Mingze Dong

Yale University.

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I am a CBB (Computational Biology & Bioinformatics) PhD student at Yale University, fortunate to be co-advised by Prof. Yuval Kluger and Prof. Rong Fan. Before my PhD, I earned a BS degree in Integrated Science from Peking University, focusing on applied math and biology. I also conducted research in the Lin lab and van Dijk lab.

My research interests lie in representation learning for biological data, in particular genomics data. My works aim to cover both theoretical rigor and superior empirical performance. My earlier works include applications for single-cell perturbation modeling (CINEMA-OT, Nature Methods), understanding GNN performance through random matrix theory (ESNR, ICML), and Granger causal inference in spatial omics data (GEASS, ICLR Spotlight). More recently, I develop identifiability theories and practical implementations to disentangle representations in spatial omics data (SIMVI, Nature Communications Editor Highlight) and massive single-cell compendiums (scShift), which overcomes the well-known batch effect problem. Additionally, I am engaged in extensive collaborations with biologists to analyze real-world biological data (Nature, with more publications in progress).

Feel free to contact me if you are interested in potential collaboration or just a chat!

selected publications

  1. Nature Methods
    Causal identification of single-cell experimental perturbation effects with CINEMA-OT
    Mingze Dong, Bao Wang, Jessica Wei, and 8 more authors
    Nature Methods, 2023
  2. Preprint
    Scaling deep identifiable models enables zero-shot characterization of single-cell biological states
    Mingze Dong, Kriti Agrawal, Rong Fan, and 3 more authors
    bioRxiv, 2024
  3. Nature Commun
    Editor’s Highlight
    SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data
    Mingze Dong, David Su, Harriet Kluger, and 2 more authors
    Nature Communications (Featured Article, <5%), 2025
  4. Preprint
    Predicting cellular responses to perturbation across diverse contexts with STATE
    Abhinav Adduri, Dhruv Gautam, Beatrice Bevilacqua, and 8 more authors
    bioRxiv, 2025
  5. Preprint
    Pertpy: an end-to-end framework for perturbation analysis
    Lukas Heumos, Yuge Ji, Lilly May, and 25 more authors
    bioRxiv, 2024
  6. ICML
    Towards Understanding and Reducing Graph Structural Noise for GNNs
    Mingze Dong, and Yuval Kluger
    Proceedings of the 40th International Conference on Machine Learning, 2023