About Me

I’m a third-year Ph.D. student in Computer Science at Shanghai Jiao Tong University (SJTU), advised by Prof. Quanshi Zhang. I’m a member of the Lab for Interpretable Machine Learning. Previously, I enrolled in the Dual Degree Program at University of Michigan - Shanghai Jiao Tong University Joint Institute (UM-SJTU JI), in which I obtained a B.S.Eng. degree in Computer Science at University of Michigan, Ann Arbor, and a B.S.Eng. degree in Electrical and Computer Engineering (ECE) at Shanghai Jiao Tong University.

Currently, my research focuses on Explainable AI (XAI):

  • Explaining artificial intelligence models (especially deep neural networks) with symbolic concepts
  • Analyzing the dynamics of symbolic concepts learned by neural networks during the training process
  • Manipulating/debugging neural networks at the concept level.
  • I also have a broad interest in general topics in machine learning, computer vision, and trustworthy large language models (LLMs).

News

  • [2024.11-12] Talks at University of California, Los Angeles (UCLA) / University of Southern California (USC) / University of California, Berkeley (UCB) / Johns Hopkins University (JHU) / University of Pennsylvania (UPenn). An unforgettable journey in the U.S.!
  • [2024.10] Remote talk at Carnegie Mellon University (CMU) with Prof. Quanshi Zhang.
  • [2024.09] Talk at “AI+X” National Excellent PhD Forum at Peking University.
  • [2024.09] One paper (see Project page) accepted by NeurIPS 2024!
  • [2024.01] One paper (see Project page) accepted by ICLR 2024!

Publications

(* indicates equal contribution)

Preprints

  • Revisiting Generalization Power of a DNN in Terms of Symbolic Interactions
    Lei Cheng, Junpeng Zhang, Qihan Ren, Quanshi Zhang
    arxiv 2025 / Paper

Conference papers

  • Towards the Dynamics of a DNN Learning Symbolic Interactions
    Qihan Ren*, Junpeng Zhang*, Yang Xu, Yue Xin, Dongrui Liu, and Quanshi Zhang
    NeurIPS 2024 / Paper / Zhihu / Project page

  • Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in DNNs
    Qihan Ren, Jiayang Gao, Wen Shen, and Quanshi Zhang
    ICLR 2024 / Paper / Code / Zhihu / Project page

  • Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities
    Dongrui Liu*, Huiqi Deng*, Xu Cheng, Qihan Ren, Kangrui Wang, and Quanshi Zhang
    NeurIPS 2023 / Paper / Code

  • Bayesian Neural Networks Avoid Encoding Perturbation-sensitive and Complex Concepts
    Qihan Ren*, Huiqi Deng*, Yunuo Chen, Siyu Lou, and Quanshi Zhang
    ICML 2023 / Paper / Code / Video

  • Discovering and Explaining the Representation Bottleneck of DNNs
    Huiqi Deng*, Qihan Ren*, Hao Zhang, and Quanshi Zhang
    ICLR 2022 (Oral) / Paper / Code / Video / Zhihu

  • Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
    Wen Shen, Qihan Ren, Dongrui Liu, and Quanshi Zhang
    NeurIPS 2021 / Paper / Code / Video

Journal papers

  • Rotation-Equivariant Quaternion Neural Networks for 3D Point Cloud Processing
    Wen Shen, Zhihua Wei, Qihan Ren, Binbin Zhang, Shikun Huang, Jiaqi Fan, and Quanshi Zhang
    IEEE T-PAMI 2024 / Paper

Book chapters

  • Engaged in the writing of the book “Introduction to Explainable Artificial Intelligence” (in Chinese 可解释人工智能导论) as a chapter co-author.
    Book link

Presentations and Invited Talks

  • [2024.11-12] Can inference logic of a neural network be faithfully explained as symbolic concepts? Talks at University of California, Los Angeles (UCLA) / University of Southern California (USC) / University of California, Berkeley (UCB) / Johns Hopkins University (JHU) / University of Pennsylvania (UPenn).
  • [2024.10] Can inference logic of a neural network be faithfully explained as symbolic concepts? Remote talk at Carnegie Mellon University (CMU) with Prof. Quanshi Zhang.
  • [2024.09] Theory and dynamical analysis of symbolic concepts encoded by deep neural networks. “AI+X” National Excellent PhD Forum (“AI+X”全国优秀博士生论坛) at Peking University.
  • [2022.04] Discovering and explaining the representation bottleneck of DNNs. At TechBeat with Huiqi Deng. See recording link.
  • [2022.03] Discovering and explaining the representation bottleneck of DNNs. At BAIYULAN OPEN AI, with Huiqi Deng. See recording link.

Selected Honors and Awards

  • [2024.12] First Prize in the Excellent PhD Forum at John Hopcroft Center, SJTU
  • [2022.06] Outstanding graduate of Shanghai Jiao Tong University
  • [2022.01] James B. Angell Scholar
  • [2021.12] Dean’s List of University of Michigan
  • [2020.10] National Scholarship (ranking 1/244)
  • [2019.10] National Scholarship (ranking 1/244)

Teaching

  • Machine Learning (CS3308 & CS3612), SJTU. Spring 2023 / Spring 2024.
    Teaching assistant
    Instructor: Quanshi Zhang
  • Academic Writing (VY100 & VY200), SJTU. Fall 2022 / Fall 2023.
    Teaching Assistant
    Instructor: Andrew Yang