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2024-25:summer [2024/04/15 23:28] – [Strengthening the Security of Autograders] baljko2024-25:summer [2025/04/09 15:05] (current) – [Proposed Projects for Summer 2024 .] baljko
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 +==== AI-Assisted Heath Assessment: Front-End and Back-End Software Development ====
 +
 +**[added 2024-04-15]**
 +
 +**Course:**  EECS4080
 +
 +**Supervisor:**  Ebrahim Ghafar-Zadeh
 +
 +**Supervisor's email address:** egz@yorku.ca
 +
 +**Project Description:** 
 +
 +The development of novel point-of-care diagnostic tools for health quality monitoring has recently attracted researchers' attention, particularly utilizing the mobile cell phone platform. The availability of sensors such as tiny cameras in cell phones has created an attractive platform for detecting various health-related parameters. We have recently developed a technology for the assessment of inflammatory diseases by combining medical and engineering techniques. This technology leverages Deep Learning methods to detect features extracted from images captured from microscopes.  In this project, there are several opportunities for computer science and software students to join this multidisciplinary project, including:      Development of a Mobile App to capture images and perform pre-image processing before transferring them to cloud-based software.     Enhancement of DNN (Deep Neural Network) models by studying various methods such as Yolo, Detectron, etc., and applying these new models to improve the accuracy of disease detection.
 +
 +**Required skills or prerequisites:**  
 +
 +  *  Students who have completed their third year and are eligible for capstone projects or other fourth-year projects (e.g. 4080) are eligible for this project.
 +  * Specific skills required: This project offers a great opportunity for students interested in applying computer science to medical and life science applications. Proficiency in programming (Python or other comparable programming languages), familiarity with fundamental concepts of App development, and some experience with Artificial Intelligence will be sufficient.
 +
 +**Recommended skills or prerequisites:**
 +
 +  * Knowledge of multi-thread programming and interprocess communication protocols will be useful
 +
 +**Instructions:**
 +The interested students are required to submit their CV along with an abstract   Max 300 Words) describing their interests and plans for their future job, as well as a self-description if they require specific accommodations for taking this course. All students, including underrepresented groups, are most welcome to take this course and join my research team.
 +
 +----
 +
 +==== Design and Implementation of Knowledge Graph Framework ====
 +
 +**[added 2024-04-15]**
 +
 +**Course:**  EECS4080
 +
 +**Supervisor:**  Kostas Kontogiannis
 +
 +**Supervisor's email address:** kkontog@yorku.ca
 +
 +**Project Description:** 
 +
 +The idea is to design and implement a graph-based model system which allows for dependencies between entities (e.g. software components, business processes) to be modeled. In addition a reasoning engine is to be implemented so events (e.g. a failure of a process) can be propagated efficiently to the dependent nodes. The project can involve a team of two and is part of a research project. 
 +
 +
 +**Required skills or prerequisites:**  
 +
 +  * Excellent Java programming, Excellent Software Design and Architecture skills, very good data base skills
 +
 +**Recommended skills or prerequisites:**
 +
 +  * Knowledge of multi-thread programming and interprocess communication protocols will be useful
 +
 +**Instructions:**
 +Please contact Prof. Kostas Kontogiannis at kkontog@yorku.ca and submit an unofficial transcript and your CV.
 +
 +----
 +
 +==== Large Language Models Based Mutation Testing ====
 +
 +**[added 2024-04-15]**
 +
 +**Course:**  EECS4080
 +
 +**Supervisor:**  Song Wang
 +
 +**Supervisor's email address:** wangsong@yorku.ca
 +
 +**Project Description:** 
 +Recently, pre-trained large language models (LLMs) have emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle large-scale datasets and exhibit remarkable performance across a wide range of tasks. Meanwhile, software testing is a crucial undertaking that serves as a cornerstone for ensuring the quality and reliability of software products. As the scope and complexity of software systems continue to grow, the need for more effective software testing techniques becomes increasingly urgent, making it an area ripe for innovative approaches such as the use of LLMs. Our recent collaboration with Meta also confirms the limitations of existing widely used testing techniques in mutation testing. This project takes a solid initial step toward exploring the next-generation software mutation testing techniques powered by LLMs.
 +
 +**Required skills or prerequisites:**  
 +
 +  * Familiarity with DL libraries such as Tensorflow and Pytorch;
 +
 +**Recommended skills or prerequisites:**
 +
 +  * Knowledge of Python programming;
 +
 +**Instructions:**
 +Send c.v. and transcript to the project supervisor.
 +
 +----
 +
 +==== LLM4SE (Large Language Models for Software Engineering) ====
 +
 +**[added 2024-04-15]**
 +
 +**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 2024-04-15]**
 +
 +**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.
 +
 +==== Machine Learning for Engineering Dependable Systems ====
 +
 +**[added 2024-04-14]**
 +
 +**Course:**  EECS4070/EECS4080
 +
 +**Supervisor:**  Hamzeh Khazaei
 +
 +**Supervisor's email address:** hkh@yorku.ca
 +
 +**Project Description:** 
 +In this research, we plan to examine the application of large language models (LLMs) in designing more dependable software systems. Dependability in this context refers to the overall trustworthiness of the software, which includes aspects such as performance (how well the system operates under varying conditions and workloads), reliability (the system's ability to function properly and consistently over time), and security (resiliency against malicious attacks and its ability to protect data and maintain privacy). One of these aspects will be addressed in this research. This has not yet been finalized as it depends on the applicant. We believe LLMs have great potential in designing intelligent adaptive large-scale computing systems. These models can process vast amounts of data, learn from it, and make decisions or predictions, which is essential for systems that require a high degree of autonomy, such as cyber-physical systems and machine-learning systems that often need to operate and maintain themselves automatically due to their scale and complexity.
