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--- 2022-23:summer [2023/04/12 17:53] – ruppert
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 **Instructions:**
  Email your full CV and transcript to marzieha@yorku.ca
+==== Robot Tutors in Higher Education  ====
+**Course:** EECS4070
+**Supervisor:** Meiying Qin
+**Supervisor's email address:** mqin@yorku.ca
+**Project Description:**
+In this reading course, you will survey the literature on robot tutoring systems, which lies in the field of human-robot interactions (HRI). In particular, you will read literature on robot tutors for different ages ranging from elementary school students to university school students. You will also learn reinforcement learning relevant to model the robot tutors. In order to gain a deeper understanding of the materials, you may design a relevant project with what you have learned, though you will not implement the project. You are expected to compile a survey of robot tutoring systems as an outcome of this course. Depending on the quality of the survey, we may publish this survey and you may gain experience of formal publication.
+**Required skills or prerequisites:**
+  * Course in artificial intelligence or machine learning
+**Recommended skills or prerequisites:**
+  * Prior experience with working in a project, either individual project or as a group
+**Instructions:**
+Please send your c.v. and transcript.
+Optional: e-portfolio that demo previous projects that one has worked on
+==== How to use ChatGPT in Classrooms ====
+**Course:** EECS4080
+**Supervisor:** Hadi Hemmati
+**Supervisor's email address:** hemmati@yorku.ca
+**Project Description:**
+ChatGPT and similar AI-based generative models are becoming very strong these days and one worry about them is their misuse in classrooms (i.e., for cheating). However, they can also be seen as a tool in the instructor's hand to leverage in the assignments and class activities. In this project we try to build tools that help university instructors use AI in classroom.
+Note: students will be expected to do a portion of the project, not the whole.
+**Required skills or prerequisites:**
+  * Software development skills including web development
+  * Familiarity with machine learning
+**Recommended skills or prerequisites:**
+  * Hands-on prior experience with full stack web development
+  * Fair understanding of how machine learning models work, specially how the generative models such as ChatGPT is working
+**Instructions:**
+Send c.v. and transcript to Prof. Hemmati.
+==== ChatGPT-based tool for code defect detection ====
+**Course:** EECS4080
+**Supervisor:** Hadi Hemmati
+**Supervisor's email address:** hemmati@yorku.ca
+**Project Description:**
+In this project, you will work with some grad students and research assitants to help implement tools that uses ChatGPT or other similar large language model to predict defects in code.
+**Required skills or prerequisites:**
+ Java, Python, basic knowledge of Machine Learning
+**Recommended skills or prerequisites:**
+  * Have taken a course on machine learning
+  * Familiarity with sequence learning
+  * Java IDE plug-in development
+  * GitHub Bot development
+**Instructions:**
+ Send your CV and transcript to the Prof. Hemmati.
+==== Design and Implementation of a Mental Health Mobile App for University Students ====
+**Course:**  EECS4080
+**Supervisor:** Kiemute Oyibo
+**Supervisor's email address:** koyibo@yorku.ca
+**Project Description:**
+Mental health issues are a growing public concern globally. University students are not left out of this global crisis, which became more prevalent during and after the COVID-19 pandemic. Mental health issues such as stress, anxiety, and depression adversely affect health and quality of life, including students’ academic performance. Research shows that 21% of counseling center students’ cases border on severe mental health issues. Despite the growing number of students experiencing mental health challenges, university support services are inadequate. This calls for additional support systems to help university students that may be in need, e.g., of social support and counselling. We propose to utilize a mental health app tailored to the university student community to support and promote students’ mental wellbeing on campus. In this project, we aim to design, implement and evaluate a mental health mobile app that has the potential to foster students’ mental health and wellbeing.
+**Required skills or prerequisites:**
+  * EECS 3461
+  * UX design
+  * Prototyping (e.g., with Figma)
+**Recommended skills or prerequisites:**
+  * Coding
+  * HCI system evaluation
+**Instructions:**
+Send your cv and transcript to Prof. Oyibo.
+==== Design and Implementation of an Interactive Website  to Create Awareness about Dark Patterns  ====
+**Course:**  EECS4080
+**Supervisor:** Kiemute Oyibo
+**Supervisor's email address:** koyibo@yorku.ca
+**Project Description:**
+Dark patterns have become prevalent in the online environment. They are deceptive and/or manipulative interfaces crafted by UX designers in the best of interest of the vendor or service. Examples include bait and switch, sneak into the basket, hidden cost, tricky question, and confirm shaming. Despite their prevalence in the online environment, public awareness about dark patterns is still low. In this project, we aim to create an interactive educational website in which the various types of dark patterns are illustrated, and users are made aware of the “underlying intentions” and the potential cost and effects on users.
+**Required skills or prerequisites:**
+  * EECS 3461
+  * UX design
+  * Prototyping (e.g., with Figma)
+**Recommended skills or prerequisites:**
+  * Coding
+  * HCI system evaluation
+**Instructions:**
+Send your cv and transcript to Prof. Oyibo.
+==== Electric Load Forecasting via Deep Generative Models ====
+**Course:**  EECS4080
+**Supervisor:**  Michael Jenkin
+**Supervisor's email address:** jenkin@yorku.ca
+**Project Description:**
+With the fast increase in renewable energy generation and electric vehicles, electric load forecasting is becoming more and more important for power system operation. Based on the forecasting horizon, there are mainly three types of load forecasting, i.e., short-term, medium-term, and long-term. Short-term load forecasting mainly aims to predict the electric load in the next few seconds to the next few hours, which can be very helpful for real-world energy dispatching. In recent years, machine learning, especially deep learning, has shown impressive performance for short-term load forecasting.
+Generative models, e.g., generative adversarial networks, have shown great potential for computer vision and natural language processing. The potential of such generative models has not been well studied for load forecasting. In this project, we mainly aim to benchmark the performance of different types of deep generative models for short-term load forecasting. We will mainly work on OPEN EI data sets which consist of electric load consumption data sets for different buildings in the US.
+**Required skills or prerequisites:**
+Good python software skills. Interest in AI systems.
+**Recommended skills or prerequisites:**
+ Interest in GANs. Interest in AI software development.
+**Instructions:**
+Send CV, (unofficial transcript), GitHub repo address if available to Prof. Jenkin.
+==== Analyze the Impacts of Ensemble Learning for Anomaly Detection ====
+**Course:** EECS4080
+**Supervisor:**  Michael Jenkin
+**Supervisor's email address:** jenkin@yorku.ca
+**Project Description:**
+Hacking and false data injection from adversaries threaten can cause significant financial loss. Accurate detection of anomalies is of significant importance for the safe and efficient operation of modern power grids. In recent years, different types of techniques, such as statistical methods, unsupervised learning methods, generative models, and prediction-based methods, have been applied for anomaly detection. However, most of the current works assume the stability of the data distribution and ignore the distribution drift, which often happens in the real world.
+In this work, we aim to utilize the benefits of ensemble learning to address real-world anomaly detection problems. Specifically, we plan to dynamically utilize the different base models via ensemble learning to tackle the challenges of distribution drift in the real world. For this project, we will mainly work on two data sets, i.e., the Secure Water Treatment (SWaT) Dataset and ICS Cyber Attack Dataset.  These two data sets are frequently used real-world data sets for anomaly detection.
+**Required skills or prerequisites:**
+ Interest in AI systems. Interest in AI software development
+**Recommended skills or prerequisites:**
+Good python programming skills.
+Some course(s) in AI systems
+**Instructions:**
+Send CV, (unofficial transcript), GitHub repo address if available to Prof. Jenkin.