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.

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.

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

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 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 indecently. 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 this form