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.
PhD, Yale University
Hussein is a Postdoctoral Research Fellow in the lab. His main research interests are centered on interpretable machine learning and somatic-germline interactions in cancer. He earned his PhD/MA in Computational Biology/History of Science from Yale University, MS in Bioinformatics from Indiana University (IU), and BS in Computer Science from the Lebanese American University (LAU).
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, 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.
BS, Massachusetts Institute of Technology
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, 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, 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.
BS, Duke University
Divya is a PhD student in the Tri-Institutional Computational Biology & Medicine program. Prior to entering her PhD, she earned a B.S. in Computer Science at Duke University and then worked as a software engineer on Apple Watch. She is primarily interested in using machine learning methods to understand gene regulatory networks.
MS, New York University
Leah is a PhD student in the Tri-Institutional Computational Biology & Medicine program. She earned a B.S. in Mathematics from the University of Wisconsin-Madison and an M.S. in Bioinformatics from New York University. She is developing a method to infer clonal lineage of tumor cells from single cell RNA-sequencing.
Aziz is a Biology and Applied Mathematics double major at Colgate University. His research interests lie in the overlap between Statistics, Machine Learning, and Human Diseases, such as cancers, parasitic infections, and mood disorders. His internship at MSK as part of the Morris Lab involves building classification models for predicting rare, out-of-distribution tumor types.