Dr. Ye Wei joins City University of Hong Kong as Assistant Professor in the Department of Data Science in January 2025. He earned his Bachelor's degree from the University of Twente in the Netherlands and his PhD from the Max Planck Institute for Sustainable Materials and Intelligent Systems in Germany from 2018 to 2021. Dr. Wei was a postdoctoral scholar at the Institute of Interdisciplinary Information Science at Tsinghua University from 2021 to 2023. He then continued his postdoctoral research at the School of Bioengineering at EPFL from 2023 to 2024, where he focused on developing data-driven tools for biological applications.
Dr. Wei's research centers on designing data-driven methodologies to derive optimal solutions from limited datasets. His work addresses high-dimensional, nonlinear challenges in complex real-world systems using optimization techniques and generative model, with a particular emphasis on applications such as materials discovery, quantum computing and protein design. His research has been featured in prestigious journals, including Science, Nature Computational Science and Nature Communications, and has received wide media coverage in outlets such as MIT Technology Review and Chemistry World.
Previous Experience
- Mar 2023 - Dec 2024, Senior Researcher, EPFL.
- Jun 2021 - Mar 2023, Research Fellow, Tsinghua University.
- Apr 2018 - Feb 2021, PhD, Max Planck Institute for Sustainable Materials.
Journal
- (Sep 2025). Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search. Advanced Science. e13771. doi:10.1002/advs.202513771
- (Aug 2025). Deep active optimization for complex systems. Nature Computational Science. doi:10.1038/s43588-025-00858-x
- (Nov 2024). Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography. Nature Communications. doi:10.1038/s41467-023-43314-y
- (Oct 2024). Near-theoretical strength and deformation stabilization achieved via grain boundary segregation and nano-clustering of solutes. Nature Communications. doi:10.1038/s41467-024-53349-4
- (Aug 2024). From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph‐Based Deep Learning. Advanced Science. doi:10.1002/advs.202405404
- (Oct 2023). Machine learning-enabled constrained multi-objective design of architected materials. Nature Communications. doi:10.1038/s41467-023-42415-y
- (Oct 2022). Machine learning-enabled high-entropy alloy discovery. Science. 378. doi:10.1126/science.abo4940
- (Aug 2022). A mechanically strong and ductile soft magnet with extremely low coercivity. Nature. 608. doi:10.1038/s41586-022-04935-3
- (Sep 2021). Ultrastrong and ductile soft magnetic high‐entropy alloys via coherent ordered nanoprecipitates. Advanced Materials. 33. doi:10.1002/adma.202102139
- (Feb 2021). Machine-learning-enhanced time-of-flight mass spectrometry analysis. Patterns. doi:10.1016/j.patter.2020.100192
- (Aug 2020). Crystal–glass high‐entropy nanocomposites with near theoretical compressive strength and large deformability. Advanced Materials. doi:10.1002/adma.202002619
Service in CityUHK
Research / Thesis Supervision
- Jan 2025 - Now, Master Thesis.
Teaching Service
- Jan 2025 - Apr 2025, Master, Statistical methods for categorical data analysis.
Last update date :
06 Nov 2025
