Contacts

Degrees

• Ph.D., Cornell University, United States (1985)
• M.Sc., Cornell University, United States (1983)
• M.Sc., Wayne State University, United States (1980)

Research interests

• Recently I am working on methods for personalized cancer immunotherapy. A key issue for personalized cancer immunotherapy is to discover neoantigens on the surface of cancer cells. We are developing automatic, highly sensitive and personalized methods to sequence those peptides and validate their immunogenicity.

Publications

Ming Li on Google Scholar

Feature article

University Professor Ian Munro’s Golden Jubilee — 50 years at the University of Waterloo

Degrees

• PhD, Computer Science, University of Toronto
• MSc, University of British Columbia
• BA, University of New Brunswick

Research interests

• Data structures, particularly fast and space-efficient structures
• Design, analysis and implementation of algorithms
• Database systems and data warehousing, particularly efficiency issues

Publications

Ian Munro on Google Scholar

Ian Munro on DBPL

Degrees

• PhD in Computational Science and Engineering, Georgia Institute of Technology, USA (2025)
• MSc in Computational Science and Engineering, Georgia Institute of Technology, USA (2023)
• Bachelor of Electronic and Information Engineering, Beihang University, Beijing, China (2019)

Research interests

• Machine learning
• Computational biology
• Single-cell sequencing data
• Algorithms for high-throughput multi-omics biological data

Research description

Professor Zhang’s research lies at the intersection of machine learning and computational biology, with a focus on developing methods for large-scale single-cell sequencing data. He studies representation learning across modalities and conditions, predictive modeling of cellular responses to perturbations, gene regulatory and cellular interaction network inference to uncover functional mechanisms, and the development of single-cell foundation models pretrained on large biological atlases. His long-term goal is to build interpretable computational models that capture biological heterogeneity and enable clinically relevant prediction at the patient level.

Publications

Ziqi Zhang on Google Scholar