I am Chudi Zhong. I am a Ph.D. candidate in computer science at Duke University, advised by Prof. Cynthia Rudin. I am also working closely with Prof. Margo Seltzer on my research projects. I recieved B.S. in Statistics and Information Science from UNC-Chapel Hill and M.S. in Statistical Science from Duke.

My research focuses on interpretable machine learning, which is crucial for responsible and trustworthy AI. My goal is to develop interpretable machine learning algorithms and pipelines to facilitate human-model interaction for high-stakes decision-making problems. Specifically, I focus on two main topics:

  1. build algorithms to optimize interpretable models to achieve performance comparable to black box counterparts in an efficient and scalable way
  2. introduce a new machine learning paradigm, called learning Rashomon sets, to break the interaction bottleneck between users and machine learning algorithms by enumerating and visualizing all well-performing models.

Please find more about my research projects here.

Selected Awards


Publications

( indicates co-first authors, equal contribution)

Exploring and Interacting with the Set of Good Sparse Generalized Additive Models, NeurIPS 2023   Paper Code Video

Chudi Zhong, Zhi Chen, Jiachang Liu, Margo Seltzer, Cynthia Rudin

OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems, NeurIPS 2023 (Spotlight)   Paper

Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin

Exploring the Whole Rashomon Set of Sparse Decision Trees, NeurIPS 2022 (Oral)   Paper Code

Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo Seltzer, Cynthia Rudin

  • – Finalist, Data Mining Best Student Paper Award, INFORMS, 2022

FasterRisk: Fast and Accurate Interpretable Risk Scores, NeurIPS 2022   Paper Code

Jiachang Liu, Chudi Zhong, Boxuan Li, Margo Seltzer, Cynthia Rudin

TimberTrek: Exploring and Curating Trustworthy Decision Trees with Interactive Visualization, IEEE VIS 2022   Paper Code Demo

Zijie Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo Seltzer

Fast Sparse Classification for Generalized Linear and Additive Models, AISTATS 2022   Paper Code

Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin

Fast Sparse Decision Tree Optimization via Reference Ensembles, AAAI 2022  Paper Code

Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo Seltzer

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges, Statistics Surveys 2022   Paper

Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong

Generalized and Scalable Optimal Sparse Decision Trees, ICML 2020   Paper Code

Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer