Computational & Systems Biology Program

The Quaid Morris Lab


Quaid Morris, PhD

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.

Quaid Morris Lab members

Publications Highlights

S Sharma, S Kajjo, Z Harra, B Hasaj, V Delisle, D Ray, RL Gutierrez, …Uncovering a mammalian neural-specific poly (A) binding protein with unique properties. Genes & Development 2023

E Kuzmin, TM Baker, T Lesluyes, J Monlong, KT Abe, PP Coelho, … Evolution of chromosome arm aberrations in breast cancer through genetic network rewiring. bioRxiv, 2023.06. 10.544434         

M Darmofal, S Suman, G Atwal, JF Chen, A Varghese, JC Chang, … Deep-learning model for tumor type classification enables enhanced clinical decision support in cancer diagnosis. Cancer Research 83 (7_Supplement), 5440-5440 2023    

O Lyudovyk, Y Elhanati, A Streltsov, Q Morris, S Vardhana, B Greenbaum   T-cell mediated response to emerging COVID-19 strains in patients with cancer studied via deep learning. Cancer Research 83 (7_Supplement), 795-795 2023

D Ray, KU Laverty, A Jolma, K Nie, R Samson, SE Pour, CL Tam, … RNA-binding proteins that lack canonical RNA-binding domains are rarely sequence-specific. Scientific Reports 13 (1), 5238 2023      

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Quaid Morris, PhD


  • Computational biologist Quaid Morris uses artificial intelligence techniques and develops machine learning algorithms to study gene regulation, cancer evolution, clinical informatics, and other topics in systems biology.
  • PhD, Massachusetts Institute of Technology
[email protected]
Email Address


Ilyes Baali
Ilyes Baali

Graduate Student

Jarry Barber

Graduate Student

Dmitrii Chebanov

Postdoctoral Fellow

Madison Darmofal
Madison Darmofal

Graduate Student

Chenlian (Tom) Fu

Graduate Student

Caitlin Harrigan

Graduate Student

Josh Hess

Graduate Student

Divya Koyyalagunta

Graduate Student

Ethan Kulman

Graduate Student

Kaitlin U Laverty

Postdoctoral Fellow

Olga Lyudovyk
Olga Lyudovyk

Graduate Student

Ellen T. Mammen
Ellen T. Mammen

Senior Administrative Assistant

Hussein Mohsen

Postdoctoral Fellow

Leah Morales

Graduate Student

Jingping Qiao

Graduate Student

Ian Shi

Graduate Student

Aditya Sinha

Graduate Student

Cyrus Tam

Graduate Student

Michael Toomey

Graduate Student

Nik von Krosigk

Graduate Student

Lab Alumni
Amir Ahmadi

Research Scientist at Autodesk AI Lab

Gurnit Atwal

Postdoctoral Research Fellow, Memorial Sloan Kettering Cancer Center, New York

Chris Cremer

Research Intern, Facebook AI, Pittsburgh

Amit Deshwar

Research Scientist, Deep Genomics

Simon Eng

Research Associate in Yeung Lab at SickKids, Toronto

Anna Goldenberg

Associate Professor, Computer Science, University of Toronto

Gavin Gray

Postdoctoral Research Fellow, Vector Institute, Toronto

Kevin Ha

Data Scientist, BioSymetrics, Toronto

Seong Woo Han

Graduate Student, University of Pennsylvania

Wei Jiao

Research Scientist, Ontario Institute for Cancer Research

Arttu Jolma

Postdoctoral Research Fellow, Hughes Lab, Toronto

Hilal Kazan

Associate Professor, Computer Science, Antalya Bilim University

Xiao Li

Assistant Professor, Case Western University

Sepand Mavandadi

Software Engineer at Amazon

Sara Mostafavi

Associate Professor, Computer Science, University of Washington; Canada CIFAR AI Chair

Gerald Quon

Assistant Professor, Computer Science, University of California Davis

Martin H. Radfar

Research Assistant Professor in Computer Science, Stony Brook University

Debashish Ray
Rozita Razavi
Yulia Rubanova

Research Scientist, DeepMind, London

Alina Selega

Postdoctoral Research Fellow, Lunenfeld Research Institute, Toronto

Kimberly Skead

Graduate Student

Linda Sundermann

Research Scientist, Silicon Valley start-up

Shankar Vembu

Founder: Argmix - A Technology Consulting Firm Specializing in Machine Learning

David Warde-Farley

Research Scientist at DeepMind, London

Lab Affiliations


  • Clarivate Web of Science Highly Cited Research (2018-present)
  • CIFAR Artificial Intelligence Chair (2018-present)
  • Assigned RNA-binding preferences to >20% of metazoan RNA-binding proteins (and >10% of eukaryotic RBPs) (w/ Timothy Hughes)
  • Developed GeneMANIA algorithm and website (w/ Gary Bader)

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Doctors and faculty members often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community.

MSK requires doctors and faculty members to report (“disclose”) the relationships and financial interests they have with external entities. As a commitment to transparency with our community, we make that information available to the public.

Quaid Morris discloses the following relationships and financial interests:

  • Microsoft Corporation

The information published here is for a specific annual disclosure period. There may be differences between information on this and other public sites as a result of different reporting periods and/or the various ways relationships and financial interests are categorized by organizations that publish such data.

This page and data include information for a specific MSK annual disclosure period (January 1, 2022 through disclosure submission in spring 2023). This data reflects interests that may or may not still exist. This data is updated annually.

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