SUDS project

Oct 02 2026

The link between neuroinflammation and neurological markers of depression

Research description:

With a life-time prevalence of 12% in Canada, major depression is the third highest cause of disability worldwide. Mid-to-late-life depression (MLD) confers a 2-5 fold increase in dementia risk, including for Alzheimer’s disease. Also, it has become increasingly clear that depression has a strong link to neuroinflammation. Thus we emphasize the importance of grounding the study in the link between a neurological signature of depression and its link with inflammation. Furthermore, there are sex differences in the immune system. Men and women may also differ in the types of inflammatory markers they produce as they age. The premier approach to studying the neurological markers of depression is neuroimaging, predominantly magnetic resonance imaging (MRI). MRI has uncovered structural changes, functional alterations, or connectivity abnormalities in specific brain regions or networks in patients of depression. Thus, this project will focus on a retrospective analysis of the Canadian Biomarker Integration Network in Depression dataset, which provides inflammatory and MRI assessments in MLD patients. We aim to (1) synthesize a multimodal neuroimaging signature of MLD severity; and (2) assess the associations between this signature and systemic inflammation.

Year: 2024

Researcher:
Jean Chen, Baycrest

Student:
Muying Hui, University of British Columbia

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

Learn more

Oct 02 2026

Understanding the Neural Correlates of Attention in Children via Neuroimaging Data

Research description:

This project aims to develop a machine learning (ML) model for predicting children’s attention ability using features extracted from over 10,000 MRI images from the Adolescent Brain Cognitive Development (ABCD) study. The project will cover diverse data science topics, including big data in neuroimaging, network analysis, complex systems, feature selection, visualization, and cloud computing. The proposed features encompass those derived from T1 and T2 MRI scans, structural connectivity via diffusion, functional connectivity via resting and task-based fMRI, and non-linear metrics like fractal dimensions and Lyapunov exponents. The ABCD database provides predictive labels, including self-report surveys, clinical assessments (e.g., NIH Toolbox Flanker Inhibitory Control), and ADHD-related diagnosis and symptoms. Model interpretability is a priority. Feature selection should be transparent, and their respective contributions should be reportable and visualized. This project is part of a broader study on brain-computer interfaces and neural plasticity.

The student will have the opportunity to work with large-scale neuroimaging data, MRI/fMRI preprocessing, experimentation with feature selection methods and ML and/or deep learning models. They will have access to cloud computing resources and Google Vertex AI tools. The student will be supported by doctoral trainees and staff engineers in the lab. The expected deliverables will be:

A deep learning model trained on 10,000 MRI images from the ABCD study to predict children’s attention ability.
A feature visualization tool to qualitatively and/or quantitatively describe what brain areas and measures are physiologically relevant to attention in children.

Year: 2024

Researcher:
Tom Chau, Holland Bloorview Kids Rehabilitation Hospital

Student:
Melody Nguyen, University of Toronto

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

Learn more

Oct 02 2026

Using advanced machine learning approaches to optimize psychological test efficiency interview

Research description:

This project will involve students to use advanced machine learning approaches to analyze large scale datasets of psychological tests with responses collected from participants all over the world. The goal of this project is to analyze response patterns from participants, train computational models to optimize the assessment of participants’ psychological traits (e.g., personality) and abilities (e.g., IQ) in an effective and efficient manner, and implement computational models in an app for use by real-world users.

The student will be responsible for data cleaning, data analysis, using machine learning techniques to optimize the models, implementing the models on a website for use by users, and writing a paper for publication.

Year: 2024

Researcher:
Kang Lee, Department of Applied Psychology and Human Development, Ontario Institute for Studies in Education, University of Toronto

Student:
Rogers Yang, University of Toronto

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

Learn more

Oct 02 2026

Using Natural Language Processing to Understand Naturalistic Speech Perception Herrman

Research description:

Many adults over 60 years live with some form of hearing impairment that makes speech comprehension difficulty. However, progress in predicting speech-comprehension difficulties in everyday life has been limited, in part, because hearing-science research has mainly focused on speech comprehension of short, disconnected sentences that lack a topical thread and are not relevant to the listener. New approaches to understanding naturalistic speech listening are thus critical to gaining insight into impaired speech processing.

The student will be involved in research that leverages novel natural language processing (NLP) approaches (e.g., sentence embeddings) with graph theoretic approaches (e.g., network centrality) to better understand how individuals listen to naturalistic speech. The student will analyze transcripts of spoken stories and transcripts of individuals recalling these stories after listening to them (using NLP), and will integrate relevant information from these analyses to capture the structure in which individuals comprehend speech (using graph theory). The student will program the analyses and visualize the results using Python/MATLAB. The student will work with the supervisor and a graduate student with biophysics and psychology background. The lab provides ample opportunities to learn how sophisticated data-analysis tools can be used to facilitate research in basic science with clinical applicability.

Year: 2024

Researcher:
Bjorn Herrmann, Baycrest

Students:
Nicholas Wong, University of Toronto Mississauga

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

Learn more

Oct 02 2026

Using single-cell data analysis approaches to inform the design of iNKT-targeted cancer immunotherapies

Research description:

Invariant natural killer T (iNKT) cells are unconventional T-cells that are ubiquitously found in mammals and provide immunity to pathogens and against tumours. Through their T-cell receptor (TCR), iNKT cells respond to glycolipid antigens, a class of antigens that is invisible to conventional CD4 or CD8 T-cells. Furthermore, these cells are heterogenous and can differentiate into discrete effector subsets. Using advanced functional genomics, we identified a bona fide cytotoxic iNKT cell subset, which is functionally equivalent to cytotoxic CD8 T-cells. These cytotoxic iNKT cells efficiently kill tumour cells in vitro/in vivo and provide several advantages over their CD8 T-cell counterparts. Interestingly, cytotoxic iNKT cells recognize and kill tumour cells through several modalities that are both TCR-dependent and TCR-independent. Together, our findings indicate that cytotoxic iNKT cells could be used to develop novel cancer immunotherapies with a lower risk of tumour evasion, although a mechanistic understanding of their function remains to be understood. The goal of this project is to leverage available single cell RNA sequencing datasets to identify immune receptors expressed by cytotoxic iNKT cells that may be involved in recognition of or response against tumour cells. Results from this work will inform the rational design of iNKT-targeted cancer immunotherapies. This project is co-supervised with Dr. Thierry Mallevaey (Department of Immunology, Temerty Faculty of Medicine, University of Toronto).

The responsibilities of the student will include, although are not limited to:

Review the primary literature to find important immune receptors expressed by cytotoxic iNKT cells
Assess the quality of available scRNA seq datasets, with consideration of experimental design/statistics
Establish a scRNA seq data processing workflow
Perform data reduction and clustering of available scRNA seq datasets using R software
Identify genes driving cluster formation from scRNA seq data and extract biological knowledge from cluster-specific biomarkers
Attend weekly lab meetings (~1 hour/week) and present research updates biweekly
If time: validate identified biomarkers on iNKT cell subsets in the laboratory setting via flow cytometry or through performing gene expression analyses using qRT-PCR

Year: 2024

Researcher:
Jastara Singh, Department of Immunology, Temerty Faculty of Medicine, University of Toronto

Students:
Jahin Kabir, University of Toronto

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

Learn more