 +
 +**Required skills or prerequisites:**  
 +  * Good programming skills. 
 +  * Good grades in System and ML courses.
 +
 +**Recommended skills or prerequisites:**
 +Interested in the intersection of machine learning and systems. Interested in building large-scale systems.
 +
 +**Instructions:**
 +Please send your CV and transcripts to the supervisor.
 +
 +
 +==== Visualizing the debugger for first-year computer science students ====
 +**[added 2024-04-15]**
 +
 +**Course:**  EECS4080
 +
 +**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 virtual reality/augmented reality. In this project, you will have the opportunity to gain hands-on experience in both designing and implementing a software application. You will gain experience in animation or virtual reality.
 +
 +**Required skills or prerequisites:**  
 +  * Proficient in Python (as you will write a visualized debugger for students learning Python)
 +  * Strong learning ability (You will be expected to learn VR/AR programming if we decide to use NR/AR)
 +
 +**Recommended skills or prerequisites:**
 +Experience with virtual reality/augmented reality or animation
 +
 +**Instructions:**
 +Please send your c.v. and transcript to the project supervisor.
 +If available, please also send your e-portfolio (e.g., GitHub, or other links) of your previous projects.
 +
 +----
 +
 +==== Sims for University Life ====
 +
 +**[added 2024-04-15]**
 +
 +**Course:**  EECS4080
 +
 +**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, you'll have the opportunity to gain hands-on experience in both designing and implementing a game.
 +
 +**Required skills or prerequisites:**  
 +
 +  * Strong software engineering skills;
 +  * The game will either be on Android or a web-based game. So you will need to have some experience in either Java or web development;
 +  * an interest in helping first-year students and suggesting game components based on your own experience.
 +
 +**Recommended skills or prerequisites:**
 +Experience with graphical user interface or game design.
 +
 +**Instructions:**
 +Please send your c.v. and transcript to the project supervisor.
 +If available, please also send your e-portfolio (e.g., GitHub, or other links) of your previous projects.
 +
 +----
 +==== Robots Tutors in First Year Programming Courses ====
 +
 +**[added (partially) 2024-04-15]**
 +
 +**Course:**  EECS4080
 +
 +**Supervisor:**  Meiying Qin
 +
 +**Supervisor's email address:** mqin@yorku.ca
 +
 +**Project Description:** 
 +
 +//project description is being updated//
 +
 +----
 +
 +==== Designing Privacy-preserving Systems ====
 +**[added 2024-04-15]**
 +
 +**Course:**  EECS4080 or EECS4480
 +
 +**Supervisor:**  Yan Shvartzshnaider 
 +
 +**Supervisor's email address:**  rhythm.lab@yorku.ca
 +
 +**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 failing 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.
 +
 +Students will help analyze information handling practices of online services and design privacy-enhancing tools. 
 +
 +Specific tasks include: comprehensive literature review of existing methodologies and tools, analysis of privacy policies and regulations, visualization of information collection practices, and design of a web-based interface for analyzing extracted privacy statements to identify vague, misleading, or incomplete privacy statements.
 +
 +For prior project, see [[https://wiki.eecs.yorku.ca/course_archive/2021-22/F/4080_4088_4090_4480_4070/4088_presentation_schedule|this link]]
 +
 +**Required skills or prerequisites:**
 +Good programming and data analysis skills overall, and experience in using Jupyter and/or R for data analysis.  Ability to work independently. 
 +Interest in usable privacy, critical analysis of privacy policies and privacy related regulation.
 +
 +**Recommended skills or prerequisites:**
 +Experience with Machine Learning, Natural Language Processing techniques, HCI design. 
 +Students with diverse backgrounds, including in technical fields, social sciences and humanities are encouraged to apply.
 +
 +**Instructions:**
 +Please fill in [[https://forms.gle/oVVg6hEConSNf9p28|this form]]
 +
 +----
 +==== Designing Privacy-preserving  Virtual Reality Systems ====
 +
 +**[added 2024-04-15]**
 +
 +**Course:**  EECS4080 or EECS4480
 +
 +**Supervisor:**  Yan Shvartzshnaider 
 +
 +**Supervisor's email address:** rhythm.lab@yorku.ca
 +
 +**Project Description:** 
 +Designing a privacy-preserving VR experience requires adhering to contextual integrity of users’ data . This involves accounting for different modes of interaction and ensuring robustness to accommodate the evolving privacy norms associated with future VR adaptations.
 +
 +To tackle this challenge, the project will operationalize a cutting-edge privacy theory and methodologies to develop mechanisms  that ensure that information flows in accordance with users’ expectations and established societal norms in VR settings.
 +
 +Students will help analyze information handling practices of VR applications and design tools to enhance privacy of VR users.
 +
 +Specific tasks include conducting a comprehensive literature review of existing methodologies and tools, performing a dynamic analysis of data practices in VR applications, and checking for compliance with existing regulations and privacy policies.
 +
 +For reference, see these papers:
 +  * https://arxiv.org/pdf/2303.13684.pdf
 +  *https://www.usenix.org/conference/usenixsecurity22/presentation/trimananda
 +
 +**Required skills or prerequisites:**  
 +  *  Good programming skills (experience with coding in Unity is a plus)
 +  *   Interest in usable privacy, critical analysis of privacy policies, and knowledge of privacy-related regulations
 +  *   Ability to work independently
 +
 +**Recommended skills or prerequisites:**
 +  *   Experience with data analysis using Jupyter and/or R
 +  *   Familiarity with HCI design
 +
 +
 +
 +----
 ==== Mnemonic-Based Serious Games ==== ==== Mnemonic-Based Serious Games ====
  
