Zhao Xu, Ph.D. student Department of Computer Science & Engineering Texas A&M University 359 L.F. Peterson Building 435 Nagle Street College Station, TX 77843 E-mail: zhaoxu@tamu.edu Google Scholar

Short Biography

I am currently a 4th-year Ph.D. student in Department of Computer Science & Engineering, Texas A&M University. My advisor is Dr. Shuiwang Ji.

Research Interests

• Machine Learning

• Deep Learning

• Self-supervised Learning

• Computational Chemistry

Education

• Ph.D., Computer Engineering, Texas A&M University, Spring 2020 - Present

• M.S., Biomedical Engineering, University of Michigan -- Ann Arbor, Fall 2015 - Spring 2017

• B.S., Biology, University of Tsukuba, September 2011 - July 2015

Experience

• Graduate Research Assistant, Texas A&M University, College Station, TX, Jan 2020 - Present

• Ph.D. Intern, Applied Research Scientist, Cruise LLC, Sunnyvale, CA, May 2023 - August 2023

• Ph.D. Intern, Applied Research Scientist, Cruise LLC, San Francisco, CA, May 2022 - August 2022

• Quantitative Analyst, Aurum Capital Management, Corp., San Jose, CA, October 2017 - December 2019

Publications

• Haiyang Yu, Zhao Xu, Xiaoning Qian, Xiaofeng Qian, and Shuiwang Ji
  
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
The 40th International Conference on Machine Learning (ICML), 2023
• Yaochen Xie*, Zhao Xu*, and Shuiwang Ji (* equal contribution)
  
Self-Supervised Representation Learning via Latent Graph Prediction
The 39th International Conference on Machine Learning (ICML), 2022
• Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, and Shuiwang Ji
  
Self-supervised learning of graph neural networks: A unified review
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
• Zhengyang Wang*, Meng Liu*, Youzhi Luo*, Zhao Xu*, Yaochen Xie*, Limei Wang*, Lei Cai*, Qi Qi, Zhuoning Yuan, Tianbao Yang, and Shuiwang Ji (* equal contribution)
  
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Bioinformatics, 2022
• Qi Qi*, Youzhi Luo*, Zhao Xu*, Shuiwang Ji, and Tianbao Yang (* equal contribution)
  
Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence
The 35th Conference on Neural Information Processing Systems (NeurIPS), 2021
• Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, and Shuiwang Ji (* equal contribution)
  
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Journal of Machine Learning Research (JMLR), 2021
• Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
NeurIPS 2021 AI for Science Workshop, 2021

Preprints

• Zhao Xu*, Yaochen Xie*, Youzhi Luo, Xuan Zhang, Xinyi Xu, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, Shuiwang Ji (* equal contribution)
3D Molecular Geometry Analysis with 2D Graphs
arXiv: 2305.13315, 2023
• Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, YuQing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stark, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alan Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Lio, Rose Yu, Stephan Gunnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, and Shuiwang Ji (* equal contribution)
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
arXiv: 2307.08423, 2023
• Shengchao Liu, Yutao Zhu, Jiarui Lu, Zhao Xu, Weili Nie, Anthony Gitter, Chaowei Xiao, Jian Tang, Hongyu Guo, and Anima Anandkumar
A Text-guided Protein Design Framework
arXiv: 2302.04611, 2023

Services

Conference Reviewer

• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022

• Conference on Neural Information Processing Systems (NeurIPS), 2022, 2023

• Learning on Graphs Conference (LOGS), 2022, 2023

• International Conference on Learning Representations (ICLR), 2023, 2024

• International Conference on Machine Learning (ICML), 2023

• ACM International Conference on Web Search and Data Mining (WSDM), 2023

Journal Reviewer

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)