Cate MacLeod

Oct 02 2026

Automated Text Analysis of Fake News and Biased News

Research description:

Using Big Data, text analysis, and machine learning techniques, we will analyze how fake news and real news differ in their moral themes, cognitive styles, antiscience attitudes, emotional valence, and other psychological characteristics. Likewise, we will analyze how ideologically more biased news and less biased news differ on the various dimensions. To answer these questions with rigor and robustness, we will analyze about 7 million news articles from about 500 media outlets. The outlets vary widely in ideological leaning, from far left to far right. They also vary in veracity, from mostly fact-checked to mostly fake, conspiracy, and pseudoscience news. Our team has already completed preprocessing of all the news articles.

The SUDS Scholar will apply automated text analysis and machine learning techniques to these articles in order to identify linguistic patterns and biases depending on how fake or real and how left-leaning or right-leaning the media outlet is.

My last SUDS Scholar (Summer 2023) worked on the first stage of this project, presented our work at the Showcase, and won one of the two Best Oral Presentation Awards. This year’s SUDS Scholar (Summer 2024) will work on the full-fledged implementation of the project.

Year: 2024

Researcher:
Spike W.S. Lee, Joseph L. Rotman School of Management, University of Toronto

Students:
Christopher Cao, University of Toronto
Rachel Way, Simon Fraser 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

Meta-analysis of randomized control clinical trial data

Research description:

Year: 2024

Researcher:
Charlie Boone, Terrence Donnely Centre for Cellular & Biomedical Research Temerty Faculty of Medicine, University of Toronto

Students:
Hassan Alsayhah, 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

Multiomics data analysis and visualization to investigate cross-talk between cells in integrated organ-on-a-chip models

Research description:

To develop safe nanoparticles for use during pregnancy, we first need to understand the cross-talk (communication) between cells of the placenta (barrier between the mother and the baby) and other cells from the mother at different pathological conditions, e.g. cancer. We developed an organ-on-a-chip model to mimic this environment in the lab and investigate the cross-talk between cells. We used this model to generate protemic and transcriptomic data. A data science student will work with a graduate student and help analyze this big data and enable different visualization approaches of the data. This a great opportunity for the student to work in an interdisciplinary team that works at the intersection between nanotechnology and microfluidics, and learn new wet-lab techniques, and apply their knowledge in data science to solve real-case problems.

Year: 2024

Researcher:
Hagar Labouta, Unity Health Toronto

Students:
Paulette Peram, McGill University
Aisha Soliman Alsomiry, 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

Population genomics of an insect outbreak

Research description:

Insects cause untold damages to agricultural and horticultural crops every year, despite our best efforts to control them with pesticides and management practices. And, because plants are the base of terrestrial food chains, damage caused by insects to plants has the potential to have ripple effects on other species. Agricultural and greenhouse pests, in particular, cause large economic losses every year. The genetic basis of how these pests thrive, or fail to thrive, on their hosts plants remains poorly understood. We are in the unique position of having a large sample of genomic sequence data for an insect amidst a population outbreak, when there numbers were growing exponentially. The research is an integrative mixture of plant biology, genetics and genomics, population genetics, and bioinformatics.

The essence of the project is for student(s) to align next generation sequence data to a reference genome for the pest, identify polymorphic sites in the population, and then link allele frequency differences in the insects with phenotypic differences in their host plants.

Year: 2024

Researcher:
John Stinchbombe, Department of Ecology and Evolutionary Biology, Faculty of Arts & Science, University of Toronto

Student:
Jessica Li, 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

Retention & Graduation by Design: Unveiling Predictive Paths to Student Success

Research description:

Ensuring student success at UofT is a critical objective for the institution. Student attrition and delayed graduations not only affect individual students but also have broader societal and economic implications. The Student Academic Analytics project is a multi-year collaboration across the University and has resulted in the development of a series of data tools focused on elements of undergraduate student success. This project will develop predictive models to help understand factors impacting student retention, graduation, and time to graduation. The ultimate goal is improving understanding of barriers to success and considering support systems and strategies to enhance student outcomes.
The successful candidate will use a variety of curated datasets relating to student success to generate and test models. The datasets include many student (e.g., gender, legal status, high school GPA, course load, course performance), though not EDI data, and environmental characteristics (e.g., academic program design, living in residence) to examine vital questions broadly around three areas.

What characteristics are most associated with being retained from year 1 to year 2?
What characteristics are most associated with greater graduation rates?
What characteristics are most associated with shorter times to graduation?

Year: 2024

Researcher:
Susan McCahan, Department of Mechanical & Industrial Engineering, Faculty of Applied Science & Engineering, University of Toronto

Students:
Varun Datta, University of Toronto

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

Learn more