🧑💻 Biography
I am now a Ph.D. student of the Institute for Artificial Intelligence of Peking University, advised by Prof. Muhan Zhang. Our group’s github page is GraphPKU.
My recent work is centered around geometric deep learning, particularly on the exploration of the theoretical expressiveness of geometric models and designing powerful models within this field. If you’re also interested in related fields, welcome to contact me!
🎖 Honors and Awards
- 2020.12, 2021.12, 2022.12, National Scholarship for Undergraduate Student
- 2021.12, Outstanding Student Pioneer of Tianjin University (Nomination Award) (only 10 awardees and 5 nominated awardees university-wide per year)
- 2021.08, First Prize of National Zhou Peiyuan Competition on Mechanics (~0.3%)
- 2021.04, Meritorious Winner (First Prize) of ICM: Interdisciplinary Contest In Modeling
📖 Educations
2023.09 -, Ph.D. student, Institute for Artificial Intelligence, Peking University
2019.09 - 2023.07, B.E., School of Future Technology, Tianjin University
💻 Internships
- 2021.08 - 2022.04, Fib-Lab, Tsinghua University
📃 Publications
- Zian Li, Cai Zhou, Xiyuan Wang, Xingang Peng, Muhan Zhang, Geometric Representation Condition Improves Equivariant Molecule Generation, arXiv preprint, 2024.
- Zian Li, Xiyuan Wang, Shijia Kang, Muhan Zhang, On the Completeness of Invariant Geometric Deep Learning Models, arXiv preprint, 2024. (Source code)
- Zian Li, Xiyuan Wang, Yinan Huang, Muhan Zhang, Is Distance Matrix Enough for Geometric Deep Learning?, Advances in Neural Information Processing Systems, 2023. (Source code)