McGill SIDISH AI Detects High-Risk Cancer Cells | AcademicJobs
Explore McGill University's SIDISH AI tool, which identifies high-risk cancer cells in pancreatic, breast, and lung tumors, bridging single-cell and bulk data for precision therapies.
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Jun Ding is an Associate Professor in the Department of Medicine at McGill University, affiliated with the Meakins-Christie Laboratories in the Faculty of Medicine and Health Sciences. He joined McGill University in 2021 as a tenure-track assistant professor and was promoted to associate professor in 2026. His academic background includes an MSc in Electrical Engineering from the University of Science and Technology of China in 2010, a PhD in Computer Science from the University of Central Florida in 2016, and a postdoctoral fellowship in Computational Biology at Carnegie Mellon University completed in 2020.
Ding leads a computational biology group focused on developing machine learning approaches, particularly probabilistic graphical models, to analyze, model, and visualize single-cell and bulk omics data, with an emphasis on longitudinal or spatial datasets. His research examines cell dynamics in biological processes related to developmental disorders, pulmonary diseases, and cancers, aiming to advance understanding of disease pathogenesis and support the development of new diagnostic and therapeutic strategies. He holds the Meakins-Christie Chair in Respiratory Research and is a recipient of an FRQS Junior 2 Scholar award. Ding is also an affiliate member of the Mila – Quebec AI Institute.
Explore McGill University's SIDISH AI tool, which identifies high-risk cancer cells in pancreatic, breast, and lung tumors, bridging single-cell and bulk data for precision therapies.