Shichang Zhang
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Shichang Zhang

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About Me

I am a postdoctoral fellow at the D^3 Institute at Harvard University. 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, where I held cutting-edge research papers readings, AI courses studies, and hosted 13 guest speakers from both academia and industry. The AI courses we covered included neurosymbolic methods, diffusion models, large language models, differential geometry, spectral graph theory, neural radiance fields, and many more. Slides and recordings of the reading groups can be found here. My presentations at the reading group can be found under Talks.

Contact

3551 (ScAI Lab), Bolter Hall, UCLA, CA, 90095
E-mail: shichang AT cs DOT ucla DOT edu
[Google Scholar] [GitHub] [Twitter] [LinkedIn] [CV]

What's New

  • [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]

  • [Feb 2024] Give a talk on Explainable AI for Graph Data and More at the AI4LIFE Group at Harvard.

  • [Dec 2023] Our paper on LLM-based explainable molecular concept learning is accepted by the XAI4Sci workshop at AAAI 2024 [PDF]

  • [Dec 2023] Our paper on GNNs for explainable material science is accepted by the AI4Mat workshop at NeurIPS 2023 [PDF]

  • [Aug 2023] Certified Excellence in Reviewing for KDD 2023 (30 in 1551)

  • [July 2023] I am excited to be selected as one of the 2023 Amazon Fellows

  • [July 2023] I am excited to receive the J.P. Morgan Chase AI Ph.D. Fellowship

  • [July 2023] Give a talk on the PaGE-Link paper at Amazon Trans.AI Research Talk Series [slides]

  • [June 2023] Our survey paper on GNN acceleration is on arXiv now, which covers algorithms, systems, and customized hardware [PDF]
    Find out older news

Research Interests

My research interests include

  • Explainable AI

  • Large Language Models

  • Graph Data Mining

  • Data Valuation

  • Model Efficiency

Recent Publications and Pre-prints

  1. Hierarchical Compression of Text-Rich Graphs via Large Language Models (pre-print)
    Shichang Zhang, Da Zheng, Jiani Zhang, Qi Zhu, Xiang Song, Soji Adeshina, Christos Faloutsos, George Karypis, Yizhou Sun. [PDF]

  2. Self-Control of LLM Behaviors by Compressing Suffix Gradient into Prefix Controller (pre-print)
    Min Cai, Yuchen Zhang, Shichang Zhang, Fan Yin, Difan Zou, Yisong Yue, Ziniu Hu [PDF] [Code] [Website]

  3. Efficient Ensembles Improve Training Data Attribution (pre-print)
    Junwei Deng, Ting-Wei Li, Shichang Zhang, Jiaqi Ma [PDF]

  4. FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion (ACL 2024 Findings)
    Fred Xu, Song Jiang, Zijie Huang, Xiao Luo, Shichang Zhang, Yuanzhou Chen, Yizhou Sun [PDF][Code]

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

  6. Automated Molecular Concept Generation and Labeling with Large Language Models (XAI4Sci@AAAI 2024)
    Shichang Zhang*, Botao Xia*, Zimin Zhang*, Qianli Wu*, Fang Sun, Ziniu Hu, Yizhou Sun (*equal contribution) [PDF]

  7. Laplacian Score Benefit Adaptive Filter Selection for Graph Neural Networks (SDM 2024)
    Yewen Wang, Shichang Zhang, Junghoo Cho, Yizhou Sun [PDF]

  8. Predicting ICU Length of Stay: A Graph Learning-based Explainable AI Approach (WITS 2023)
    Tianjian Guo, Shichang Zhang, Indranil Bardhan, Ying Ding [PDF]

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

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

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

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

Full list of publications

Honors and Awards