About

Profile picture xikunz2@cs.[university].edu
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I am a Ph.D. student in Computer Science at Stanford University. I generally focus on deep learning, generative AI, large language models and computer vision. I am very fortunate to have been working with Jure Leskovec, Emma Lundberg, Anshul Kundaje and Aaron Newman. Some of my already published work includes

  1. Graph machine learning (JODIE, GreaseLM, DRAGON, AutoPath)
  2. Understanding large language models (NumBERT)
  3. Common-sense reasoning (NumBERT, GreaseLM, DRAGON)
  4. Retrieval augmented models for knowledge-intensive tasks (GreaseLM, DRAGON)
  5. Foundation models (Foundation Models)
  6. AI in biomedicine (Virtual Cell)
Before Stanford, I was an AI Resident at Google Research working with Michael Collins on pretraining multimodal vision+language models. I was also a research intern at Google Research in summer 2019 working with Dan Roth and Ian Tenney on probing pretrained language models.

I received my B.S. in computer science at UIUC in 2019. I did research with Jiawei Han on data mining and heterogenous graph representation learning.

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Selected Publications

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities
Charlotte Bunne*, Yusuf Roohani*, Yanay Rosen*, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B. Burkhardt, Andrea Califano, Jonah Cool, Abby F. Dernburg, Kirsty Ewing, Emily B. Fox, Matthias Haury, Amy E. Herr, Eric Horvitz, Patrick D. Hsu, Viren Jain, Gregory R. Johnson, Thomas Kalil, David R. Kelley, Shana O. Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicholas J. Sofroniew, Fabian Theis, Christina V. Theodoris, Srigokul Upadhyayula, Marc Valer, Bo Wang, Eric Xing, Serena Yeung-Levy, Marinka Zitnik, Theofanis Karaletsos, Aviv Regev, Emma Lundberg, Jure Leskovec, Stephen R. Quake (*equal contribution)
Cell, Dec 2024
PDF Abstract Bibtex

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GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D Manning, Jure Leskovec
ICLR 2022 (Spotlight, Top 5%)
PDF Abstract Bibtex Code Video

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On the opportunities and risks of foundation models
Rishi Bommasani, …, Xikun Zhang, …, Percy Liang (116 authors)
arXiv 2021
PDF Abstract Bibtex Website

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Do Language Embeddings Capture Scales?
Xikun Zhang*, Deepak Ramachandran*, Ian Tenney, Yanai Elazar, Dan Roth (*equal contribution)
Findings of EMNLP 2020
EMNLP BlackboxNLP workshop 2020
PDF Abstract Bibtex Code Blog

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Predicting Embedding Trajectories for Temporal Interaction Networks
Srijan Kumar, Xikun Zhang, Jure Leskovec
KDD 2019 (Oral, Top 9%)
PDF Abstract Bibtex Code Website Video

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Other Publications

Deep Bidirectional Language-Knowledge Graph Pretraining
Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D Manning, Percy Liang*, Jure Leskovec* (*equal contribution)
NeurIPS 2022
AAAI DLG workshop 2023 (Best Paper Award)
PDF Abstract Bibtex Code Blog

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CLEVRER-Humans: Describing Physical and Causal Events the Human Way
Jiayuan Mao*, Xuelin Yang*, Xikun Zhang, Noah Goodman, Jiajun Wu (*equal contribution)
NeurIPS Datasets and Benchmarks Track 2022
PDF Abstract Bibtex Code Website

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Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery
Carl Yang, Mengxiong Liu, Frank He, Xikun Zhang, Jian Peng, Jiawei Han
ECML-PKDD 2018
PDF Abstract Bibtex Code

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TruePIE: Discovering Reliable Patterns in Pattern-Based Information Extraction
Qi Li*, Meng Jiang*, Xikun Zhang, Meng Qu, Timothy Hanratty, Jing Gao, Jiawei Han (*equal contribution)
KDD 2018 (Long Presentation, Top 11%)
PDF Abstract Bibtex Code Video

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Awards

Academic Services