Research Group

I am moving my lab to Carnegie Mellon University in Fall 2023, and will be building up my research group anew.

Current group members

Yun William Yu headshot Yun William Yu - Assistant Professor - Computational Biology

William is a computational biologist and applied mathematician interested in compression, genomics, privacy, and sketching. Prior to moving to CMU, he was an assistant professor in the math department at the University of Toronto. He was a graduate student with Bonnie Berger in Mathematics at MIT, and a postdoctoral fellow with Griffin Weber in Biomedical Informatics at Harvard Medical School. William is originally from Huntingburg, Indiana, USA.

Google Scholar  -  Pubmed  -  ORCID  -  Github  -  Twitter
Jim Shaw headshot Jim Shaw - PhD Student - Mathematics

Jim is interested in applying mathematical techniques, usually with a combinatorial, probabilistic, or topological flavour, to problems in computational genomics. His current research interests include genome assembly, haplotyping, and metagenomics.

Personal Homepage  -  Google Scholar  -  Github
Grace Oualline headshot Grace Oualline - Undergraduate Student - Computational Biology and Biology

Grace Oualline is a computational biology researcher, currently pursuing a Bachelor of Science in Biology with an additional major in Computational Biology from Carnegie Mellon University. With a strong foundation in wet lab methodologies acquired through previous years of experience, she transitioned into computational research, interested in applying her knowledge of biology to analyze large biological datasets. Currently, Grace is collaborating with Brian in developing a computational biology tool, Skandiver, that aims to predict the precise locations of mobile elements within BioSeq reads.

LinkedIn
Brian Zhang headshot Xiaolei (Brian) Zhang - M.S. Student - Quantitative Biology and Bioinformatics

Brian is a computational biologist with a passion for experimental microbiology and immunology. He is interested in applying novel bioinformatics techniques to unravel more of the secrets of microbial life, such as defining host-microbe interactions and the role of certain compounds in immune response modulation. He is currently using genome assembly and metagenomics to identify putative mobile genetic elements within large-scale biological datasets.Brian is a computational biologist with a passion for experimental microbiology and immunology. He is interested in applying novel bioinformatics techniques to unravel more of the secrets of microbial life, such as defining host-microbe interactions and the role of certain compounds in immune response modulation. He is currently using genome assembly and metagenomics to identify putative mobile genetic elements within large-scale biological datasets.

LinkedIn
Molly Borowiak headshot Molly Borowiak - PhD Student - Computational Biology

 

 
Spencer Gibson headshot Spencer Gibson - M.S. Student - Biomedical Engineering

 

 

Join the group

The primary goal of our research group is to develop applied math/CS techniques and translate them to biological and medical problems. As such, we are interested in mathematicians and scientists with diverse backgrounds and training.

Graduate students Postdocs Internships, undergraduates, and visitors
Graduate students must be enrolled in a Carnegie Mellon degree program. My home department is computational biology, but I am also happy advising students from other allied departments, such as computer science or math. If you are an enrolled graduate student, please send an email with an explanation of your goals and your interest in the lab.

Unfortunately, we cannot admit external PhD students directly to the lab. If you are not currently a graduate student at CMU, consider applying to joint CMU-Pittsburgh Ph.D. program in computational biology.
Unfortunately, at the moment, we are not hiring postdocs.

We are interested in both more theoretically-minded and more computational/engineering-focused postdocs, though all postdocs should expect to be both. If you are interested in working with us, please email your CV, at least one paper you've written, a brief description of your background, and your goals in pursuing a postdoc.
If you are an undegraduate at Carnegie Mellon interested in computational biology research, please email with a description of your goals and interest in the lab, as well as relevant math, CS, and biology coursework you've taken.

Alumni

  Degree and research project Last seen
Ben Connors University of Toronto MSc in Math
Chasing Databases: The Theoretical Evolution of Data Migration.
Western Ontario Math PhD program
Yeonjoon (Philip) Choi) University of Toronto MSc in Math
Exploration of Persistence Landscapes as Early Warning Signals.
Kiran Deol University of Toronto Summer Research Experience (SUDS)
SlowMoMan: A web app for discovery of important features along user-drawn trajectories in 2D embeddings
University of Alberta undergrad in CS
Andrew Zheng University of Toronto BS, Mathematics / Cell & Molecular Biology
Mora: abundance aware metagenomic read re-assignment for disentangling similar strains
University of Toronto PhD program in Math
Siyue (Nina) Wang University of Toronto MScAc, Computer Science
Secure Cross-service Genomic Data Federated Analysis with GraphQL
 
Xinqi Shen University of Toronto MScAc, Applied Computing
Achieving Clinical Automation in Pediatric Emergency Medicine with Machine Learning Medical Directives
 
Luke Staniscia University of Toronto MSc, Mathematics
Image-centric compression of protein structures improves space savings
Scotiabank
Ziye (Rachel) Tao University of Toronto BS, Applied Math Specialist & Statistics Major
Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories
Harvard Data Science MSc program
Yeji Wi University of Toronto BS in Mathematics and Physics
Privacy-ppreserving machine learning techniques using homomorphic encryption in medicine
Politecnico di Milano Masters in Mathematical Engineering program
Abbas Hammoud University of Toronto MSc in Mathematics
Privacy-accuracy trade-offs in noisy digital exposure notifications