Shichang (Ray) Zhang

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About Me

I am Shichang (Ray) Zhang, an Assistant Professor in the Department of Information Systems at the W. P. Carey School of Business, Arizona State University. Before joining ASU, I was a postdoctoral fellow at the Harvard Business School AI Institute working with Hima Lakkaraju. I received my Ph.D. in Computer Science at UCLA, advised by Yizhou Sun. I received my M.S. in Statistics at Stanford and my B.A. in Statistics at Berkeley.

I am interested in the broad area of machine learning and artificial intelligence (AI). 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, from model mechanisms to training processes to data features. (1) Model-wise, I study large language models (LLMs) to reveal their internal mechanisms and reasoning capabilities, enabling task-specific interpretable models built on them. (2) Training-wise, I develop techniques to measure the training influence on AI behavior, providing new tools for training data assessment, model auditing, and credit assignment to developers. (3) Data-wise, I design methods to examine how data features drive AI decisions, allowing non-expert users to interpret and effectively use AI in healthcare, science, and e-commerce applications.

Contact

300 E Lemon St. BAC 522, Tempe, AZ 85287
E-mail: shichang.zhang AT asu DOT edu

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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. 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]

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

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

  7. 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