Zongliang (Jerry) Ji
Ph.D. Candidate @ University of Toronto, Department of Computer Science
I am a Computer Science Ph.D. candidate at the University of Toronto and a researcher at the Vector Institute, where I am co-advised by Prof. Anna Goldenberg and Prof. Rahul G. Krishnan. Currently, I am a Research Intern at Google Research, Health AI in Mountain View, focused on developing medical-grade LLM agents and benchmarks for clinical decision support.
My research interests lie at the intersection of machine learning and healthcare, specifically in clinical time-series representation learning, multi-modal contrastive learning, and the application of Large Language Models (LLMs) to Electronic Health Records (EHR). I am also deeply interested in associating diverse biological modalities for drug discovery, with a particular focus on integrating single-cell and spatial transcriptomics data.
Prior to my Ph.D., I was a Pre-doctoral Researcher at Microsoft Research New England within the Biomedical ML Group, where I worked on computational pathology and spatial transcriptomics. I earned my Master of Mathematics from the University of Waterloo, advised by Prof. Olga Veksler, and my undergraduate degrees in Computer Science and Mathematics from Union College, where my research journey began under the mentorship of Prof. Matthew Anderson and Prof. John Rieffel. I grew up in the beautiful coastal city of Qingdao, China.
news
| Apr 12, 2026 | Made this website, haven’t touch personal webpage since end of 2023. |
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| Jan 26, 2026 | Record2Vec accepted to ICLR 2026. See you in Rio! |
| Dec 01, 2025 | On the organizing team of ML4H 2025—hope to see you in San Diego! |
selected publications [full list]
- MIDLUnpaired Multimodal Learning for Biological DatasetsIn Medical Imaging with Deep Learning , 2026
- GCCMachine learning in computational histopathology: Challenges and opportunitiesGenes, Chromosomes and Cancer, 2023
- MIDLConsiderations for data acquisition and modeling strategies: Mitosis detection in computational pathologyIn Medical Imaging with Deep Learning , 2023