PhD, Massachusetts Institute of Technology
Our lab uses machine learning and artificial intelligence to do biomedical research, focusing on cancer evolution, gene regulation, clinical informatics, and gene function prediction. A key interest is the role of RNA-binding proteins (RBPs) in post-transcriptional regulation. We focus on developing computational and experimental techniques to determine the RNA specificities of RBPs (both sequence and structural) and use these specificities to predict their target transcripts, determine RBP function, and ultimately decipher the regulatory code. Another focus is reconstructing and modelling somatic evolution (pre- and post-cancer) using bulk and single-cell genomic data. In general, we are focused on using large, heterogeneous functional genomic datasets to uncover insights about gene function. Recently, we have becoming increasingly interested in using artificial intelligence and predictive analytics, along with electronic medical records, to inform patient care, particularly in the domain of auto-immune disease.
BS, University of Toronto
Kimberly is a PhD student in the Department of Molecular Genetics at the University of Toronto and the Ontario Institute for Cancer Research where she is jointly supervised by Dr. Philip Awadalla and Dr. Quaid Morris. She earned her Hon. B.Sc. from the University of Toronto in 2016 with a double major in Global Health and Genome Biology. Her doctoral research is focused on understanding the evolutionary pressures governing blood dynamics in healthy and precancerous contexts and how these can be used to better predict cancer risk at an individual level.
Niklas von Krosigk
BS, University of British Columbia
Nik is a PhD student in the Computational Biology in Molecular Genetics track at the University of Toronto, and is co-supervised by Quaid Morris and Lincoln Stein. He earned a B.Sc (Honours) in Cell and Developmental Biology from the University of British Columbia in 2018. He is working on applying machine learning approaches to single cell sequencing data.
BScH, Queen's University
Jarry is a PhD student in the CBMG program at the University of Toronto. He earned his Bachelor in Astrophysics from Queen's University in 2013. His research interests include applications of machine learning methods and cancer genetics. He is currently developing tools to reconstruct the evolutionary history of cancers using single-cell sequencing data.
Kaitlin U Laverty
BS, University of Toronto
Kaitlin is a PhD student in the Department of Molecular Genetics at U of T. She also completed her B.Sc. degree at U of T, with a major in Molecular Genetics and minor in both Statistics and Computer Science. She is interested in the application of machine learning methods to functional genomics data. Kaitlin is particularly interested in post-transcriptional gene regulation.
MS, Northeastern University (Boston)
Jingping is working on multi-omics integration of breast cancer involving genomics, transcriptomics, proteomics and radiomics. She earned BS degree in Applied Mathematics from Saint Louis University, and MS degree in Bioinformatics from Northeastern University. Her interest including Machine Learning and Cancer Genetics.
MS, Computer Science, University of Toronto
Cait earned her BSc. in Computational Biology, and MSc. in Computer Science at the University of Toronto. Her research interests are centered on machine learning and cancer evolution. Cait is co-supervised by Quaid Morris and Kieran Campbell, currently working on applying topic modeling approaches to understand mutational signatures in cancer evolution.
Ruian (Ian) Shi
MSc, University of Toronto
Ian is a PhD student in the University of Toronto's Department of Computer Science. He previously earned his BSc in Computer Science and Bioinformatics and MSc in Computer Science at the University of Toronto. Ian is interested in deep time series methods, deep generative models, and machine learning applications in the health and biology domains.
MA, Columbia University
Olga is a PhD student in the Tri-I CBM program. She earned a Master degree in Biomedical Informatics from Columbia University in 2018, an MBA from INSEAD, and a Bachelor degree from New Mexico State University. She is building NLP-inspired deep learning models to understand the specificity of the adaptive immune system in order to design cancer cell therapies.
Madison graduated from MIT in 2019 with a degree in Computer Science and Biology. She then entered the Tri-Institutional Computational Biology & Medicine program, and is co-supervised by Quaid Morris and Mike Berger. She is interested in the field of precision oncology, specifically in using machine learning techniques to improve cancer treatment and patient outcomes. Madison is currently working on developing models for cancer diagnosis and detection using clinical genomic assays.
BA, New York University
Cyrus Tam is a student in the CBM program with a BA in Biology from NYU. He is interested in understanding how in vivo RNA structures impact RBP binding and their applications in cancer progression.
MS, Computer Engineering, Antalya Bilim University
Ilyes is a PhD student in the Tri-I CBM program. He earned Bachelor of Science degrees in Electrical & Electronics Engineering and in Computer Engineering from Antalya Bilim University (ABU) in 2017. and Master of Science in Electrical and Computer Engineering from ABU. He is interested in the application of machine learning methods to functional genomics, particularly in understanding post-transcriptional regulation.
MS, Electrical Engineering, Columbia University
Aditya is a PhD student in Tri-I Computational Biology & Medicine, mainly interested in studying regulation of gene expression using mathematical modeling. He did his B.Tech. and MS in Electrical Engineering.
M.S, Computer Science, University of Rhode Island
Ethan is a Research Associate in the Morris Lab focused on developing computational methods to help us better understand cancer evolution. His research interests include machine learning, and the genetic factors of disease.
MS, New York University
Leah is a PhD student in the Tri-Institutional Computational Biology & Medicine program. She graduated with a Bachelor of Science degree in Mathematics from the University of Wisconsin-Madison in 2015 and a Master of Science in Bioinformatics from New York University in 2020. Leah is interested in developing and applying computational methods to characterize cancer progression using single cell sequencing data.