SUDS project

Oct 02 2026

Diffusion Policies for Discrete and Continuous Actions

Research description:

Advancements in reinforcement learning have paved the way for more sophisticated policies for decision-making in complex environments. This project delves into the exploration of diffusion policies, a class of algorithms that leverage stochastic processes to model the decision-making process in both discrete and continuous action spaces. The primary objective is to design, implement, and evaluate diffusion policies for a range of applications, showcasing their adaptability and effectiveness in diverse scenarios. The student will be responsible for implementing the method, conducting experiments, and compiling the results into a paper.

Year: 2024

Researcher:
Florian Shkurti, Department of Mathematical and Computational Sciences, University of Toronto Mississauga

Student:
Jierui Zhu, University of Toronto

Through SUDS, undergraduate students engage in hands-on research focused on data sciences and AI methodology applications.

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Oct 02 2026

Data science and global health: investigating the relationship between disease and data science methodology

Research description:

Novel statistical and data science methodology developments are often motivated by the increasingly complex data collected for research studies that involve a disease or health outcome. For example, methodological studies motivated by breast cancer or Alzheimer’s disease are common. However, whether the effort for methodological development is appropriately being used for diseases that affect the global population the most is unknown.

In 2020, Lancet published the updated global burden of 369 diseases and injuries in 204 countries and territories. For each top 25 disease, using an automated systematic literature review, we will identify all published methodological research motivated by the disease.

Then we will:

Assess the relationship between the common global diseases and the common diseases that motivate methodological research.
Identify global diseases that are being “neglected” by methodologists.

The student will learn how to conduct an automated systematic literature review, text analysis, produce professional tables, figures, and graphics. The student will have the opportunity to be a co-author of a paper in an academic journal. The R statistical programming language will be primarily used, but other programming languages may be considered based on the student’s proficiency.

Year: 2024

Researcher:
Aya Mitani, Dalla Lana School of Public Health, University of Toronto

Student:
Bilin Nong, University of Toronto

Through SUDS, undergraduate students engage in hands-on research focused on data sciences and AI methodology applications.

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Oct 02 2026

Discover an ultra-diverged RNA virus in the human brain

Research description:

Data-driven virus discovery is revolutionizing our understanding of virology across Earth’s biosphere. In 2020 there were 15,000 known RNA viruses, since then our lab has discovered more new species (currently 375,000+) than everyone else in the world combined, including so called “Dark RNA Viruses” (see Nature paper)

Our lab explores the evolution, ecology, and molecular interactions of these viruses through state-of-the-art computational analysis. Our focus is on how these viruses intersect human health and disease. Currently we’re searching for viruses which cause neurodegenerative disease (i.e. Alzheimer’s) and human cancers. By finding such causal agents, it creates the possibility of developing vaccines or new therapies against devastating diseases.
Your project will be to select/prioritize the thousands of “candidate unknown human viruses” we have identified, and characterize them by any means to identify which viruses are human pathogens.

Info links

Year: 2024

Researcher:
Artem Babaian, Department of Molecular Genetics, Temerty Faculty of Medicine, University of Toronto

Students:
Ayaan Rashid, University of Toronto
Rakan Hadi Alsallum, King Abdullah University of Science & Technology

Through SUDS, undergraduate students engage in hands-on research focused on data sciences and AI methodology applications.

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Oct 02 2026

AI to decipher subclass switching across cancers

Research description:

We are focused on developing and applying state-of-the-art machine learning and computational biology tools to understand the interplay between cancer evolution and phenotypes. In collaboration with the Bremner Lab, we aim to identify the pivotal genes and regulatory networks that drive lineage switching and drug resistance across cancers, with a focus on AML. Our previous work has highlighted the important role of YAP1 and TAZ in stratifying cancers into binary classes which interchange to drive drug resistance. We would like to expand these findings by mapping these subtypes pan-cancer given the wealth of single-cell data generated across both primary tumours and cell lines.

The SUDS student will be immersed in hands-on computational research, working with state-of-the-art deep learning frameworks to integrate large-scale pan-cancer datasets for in-depth analysis leading to discovery of cancer lineage switch drivers. There is significant freedom in project direction, including integrating perturbational datasets. The student will have the opportunity to join a vibrant computational lab and learn cutting-edge tools and techniques for exploration of high-dimensional data.

Year: 2024

Researcher:
Kieran Campbell, Lunenfeld-Tanenbaum Research Institute

Student:
Elliot Sicheri, University of Toronto

Through SUDS, undergraduate students engage in hands-on research focused on data sciences and AI methodology applications.

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Oct 02 2026

AI-based Multi-Sensor Processing for Real-time Analytics in the Medical Imaging Department

Research description:

Join our exciting research project and become a crucial part of our mission to revolutionize healthcare delivery. As a SUDS scholar, you’ll collaborate closely with our principal investigator and research assistants (including a former SUDS scholar). Together, we’re developing cutting-edge, real-time intelligent video analytics tools that have a direct impact on hospitals and medical imaging departments. Your role will involve crafting and implementing edge computing prototypes utilizing advanced technology like the Nvidia Jetson, the world’s leading AI computing platform. You’ll harness the power of open-source frameworks such as Gstreamer and Tensorflow to apply deep learning techniques to image data, unlocking valuable insights. The data you gather will be the key to extracting essential performance metrics and enhancing hospital productivity. Notably, our previous SUDS scholar successfully deployed vision-based AI tools in our hospital’s CT suite last summer. This year, our focus is on aggregating data from multiple image sensors to gain deeper insights across diverse environments. Join us on this exciting journey, where your work will directly influence healthcare access, costs, and quality. If you’re passionate about AI, healthcare, and making a real-world impact, this project may be perfect for you!

Year: 2024

Researcher:
Andrew Brown, Unity Health Toronto

Students:
Tenzin Migmar, McMaster University
Wael Sulais, King Abdullah University of Science & Technology

Through SUDS, undergraduate students engage in hands-on research focused on data sciences and AI methodology applications.

Learn more