Zongliang (Jerry) Ji

Ph.D. Candidate @ University of Toronto, Department of Computer Science

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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. :smile:
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]

  1. ICLR
    Can we generate portable representations for clinical time series data using LLMs?
    Zongliang Ji, Yifei Sun, Andre Carlos Kajdacsy-Balla Amaral, and 2 more authors
    In The Fourteenth International Conference on Learning Representations , 2026
  2. MIDL
    Unpaired Multimodal Learning for Biological Datasets
    Zongliang Ji, Cian Eastwood, Anna Goldenberg, and 4 more authors
    In Medical Imaging with Deep Learning , 2026
  3. ML4H
    Dialogue to Question Generation for Evidence-based Medical Guideline Agent Development
    Zongliang Ji*, Ziyang Zhang*, Xincheng Tan, and 5 more authors
    Machine Learning for Healthcare Symposium, 2025
  4. CHIL
    ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orders
    Zongliang Ji, Andre Carlos Kajdacsy-Balla Amaral, Anna Goldenberg, and 1 more author
    In Conference on Health, Inference, and Learning , 2025
  5. ICLR LMRL
    Multi-modal disentanglement of spatial transcriptomics and histopathology imaging
    Hassaan Maan, Zongliang Ji, Elliot Sicheri, and 6 more authors
    Learning Meaningful Representations of Life (LMRL) Workshop, 2025
  6. TPAMI
    Regularized loss with hyperparameter estimation for weakly supervised single class segmentation
    Zongliang Ji, and Olga Veksler
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
  7. GCC
    Machine learning in computational histopathology: Challenges and opportunities
    Michael Cooper*, Zongliang Ji*, and Rahul G Krishnan
    Genes, Chromosomes and Cancer, 2023
  8. MIDL
    Considerations for data acquisition and modeling strategies: Mitosis detection in computational pathology
    Zongliang Ji*, Philip Rosenfield, Christina Eng, and 6 more authors
    In Medical Imaging with Deep Learning , 2023
  9. BMVC
    Weakly Supervised Semantic Segmentation: From Box to Tag and Back.
    Zongliang Ji, and Olga Veksler
    In Proceedings of the British Machine Vision Conference (BMVC) , 2021