Chengchang Liu (刘程畅)

Chengchang Liu, Ph.D candidate@CUHK Email: 7liuchengchang@gmail.com

About me

I am a Ph.D candidate under supervision of Prof. John C.S. Lui. I received a B.S. in Statistics from USTC at 2022.

Previously, I visited CUHK (SZ) in 2024, hosted by Prof. Zhi-Quan (Tom) Luo. I visited FDU in 2022, hosted by Prof. Luo Luo.

My research focuses on designing efficient optimization methods for large-scale problems and the intersection of quantum computing and optimization.

I am the PI for the NSFC basic research scheme for PhD students on ‘‘Second-Order Optimization for Large-Scale Machine Learning: Algorithms and Analysis’’ from 2025 to 2026.

I am currently on the job market and actively looking for postdoc or faculty position. Please feel free to contact me if you have openings or any information.

Publications

1. PANDA: Partially Approximate Newton Methods for Distributed Minimax Optimization with Unbalanced Dimensions.
   Minheng Xiao, Chengchang Liu, Cheng Chen, John C.S. Lui, Sen Na.
   Machine Learning, 2025

2. Solving Convex-Concave Problems with Õ(ɛ^{-4/7}) Second-Order Oracle Complexity.
   Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang.
   COLT, 2025 (best student paper)

3. Second-order Min-Max Optimization with Lazy Hessians.
   Lesi Chen, Chengchang Liu, Jingzhao Zhang.
   ICLR, 2025 (oral)

4. An Enhanced Levenberg–Marquardt Method via Gram Reduction.
   Chengchang Liu, Luo Luo, John C.S. Lui.
   AAAI, 2025

5. Quantum Algorithms for Non-smooth Non-convex Optimization.
   Chengchang Liu, Chaowen Guan, Jianhao He, John C.S. Lui.
   NeurIPS, 2024

6. Quantum Algorithm for Online Exp-concave Optimization.
   Jianhao He, Chengchang Liu, Xutong Liu, Lvzhou Li, John C.S. Lui.
   ICML, 2024

7. Communication Efficient Distributed Newton Method over Unreliable Networks.
   Ming Wen, Chengchang Liu, Yuedong Xu.
   AAAI, 2024

8. Communication Efficient Distributed Newton Method with Fast Convergence Rates.
   Chengchang Liu, Lesi Chen, Luo Luo, John C.S. Lui.
   KDD, 2023

9. Block Broyden's Methods for Solving Nonlinear Equations.
   Chengchang Liu, Cheng Chen, Luo Luo, John C.S. Lui.
   NeurIPS, 2023

10. Quasi-Newton Methods for Saddle Point Problems.
    Chengchang Liu, Luo Luo.
    NeurIPS, 2022 (spotlight)

11. Partial-Quasi-Newton Methods: Efficient Algorithms for Minimax Optimization Problems with Unbalanced Dimensionality.
    Chengchang Liu, Shuxian Bi, Luo Luo, John C.S. Lui.
    KDD, 2022 (best paper runner-up)

Preprints (selected)

1. Computationally Faster Newton Methods by Lazy Evaluations.
   Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang.
   arXiv preprint, 2025

2. Symmetric Rank-k Methods.
   Chengchang Liu, Cheng Chen, Luo Luo.
   arXiv preprint, 2023

Selected Awards

• Mark Fulk Award for the Best Student Paper
  The 38th Annual Conference on Learning Theory, 2025.

• Best Paper Award Runner-Up
  The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022.

• NSFC Basic Research Scheme for Ph.D. Student
  National Natural Science Foundation of China, 2025-2026.

• National Scholarship
  Ministry of Education of the People’s Republic of China, 2018.

Services

• Conference Reviewer: NeurIPS 2023-25, ICLR 2024-25, AISTATS 2024, ICML 2024-25, AAAI 2025.
  

• Journal Reviewer: IEEE TPAMI.