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