SUDS project

Oct 05 2026

Coronavirus in the Urban Built Environment (CUBE): LTC Prediction Model

Research description:

Our team has validated a method of environmental sampling for SARS-CoV-2 in which swab samples are taken from floors and processed using quantitative polymerase chain reaction (qPCR). Our work in hospitals and long-term care (LTC) homes has shown that COVID-19 burden is reflected in built environment samples, and that SARS-CoV-2 can be detected in the built environment days before an outbreak is recognized. Based on this promising data, we believe that built environment sampling is feasible as a new paradigm for SARS-CoV-2 surveillance. We have data from approximately 5,000 swabs taken at LTC homes, along with covariates for each home.

We are seeking a SUDS Scholar to help identify the sampling covariates that provide greatest power for predicting pending outbreaks in LTC, and to help the team to design a machine learned model that would provide forecasts for the current and upcoming risk of an outbreak. We will use multiple supervised machine learning techniques ranging from logistic regression to extreme gradient boosting.

Year: 2023

Researcher:
Michael Fralick, Lunenfeld-Tanenbaum Research Institute, Sinai Health System

Student:
Bryant Bak-Yin Lim, 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 05 2026

Computer Vision to Capture Patient Flow Data in the Medical Imaging Department

Research description:

The delivery of medical imaging services involves significant resources, including equipment, materials and labor. Proper coordination of these resources positively impacts the productivity of the radiology department, which, in turn, influences access to care, costs, and quality. Currently, the most commonly used techniques to measure work task productivity in radiology departments are manual and have not changed in over 40 years. This research project focuses on computer vision-based approaches to capturing workflow activity data related to the delivery of medical imaging services.

The SUDS Scholar will work closely with the principal investigator and students in the lab to design and implement edge computing prototypes using devices such as the Raspberry Pi and Google Coral Dev Board to collect video data. The SUDS Scholar will use open-source frameworks such as Gstreamer for image handling and Tensorflow to apply deep learning to the image data. The data collected from these approaches will be used to extract metrics important in department performance such as cycle time, flow rate, capacity and utilization. These video-based approaches may provide a nonintrusive, easy, inexpensive, and rapid mechanism for generating operational information and knowledge on the productivity of the medical imaging department.

Year: 2023

Researcher:
Andrew Brown, Unity Health Toronto

Student:
Kunzhi Yu, 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 05 2026

Computational models of social factors in language

Research description:

People adjust the way they speak depending on the context. What factors influence this kind of style shifting? For example, past work has suggested that women adjust their language more than men in male-dominated social settings. We use data science methods on large-scale social media text (from Reddit) to test such hypotheses at scale, and increase our understanding of the relationship between language expression and social constructs like gender and power.
This research contributes to a growing scientific literature using computational models and statistical analyses to study how people use language in social media interaction, and what that reveals about social attitudes and norms. Advances in these topics are needed to inform the design and moderation of social media platforms, and of artificial intelligence systems that can effectively interact with people.

This project will take a truly interdisciplinary data sciences approach, with possible SUDS Scholar tasks including: on the computational side, learning to write programs to process social media data, using current methods from natural language processing; on the linguistics and cognitive science side, designing and running an online survey; on the statistics side, deploying various data analysis methods to analyze both social media data and human survey data.

Year: 2023

Researcher:
Suzanne Stevenson, Department of Computer Science, Faculty of Arts & Science, University of Toronto

Student:
Yu Zhang, 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 05 2026

Analysis of sedation use according to patient race/ethnicity in mechanically ventilated patients

Research description:

This project involves evaluating and improving the performance of an existing algorithm for a computationally intensive graph optimization task.

Community detection is the process of inductively identifying groups within a networked system. The Bayan algorithm has been developed in a recent project supported by the DSI. It currently outperforms all existing methods in accurate retrieval of communities. Bayan is publicly accessible (github.com/saref/bayan) as a part of the open-source Python library, CDlib.

In this project, the SUDS Scholar will work alongside a senior researcher from Huawei and the project supervisor to complete a series of weekly tasks. After receiving training on mathematical optimization and the design of a branch-and-cut algorithm, weekly tasks will be assigned which involve data analyses, computational experiments, and implementing and testing new algorithm speed-ups in a GitHub environment.

This project leverages the state-of-the-art methods in computing and optimization to push the limits for solving a fundamental NP-complete problem exactly and efficiently. The output of this summer research project contributes to the development of a reliable, open-source, and reproducible algorithm for a robust and theoretically grounded detection of communities, thereby improving upon a widely used computational tool for data science.

Year: 2023

Researcher:
Samin Aref, Department of Mechanical and Industrial Engineering, Faculty of Applied Science and Engineering, University of Toronto

Student:
Sanchaai Mathiyarasan, 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 05 2026

Bridging the gap between quantum chemistry and deep learning

Research description:

Deep learning (DL) can potentially substitute traditionally used expensive quantum mechanical (QM) methodologies in chemistry to predict various chemical properties by offering to generate fast and accurate mathematical models better suited to everyday computers. However, training DL models like
neural networks require hundreds of thousands to millions of data points of a particular chemical property to attain good generalization. Such a requirement is currently hindering the development of DL models for chemistry because the generation of large training data using accurate QM methodologies has an infeasible computational cost.

The SUDS Scholar will tackle this problem and utilize a novel quantum mechanics-based approach to efficiently yet accurately generate large QM training data (hundreds of thousands to millions of data points) for a chosen chemical property. Once the QM training data becomes available, they will utilize it to generate new DL models for the chosen property to demonstrate the acceleration in DL for chemistry provided by applying the novel quantum mechanics-based approach.

Year: 2023

Researcher:
Hans-Arno Jacobson, Edward S. Rogers Sr. Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto

Student:
Preet Mistry, University of Toronto

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

Learn more