented Software Quality Assurance Techniques ====
-**[added 2025-04-16]**
+**[added 2025-07-15]**
 **Course:**  {EECS4070}
@@ Line 208: / Line 897: @@
 ==== Benchmarking LLM-Based IDEs for Repository-Level Code Generation ====
-**[added 2025-04-16]**
+**[added 2025-07-15]**
 **Course:**  {EECS4080}
@@ Line 232: / Line 922: @@
 ==== Evaluating Large Language Models on Code Behavior and Execution Analysis ====
-**[added 2025-04-16]**
+**[added 2025-07-15]**
 **Course:**  {EECS4080}
@@ Line 251: / Line 942: @@
 **Instructions:**
 Send the CV and transcript to the professor.
 ----
-==== Evaluation of Single-switch Scanning Keyboards ====
-**[added 2025-04-17]**
+==== Tethered Quadcopter Development  ====
+**[added 2025-07-15]**
 **Course:**  {EECS4080}
-**Supervisor:** Scott MacKenzie
+**Supervisor:** Michael Jenkin
-**Supervisor's email address:** mack@yorku.ca
+**Supervisor's email address:** jenkin@yorku.ca
 **Project Description:**
-This project aims to evaluate the capabilities of Large Language Models (LLMs) in understanding and analyzing code behavior based on execution results. While LLMs have shown strong performance in code generation and completion, their ability to reason about dynamic execution—such as interpreting outputs, diagnosing runtime errors, and explaining unexpected behaviors, in general, remains underexplored. We will develop a benchmark dataset containing code snippets paired with execution outcomes (e.g., outputs, errors, return values) and assess LLMs on tasks including output prediction, behavior explanation, and error diagnosis. The evaluation will consider both quantitative metrics (e.g., accuracy) and qualitative aspects (e.g., reasoning depth), offering insights into the strengths and limitations of current LLMs in execution-aware code analysis.
+Having an ‘eye in the sky’ can enhance considerably the sensing ability of a ground-based robot. This project involves planning and constructing a tethered (10m) drone to operate from a moving platform to provide sensor data beyond the line of sight of the ground-based robot. This will involve modifying an existing quadcopter design to support tethered operation and dealing with tether management,
 **Required skills or prerequisites:**
-Skill in designing and conducting a user study, as per EECS 4441 (Human-Computer Interaction) or EECS 4443 (Mobile User Interfaces)
+  - Ability to work independently and in groups
+  - Good Python programming skills
+  - interest in building/flying a tethered quadcopter
+  - Knowledge of/interest in ROS2 would be helpful
 **Recommended skills or prerequisites:**
-See above
+None beyond 4080 prerequisites
 **Instructions:**
-Submit CV.  Include information on when EECS 4441 or EECS 4443 was taken, noting the grade received and the title of the course project.
+Contact Michael Jenkin by email (jenkin@yorku.ca) if interested.
 ----
 ==== Autonomous Aquatic Robot  ====
+**[added 2025-07-15]**
-**[added 2025-04-18]**
 **Course:**  {EECS4080}
@@ Line 310: / Line 1008: @@
-==== Enhanced avatar for human-robot interaction  ====
+==== Enhanced Avatar for Human-Robot Interaction  ====
+**[added 2025-07-15]**
-**[added 2025-04-18]**
 **Course:**  {EECS4080}
@@ Line 339: / Line 1038: @@
 ----
-==== Indoor navigation for an omnidirectional robot  ====
+==== Indoor Navigation for an Omnidirectional Robot  ====
+**[added 2025-07-15]**
-**[added 2025-04-18]**
 **Course:**  {EECS4080}
@@ Line 368: / Line 1068: @@
 ----
-==== Leveraging local LLMs for interactive office assistance  ====
+==== Leveraging Local LLMs for Interactive Office Assistance  ====
+**[added 2025-07-15]**
-**[added 2025-04-18]**
 **Course:**  {EECS4080}
@@ Line 397: / Line 1098: @@
 ----
-==== Survey of gaming applications and heads-up diegetic displays ====
-** [added 2025-05-05] **
-**Course:** {EECS4080, EECS4070, EECS4480}
-**Supervisor:** Scott MacKenzie
-**Supervisor's email address:** mack@yorku.ca
-**Project Description:** Survey of gaming applications and heads-up diegetic displays
-**Required skills or prerequisites:** Avid gamer, skill in doing literature searches, skill in writing-up a literature review, comfortable using Overleaf and LaTeX.
-Recommended skills or prerequisites: See above
-----
-==== Tethered Quadcopter Development  ====
-**[added 2025-04-18]**
-**Course:**  {EECS4080}
-**Supervisor:** Michael Jenkin
-**Supervisor's email address:** jenkin@yorku.ca
-**Project Description:**
-Having an ‘eye in the sky’ can enhance considerably the sensing ability of a ground-based robot. This project involves planning and constructing a tethered (10m) drone to operate from a moving platform to provide sensor data beyond the line of sight of the ground-based robot. This will involve modifying an existing quadcopter design to support tethered operation and dealing with tether management,
-**Required skills or prerequisites:**
-  - Ability to work independently and in groups
-  - Good Python programming skills
-  - interest in building/flying a tethered quadcopter
-  - Knowledge of/interest in ROS2 would be helpful
-**Recommended skills or prerequisites:**
-None beyond 4080 prerequisites
-**Instructions:**
-Contact Michael Jenkin by email (jenkin@yorku.ca) if interested.