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UCLA DM

Shichang (Ray) Zhang

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About Me

I am Shichang (Ray) Zhang. I am a postdoctoral fellow at the Harvard Business School AI Institute working with Professor Hima Lakkaraju. I received my Ph.D. in Computer Science from University of California, Los Angeles (UCLA) advised by Professor Yizhou Sun. My Ph.D. research was generously supported by the J.P. Morgan Chase AI Ph.D. Fellowship and the Amazon Fellowship. Before UCLA, I received my M.S. and B.A., both in Statistics, from Stanford and Berkeley, respectively.

My research aims to scientifically understand AI to ensure it is trustworthy and beneficial to humanity. I have developed principled methods to analyze and improve the trustworthiness of AI systems across the full spectrum, from model mechanisms to training processes to data features.

Contact

Science and Engineering Complex (SEC) 6.220, 150 Western Ave, Boston, MA 02134
E-mail: shzhang AT hbs DOT edu

[CV][Google Scholar] [GitHub] [LinkedIn] [X]

What's New

  • [Apr 2026] One paper on accountability attribution and one position paper on XAI research fundations are accepted by ICML 2026.

  • [Apr 2026] Our paper on efficient ensemble for data attribution is accepted by TMLR 2026. [PDF]

  • [Mar 2026] Our GNN acceleration survey paper is accepted by CSUR 2026. [PDF]

  • [Dec 2025] Give a tutorial talk on explainable AI at NeurIPS 2025. [website]

  • [July 2025] Our paper on A Mechanistic View of How Post-Training Reshapes LLMs has been accepted by COLM 2025. [PDF]

  • [June 2025] Selected as one of the top 10% of reviewers for KDD 2025 again for the February cycle.

  • [Apr 2025] Our paper on A Mechanistic View of How Post-Training Reshapes LLMs has won the NENLP 2025 Outstanding Paper Award. [PDF][slides]

  • [Apr 2025] Our paper on A Mechanistic View of How Post-Training Reshapes LLMs has been selected as Oral presentation for NENLP 2025.

Find out older news

Selected Publications

  1. Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems
    Shichang Zhang, Hongzhe Du, Jiaqi W. Ma, Himabindu Lakkaraju
    ICML 2026 [PDF]

  2. How Post-Training Reshapes LLMs: A Mechanistic View on Knowledge, Truthfulness, Refusal, and Confidence
    Hongzhe Du*, Weikai Li*, Min Cai, Karim Saraipour, Zimin Zhang, Himabindu Lakkaraju, Yizhou Sun, Shichang Zhang (*equal contribution)
    COLM 2025 (NENLP Outstanding Paper) [PDF] [Code] [slides]

  3. An Explainable AI Approach using Graph Learning to Predict ICU Length of Stay
    Tianjian Guo, Indranil Bardhan, Ying Ding, Shichang Zhang
    ISR Oct. 2024 [PDF (official)] [PDF (preprint)]

  4. Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
    Haoyu Li*, Shichang Zhang*, Longwen Tang, Mathieu Bauchy, Yizhou Sun (*equal contribution)
    ICML 2024 [PDF] [Code]

  5. PaGE-Link: Graph Neural Network Explanation for Heterogeneous Link Prediction
    Shichang Zhang, Jiani Zhang, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos, Yizhou Sun
    WWW 2023 [PDF] [Code]

  6. GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games
    Shichang Zhang, Neil Shah, Yozen Liu, Yizhou Sun
    NeurIPS 2022 [PDF] [Code]

  7. Graph-less Neural Networks, Teach Old MLPs New Tricks via Distillation
    Shichang Zhang, Yozen Liu, Yizhou Sun, Neil Shah
    ICLR 2022 [PDF] [Code]

  8. A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
    Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan, Zijie Huang, Ziniu Hu, Yewen Wang, Yingyan (Celine) Lin, Jason Cong, Yizhou Sun
    CSUR 2026 [PDF]

Full list of publications

Honors and Awards