Jiachang Liu

prof_pic.jpg

fistName.lastName at cornell.edu

Cornell University

Ithaca, NY, 14853

I am an assistant research professor (postdoc) at the Center for Data Science for Enterprise and Society (CDSES) at Cornell University. My hosts are Professor Andrea Lodi and Professor Soroosh Shafiee.

Prior to joining Cornell, I completed my Ph.D. in Electrical and Computer Engineering at Duke University, advised by Professor Cynthia Rudin. I also worked closely with Professor Margo Seltzer. Before Duke, I earned my B.S. degree with double majors in physics and mathematics and a minor in computer science from University of Michigan, Ann Arbor in 2018. Go Blue!

My research interest is organized around three thrusts:

1) creating interpretable and trustworthy ML solutions for high-stakes decision making, in domains such as healthcare, criminal justice, and finance;

2) designing efficient discrete and continuous optimization techniques to solve related optimization problems, which are usually nonconvex and have a combinatorial nature;

3) building open-source, efficient, and user-friendly software packages for the broad data science community.

The long-term goal is to let humans and machines seamlessly collaborate and complement each other.

news

Oct 22, 2024 I was awarded the runner-up award (2nd place) for the INFORMS Computing Society (ICS) Student Paper Award for my paper OKRidge.
Oct 21, 2024 I was awarded the runner-up award (2nd place) for the INFORMS Data Mining and Data Analysis (DMDA) Workshop Best Theoretical Paper for my paper FastSurvival.
Oct 5, 2023 Together with Cynthia Rudin, Margo Seltzer, and Chudi Zhong, I was awarded the Bell Labs Prize 2023 at 2nd place for work on trustworthy AI.

Publications

  1. NeurIPS
    FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models
    Jiachang Liu, Rui Zhang, and Cynthia Rudin
    Advances in Neural Information Processing Systems (NeurIPS) 2024
  2. ICML
    Position: Amazing Things Come From Having Many Good Models
    Cynthia Rudin, Chudi Zhong*, Lesia Semenova*, Margo Seltzer*, Ronald Parr*, Jiachang Liu*, Srikar Katta*, Jon Donnelly*, Harry Chen*, and Zachery Boner*
    Proceedings of the International Conference on Machine Learning (ICML), Spotlight 2024
  3. NeurIPS
    OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems
    Jiachang Liu, Sam Rosen, Chudi Zhong, and Cynthia Rudin
    Advances in Neural Information Processing Systems (NeurIPS), Spotlight 2023
  4. NeurIPS
    Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
    Chudi Zhong*, Zhi Chen*, Jiachang Liu, Margo Seltzer, and Cynthia Rudin
    Advances in Neural Information Processing Systems (NeurIPS) 2023
  5. NeurIPS
    FasterRisk: Fast and Accurate Interpretable Risk Scores
    Jiachang Liu*, Chudi Zhong*, Boxuan Li, Margo Seltzer, and Cynthia Rudin
    Advances in Neural Information Processing Systems (NeurIPS) 2022
  6. AISTATS
    Fast Sparse Classification for Generalized Linear and Additive Models
    Jiachang Liu, Chudi Zhong, Margo Seltzer, and Cynthia Rudin
    In International Conference on Artificial Intelligence and Statistics 2022
  7. ACL-W
    What Makes Good In-Context Examples for GPT-3?
    Jiachang Liu, Dinghan Shen, Yizhe Zhang, William B Dolan, Lawrence Carin, and Weizhu Chen
    In Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, Association for Computational Linguistics 2022
  8. TPAMI
    Representing Graphs via Gromov-Wasserstein Factorization
    Hongteng Xu, Jiachang Liu, Dixin Luo, and Lawrence Carin
    IEEE Transactions on Pattern Analysis and Machine Intelligence 2022
  9. ICML
    CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information
    Pengyu Cheng, Weituo Hao, Shuyang Dai, Jiachang Liu, Zhe Gan, and Lawrence Carin
    In International Conference on Machine Learning 2020