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 Contact professor at email above. Contact professor at email above.
  
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 ==== Strengthening the Security of Autograders ==== ==== Strengthening the Security of Autograders ====
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 **Instructions:** **Instructions:**
 Additional information about PrairieLearn can be found here: https://prairielearn.readthedocs.io/en/latest/. A sample PrairieLearn assessment that includes Python autograded questions can be found here: https://us.prairielearn.com/pl/course_instance/136606/assessment/2351069. Please submit a brief description of your experience with the skills listed above. Additional information about PrairieLearn can be found here: https://prairielearn.readthedocs.io/en/latest/. A sample PrairieLearn assessment that includes Python autograded questions can be found here: https://us.prairielearn.com/pl/course_instance/136606/assessment/2351069. Please submit a brief description of your experience with the skills listed above.
 +----
 ==== Chat Bots in LMS for Easy Course Navigation ==== ==== Chat Bots in LMS for Easy Course Navigation ====
  
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 Please send your c.v., transcript, and Statement of Interest in the project to the project supervisor. Please send your c.v., transcript, and Statement of Interest in the project to the project supervisor.
  
 +----
 ==== Autograders for C and Linux Programs in Undergraduate Courses ==== ==== Autograders for C and Linux Programs in Undergraduate Courses ====
  
2024-25/summer.1713223710.txt.gz · Last modified: 2024/04/15 23:28 by baljko
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