Hi, I’m Cait
I am a graduate student at the University of Toronto supervised by Quaid Morris and Kieran Campbell, and a graduate researcher at the Vector Institute and Ontario Institute for Cancer Research. I use machine learning to understand cancer evolution.
I did my undergraduate studies at the University of Toronto, in Computational Biology and Statistics. I’m passionate about open science, and promoting great mentorship in the sciences.
I like to drink coffee and go hiking ☕ 🏔️ ☀️
Research
* Indicates equal contribution
Peer Reviewed Publications
Regional Mutational Signature Activities in Cancer Genomes
Caitlin Timmons, Quaid Morris, and Caitlin F. Harrigan
PLOS Computational Biology, 2022
Activating mTOR mutations are detrimental in nutrient-poor conditions
Agata A. Bielska, Caitlin F. Harrigan, Yeon Ju Kyung, Quaid Morris, Wilhelm Palm, and Craig B. Thompson
Cancer Research, 2022
Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
Yulia Rubanova, Ruian Shi, Caitlin F. Harrigan, Roujia Li, Jeff Wintersinger, Nil Sahin, Amit Deshwar, and Quaid Morris
Nature Communications, 2020
TrackSigFreq: subclonal reconstructions based on mutation signatures and allele frequencies
Caitlin F. Harrigan, Yulia Rubanova, Quaid Morris, and Alina Selega
Pacific Symposium on Biocomputing, 2020
Other Publications
Posters
Dirichlet allocation of mutations to model DDR in cancer
Toronto DNA Damage & Repair Symposium
Dirichlet Allocation of Mutations Captures the Action of DNA Damage and Misrepair Processes
Intelligent Systems for Molecular Biology
Dirichlet Allocation of Mutations in Cancer Genomes
Machine Learning in Computational Biology
Tandem Signatures of DNA Damage and Misrepair in Cancer
Computing Research Association’s Grad Cohort for Women
TrackSigFreq: subclonal reconstructions based on mutation signatures and allele frequencies
Machine Learning in Computational Biology
Talks
Academic Talks
DAMUTA: Dirichlet allocation of mutations as a function of both damage and DNA repair
Cold Spring Harbour Laboratory Meeting: Genome Informatics
Selected Talk
TrackSigFreq: subclonal reconstructions based on mutation signatures and allele frequencies
Pacific Symposium on Biocomputing
Selected Talk, Poster
Guest Lectures
Exploring & Explaining Data in the Wild
AI & Data Science Post-Graduate Program, Loyalist College
Invited by Prof. Peter Papadakos
Data Collection & Analysis
PRISM research & mentorship program, University of Toronto
Invited by Prof. Sadia Sharmin
Environmental & Life Sciences Workshop Series
STEMHub Foundation
Invited workshop series
R for bioinformatics
Global Society for Genetics and Genome Biology
Invited workshop
Other Talks
Finding the ‘I’ in science
ACM Canadian Celebration of Women in Computing
Selected Workshop
Undergraduate research opportunities: how to find them and make them work for you
UofT Bioinformatics and Computational Biology Student Union
Invited by the BCBSU
How to hack your degree
Computer Science Student Union, University of Toronto
Invited by the CSSU
Teaching
Research Mentorship
Kiki Zhang
Undergraduate Student, Johns Hopkins University
Topic: Mutational signatures in the context of branching evolution
Fedir Zhydok
Undergraduate Student, National University of Kyiv-Mohyla Academy
Topic: Identifying metastatic tumours from mutational signatures
Caitlin Timmons
Undergraduate Student, Memorial Sloan Kettering Cancer Center
Topic: Modelling spatial distribution of mutational signatures in cancer genomes.
Haritha Lakshmanan
Highschool Student, Memorial Sloan Kettering Cancer Center
Topic: Automatic discovery of mutations predictive of survival in breast cancer patients
Teaching Assistant Positions
JSC370: Data Science II
JSC270: Data Science I
STA313: Data Visualization
PRISM: Preparation for Research through Immersion, Skills, and Mentorship
JSC370: Data Science II
CSC197: Privacy in the Age of Big Data Collection
STA4273: Minimizing Expectations
CSC197: Privacy in the Age of Big Data Collection
JSC270: Data Science I
CSC373: Algorithm Design, Analysis & Complexity
Testimonials
“I was fortunate to work with Cait for almost a year and had a wonderful experience. She was patient, supportive, and skilled at explaining complicated concepts in biology and programming. As a mentor, she has a great sense of when to guide you through a problem and when to let you work through challenges on your own. She helped me grow as a researcher and writer and I’m grateful for everything I learned from her.”
“As a mentor, Caitlin has greatly enhanced my ability to conduct research. She also provided valuable resources and aimed to make sure that I understood the fundamental concepts behind all the research conducted by breaking down difficult principles into easily comprehensible statements and thoughts. Additionally, she helped me find any sources of error as the research progressed. I enjoyed researching under her guidance, and I had an overall positive experience.”