Zhiwei Nie-Institute of Science and Technology for Brain-inspired Intelligence

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Zhiwei Nie
Research Direction： AI for Science, Computational biology
Email：zhiwei_nie@fudan.edu.cn
Website：https://ai4edge-lab.com/

Brief Introduction：Zhiwei Nie is a tenure-track Assistant Professor and doctoral supervisor whose research focuses on AI for Science, particularly foundation models for biomolecules and their applications. He received his bachelor’s degree from Dalian University of Technology and his master’s and doctoral degrees from Peking University. He subsequently joined Fudan University as an independent principal investigator. Over the past five years, he has published nearly 30 research papers in leading international journals and conferences, including Nature Machine Intelligence, Nature Communications, ICML, and AAAI. His research has been widely featured by academic and scientific media organizations, including the Chinese Association for Artificial Intelligence, China Science Daily, and the Shanghai Association for Science and Technology. He is a core developer of Pengcheng Shennong, China’s first domestically developed, integrated hardware-software AI platform for life science research. His honors include the inaugural “Qingyuan Rising Scientist” from the Chinese Association for Artificial Intelligence, the “Youth Outstanding Paper Award” at the World Artificial Intelligence Conference, and recognition as the finalist for the 2022 ACM Gordon Bell Special Prize-the only finalist from China-as well as nomination for Peking University Person of the Year, among dozens of other distinctions. The research group welcomes applications from prospective master’s and doctoral students, research interns, research assistants, postdoctoral researchers, and assistant research fellows. Please visit the group website for further information.
Achievement：

[1] A unified evolution-driven deep learning framework for virus variation driver prediction. Nature Machine Intelligence, 2025.
[2] Running ahead of evolution—AI-based simulation for predicting future high-risk SARS-CoV-2 variants. IJHPCA, 2023. (ACM Gordon Bell Prize Finalist)
[3] ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics. AAAI, 2026.
[4] MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction. IJCAI Oral, 2025.
[5] Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition. ICML, 2026.
[6] Predicting protein stability changes upon mutations with dual-view ensemble learning from single sequence. Briefings in Bioinformatics, 2025.
[7] Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution. Briefings in Bioinformatics, 2025.
[8] Antibody-antigen neutralization prediction by integrating structural information distillation and physicochemical constraints. Briefings in Bioinformatics, 2026.