Info
Main
Research
Teaching
Talks
Miscellaneous
Links
UCLA DM

Shichang (Ray) Zhang

alt text 

About Me

I am Shichang (Ray) Zhang. I am a postdoctoral fellow at the D^3 Institute at Harvard University working with Professor Hima Lakkaraju. I received my Ph.D. in Computer Science from University of California, Los Angeles (UCLA), where I was very fortunate to have Professor Yizhou Sun as my advisor. My Ph.D. research was generously supported by the J.P. Morgan Chase AI Ph.D. Fellowship and the Amazon Fellowship. Before joining UCLA, I received my M.S. and B.A., both in Statistics, from Stanford and Berkeley respectively.

During my Ph.D. at UCLA, I organized the Data Mining Reading Group for two years. Slides and recordings of the reading groups can be found here. My presentations at the reading group can be found under Talks.

Contact

Science and Engineering Complex (SEC) 6.220, 150 Western Ave, Boston, MA 02134
E-mail: shzhang AT hbs DOT edu
[Google Scholar] [GitHub] [LinkedIn] [X] [CV]

What's New

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

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

  • [April 2025] Give a talk on AI Interpretability at Georgia Institute of Technology.

  • [April 2025] Give a talk on AI Interpretability at Emory University.

  • [Feb 2025] Serving as an Area Chair for ACL ARR 2025.

  • [Feb 2025] Our paper on Advancing Interpretability by Unifying Feature, Data and Model Component Attribution is on arXiv now. [PDF]

  • [Dec 2024] Selected as the top 10% of reviewers for KDD 2025.

  • [Nov 2024] Our paper on LLM-based explainable molecular concept learning is accepted by COLING 2025. [PDF]

  • [Oct 2024] Our paper on explainable graph learning for predicting ICU length of stay is accepted by ISR. [PDF]

  • [Oct 2024] Our paper on generalized group data attribution is on arXiv now. [PDF]

  • [July 2024] A Mind Map of Knowledge in LLMs.

  • [June 2024] Our paper on controlling LLM behaviors with LLM itself as a judge is on arXiv now. [PDF] [Code] [Website]

  • [May 2024] Defended my Ph.D. thesis “Explainable AI for Graph Data”.

  • [May 2024] Our paper on efficient ensembling for training data attribution is on arXiv now. [PDF]

  • [May 2024] Our paper on measure-theoretic compact fuzzy set representation for taxonomy expansion is accepted by ACL 2024 as Findings. [PDF]

  • [May 2024] Two papers on GNNs for explainable material science and benchmarking LLMs on scientific problems are accepted by ICML 2024. [PDF1] [PDF2]

Find out older news

Research Interests

My research interests include

  • Explainable AI

  • Data Attribution

  • Mechanistic Interpretability

  • Large Language Models

  • Graph Data Mining

  • Model Efficiency

Selected Publications and Pre-prints

  1. 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)
    NENLP 2025 Outstanding Paper [PDF] [slides]

  2. Building Bridges, Not Walls - Advancing Interpretability by Unifying Feature, Data and Model Component Attribution
    Shichang Zhang, Tessa Han, Usha Bhalla, Himabindu Lakkaraju
    BuildingTrust@ICLR 2025 [PDF]

  3. Automated Molecular Concept Generation and Labeling with Large Language Models
    Zimin Zhang*, Qianli Wu*, Botao Xia*, Fang Sun, Ziniu Hu, Yizhou Sun, Shichang Zhang (*equal contribution)
    COLING 2025 [PDF] [Code]

  4. 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]

  5. Generalized Group Data Attribution
    Dan Ley, Suraj Srinivas, Shichang Zhang, Gili Rusak, Himabindu Lakkaraju
    ATTRIB@NeurIPS 2024 [PDF]

  6. Efficient Ensembles Improve Training Data Attribution
    Junwei Deng*, Ting-Wei Li*, Shichang Zhang, Jiaqi Ma (*equal contribution)
    DMLR@ICML 2024 [PDF] [Code]

  7. 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]

  8. 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]

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

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

Full list of publications

Honors and Awards