SUDS project

Oct 02 2026

Equitable Streets: Unveiling the Impact of Automatic Speed Enforcement on Road Safety Disparities in Guelph

Research description:

Road traffic collisions pose a significant public health challenge, and controlling vehicle speed is a crucial factor in enhancing road safety. Excessive speed not only endangers drivers but also presents substantial risks to vulnerable road users. Automatic Speed Enforcement (ASE), employing cameras and sensors to detect speeding vehicles, has emerged as an effective strategy. This research focuses on evaluating the impact of ASE in a medium-sized Canadian city, considering the socio-economic diversity across neighborhoods.

The city of Guelph, reflecting varying collision rates and safety infrastructure distributions, serves as a key location for this study. Leveraging diverse data sources, including speed records, offender postal codes, and marginalization indices, the research employs quasi-experimental analyses to: assess pre-existing traffic speed differences; explore the impact of ASE across neighborhoods with different levels of marginalization, and determine whether offenders reside in the camera-equipped neighborhoods.

Led by a multidisciplinary team with expertise in public health, epidemiology, and biostatistics, this research aims to contribute valuable insights into ASE deployment, particularly in terms of social equity. Partnering with city authorities ensures the translation of findings into practical policies, making the study a potential nationwide reference for equitable ASE implementation initiatives. The expected responsibilities of the student will be:

Data Cleaning : Ensure accuracy and consistency in the dataset by meticulously cleaning and validating the data.
Data Integration: Merge data from diverse sources to create a unified dataset, facilitating comprehensive and holistic analysis.
Data Visualization: Proficiently employ data visualization techniques to convey insights effectively, making complex information easily understandable.
Communication and Collaboration : Facilitate effective collaboration among team members, ensuring seamless information exchange and understanding.

Year: 2024

Researcher:
Equitable Streets: Unveiling the Impact of Automatic Speed Enforcement on Road Safety Disparities in Guelph

Student:
Nevan Opp, Carleton University

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

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Oct 02 2026

Engagement, Recruitment and Retention of Small-to-Medium Enterprises in the Skilled Trades in Ontario: Understanding the Impact of the Employer Through an Occupational Lens

Research description:

We are proposing a novel gender-inclusive approach focusing on understanding barriers faced by women, Indigenous people, youth, and other underrepresented groups to increase recruitment, improve retention, expand and stabilize the construction and industrial workforces across Ontario. This proposal builds on our prior research on workplace factors associated with health professions’ workplace stressors, injuries and retention, and my former collaborative professional practice with injured miners, employers, and unions on workers’ return to work. Our research will develop and implement strategies to increase worker participation and retention in the construction and industrial workforce, based on gender-, age-, and ethnicity-informed systematic analysis of barriers to recruitment and retention.

The SUDS scholar will play a crucial role in several aspects of the project, contributing to both data analysis and project development:

Data Analysis and Interpretation (Quantitative Data Analysis)
Data Analysis and Interpretation (Qualitative Research)
Epigenetics analysis

This research provides a unique learning experience for the SUDS scholar, combining data science techniques with insights into social and workplace dynamics. The SUDS scholar will join the ReSTORE lab and be part of a multidisciplinary team. They will gain hands-on experience in advanced analytical methods while contributing to a project with real-world implications.

Year: 2024

Researcher:
Behdin Nowrouzi-Kia, Department of Occupational Science and Occupational Therapy, Temerty Faculty of Medicine, University of Toronto

Student:
Advika Gudi, 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

Learning the DNA characteristics of mutational processes in cancer

Research description:

Cancer is a genetic disease caused by small mutations in DNA that occur in individual’s cells over time. Most mutations are harmless “passenger” mutations while a small minority of mutations termed “driver” mutations unlock the features of cells that lead to cancer. Passenger mutations tell us about the history of the cancer and how mutations arise due to age, carcinogens, or deficient DNA repair processes in cells. Thousands of cancer genomes with millions of mutations are now available. These datasets show that mutations do not occur randomly but instead have nucleotide characteristics (such as C>T mutations correlated with patient age vs. C>A mutations associated with tobacco smoking). However, these “mutational signatures” are based on very limited DNA context, usually just the two nucleotides around the mutated position. The objective of this research project is to develop sequence-base machine learning models that classify or generate cancer mutations based on their mutational process that caused the mutations, or the cancer type they occur in. In addition to developing accurate models, we aim to enhance model interpretation and decipher the sequence features contributing most to model performance, allowing us to better understand how mutations contribute to cancer development and molecular complexity. The student is expected to develop and test ML models using R or python coding, interpret data from computational and biological angles, visualize data, prepare documentation, and present at lab meetings. We will finetune the project based on the computational and/or biological or disease research interests of the student.

Year: 2024

Researcher:
Judi Reimand, Ontario Institute for Cancer Research

Students:
Keren Zhang, University of Toronto
Yahya Abdullah Alhabboub, 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

Interactive approaches to automatic source code summarization using deep learning

Research description:

Automatic source code summarization is the task of generating a readable summary that describes the functionality of the code in natural language. In recent years, the use of deep learning-based approaches has led to significant improvement in the performance of automatic code summarization, e.g., using Transformers and Graph Neural Networks. However, the performance is still far from optimal and developers that are unsatisfied with a given summary are not able to provide feedback or additional information that can be used to refine the output.

In this research project, the goal is to investigate ways in which additional input from the developer can further improve the performance of automatic code summarization. Specifically, the main tasks in the project are:

Investigating existing failures of state-of-the-art source code summarization solutions
Developing new computational approaches and interactive schemes for incorporating developer input and feedback in order to improve the performance of deep learning-based approaches for source code summarization
Evaluating the impact of the new approaches on existing large code summarization datasets.

The responsibilities of the SUDS student will be:

Read about, implement, and empirically evaluate state-of-the-art models for automatic source code summarization.
Investigate existing failures of state-of-the-art source code summarization solutions and develop interactive schemes for incorporating developer input and feedback in order to improve their performance.

Evaluating the impact of the new approaches on existing large code summarization datasets.

Year: 2024

Researcher:
Eldan Cohen, Department of Mechanical and Industrial Engineering, Faculty of Applied Science & Engineering, University of Toronto

Student:
Yifan Liu, 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

Improving a flexible search system for high-accuracy identification of biological entities and molecules using AI

Research description:

Year: 2024

Researcher:
Gary Bader, Terrence Donnely Centre for Cellular & Biomedical Research, University of Toronto

Student:
Fatemah Alsolaiman, 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