• Assistant Professor, Computer Science & Engineering
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Yimeng Min

Educational Background

  • Ph.D., Computer Science, Cornell University – 2026
  • M.A.Sc., Electrical and Computer Engineering, University of Toronto – 2019
  • B.S., Physics, Nanjing University – 2017

Research Interests

    • Artificial intelligence for science
    • Computational sustainability
    • Generative models
    • Geometric deep learning

Awards & Honors

  • Conference on Neural Information Processing Systems (NeurIPS) Scholar Award – 2022
  • Ted Rogers Scholarship, University of Toronto – 2018
  • Candidate for Student of the Year, Nanjing University – 2017

Selected Publications

  • Min, Y. and Gomes, C.P. 2026. “Learning Unbiased Permutations via Flow Matching.” NeurIPS.
  • Min, Y., Bai, Y., and Gomes, C.P. 2023. “Unsupervised Learning for Solving the Travelling Salesman Problem.” NeurIPS.
  • Min, Y., Wenkel, F., Perlmutter, M., and Wolf, G. 2022. “Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?” NeurIPS.
  • Zhong, M., Tran, K., Min, Y., Wang, C. et al. 2020. “Accelerated Discovery of CO₂ Electrocatalysts Using Active Machine Learning.” Nature.
  • Min, Y., Wenkel, F., and Wolf, G. 2020. “Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks.” NeurIPS.
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