Elio (Keqiang) Yan [严克强]
Ph.D. Student, Texas A&M University
keqiangyan [AT] tamu.edu
Bio
I am currently a 4th year Ph.D. student in the Department of Computer Science & Engineering, Texas A&M University. My advisor is Prof. Shuiwang Ji, who leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory. I obtained my bachelor’s degree from Peking University in 2020, advised by Prof. Jiaying Liu. Here is my résumé.
My research interests are machine learning and AI for Science topics. Specifically, I am currently working on (1) LLM and graph geometric learning, (2) AI for science, and (3) generative modeling.
News
Selected Publications [Google Scholar]
* indicates equal contribution.
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian and Shuiwang Ji
(ICLR), 2024
A Latent Diffusion Model for Protein Structure Generation
Cong Fu*, Keqiang Yan*, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian and Shuiwang Ji
Learning on Graphs Conference (LoG), 2023
Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li, Kevin F Garrity, Vishu Gupta, Aldo H Romero, Jaron T Krogel, Kayahan Saritas, Addis Fuhr, Panchapakesan Ganesh, Paul RC Kent, Keqiang Yan, ..., Andrew Dale Rohskopf, Jason Hattrick-Simpers, Shih-Han Wang, Luke EK Achenie, Hongliang Xin, Maureen Williams, Adam J Biacchi, Francesca Tavazza
Npj Computational Materials
Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity
Sheng Gong, Keqiang Yan, Tian Xie, Yang Shao-Horn, Rafael Gomez-Bombarelli, Shuiwang Ji, and Jeffrey C. Grossman
Science Advances
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
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, ..., Tommi Jaakkola, Connor W Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji
under review
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian and Shuiwang Ji
Fortieth International Conference on Machine Learning (ICML), 2023
Periodic Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Yi Liu, Yuchao Lin and Shuiwang Ji
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS), 2022
GraphDF: A Discrete Flow Model for Molecular Graph Generation
Youzhi Luo, Keqiang Yan, and Shuiwang Ji
International Conference on Machine Learning (ICML), 2021
GraphEBM: Molecular Graph Generation with Energy-Based Models
Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji
EBM Workshop at ICLR, 2021
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
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
Journal of Machine Learning Research (JMLR), 2021
Multitask Attentive Network For Text Effects Quality Assessment
Keqiang Yan, Shuai Yang, Wenjing Wang and Jiaying Liu
International Conference on Multimedia and Expo (ICME), 2020
Services
Program Committee Member & Reviewer [Selected]
Annual Conference on Neural Information Processing Systems (NeurIPS) 2022, 2023
International Conference on Machine Learning (ICML) 2022, 2023
International Conference on Learning Representations (ICLR) 2023, 2024
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Résumé
Here is my résumé.
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Texas A&M University Aug 2020 - now
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Microsoft AI4Science, Redmond Summer 2023Research Intern
Mentor: Jake Smith -
Peking University Aug 2016 - Jul 2020