几何与泛函系列报告
报告题目:Mathematical AI for molecular data analysis
报告人:夏克林 教授
报告时间:2022年9月21日(周三)9:30 开始
报告地点:腾讯会议:339-571-260
报告摘要:Artificial intelligence (AI) based molecular data analysis has begun to gain momentum due to the great
advancement in experimental data, computational power and learning models. However, a major issue that
remains for all AIbased learning models is the efficient molecular representations and featurization.
Here we propose advanced mathematics-based molecular representations and featurization
(or feature engineering). Molecular structures and their interactions are represented as various simplicial
complexes (Rips complex, Neighborhood complex, Dowker complex, and Homcomplex), hypergraphs,
and Tor-algebrabased models. Molecular descriptors are systematically generated from various
persistent invariants, including persistent homology, persistent Ricci curvature, persistent spectral, and
persistent Toralgebra. These features are combined with machine learning and deep learning models,
including random forest, CNN, RNN, Transformer, BERT, and others. They have demonstrated great
advantage over traditional models in drug design and material informatics.
报告人简介: Dr. Kelin Xia obtained his Ph.D. degree from the Chinese Academy of Sciences in Jan
2013. He was a visiting scholar in the department of Mathematics, Michigan State University from
Dec 2009-Dec 2012. From Jan 2013 to May 2016, he worked as a visiting assistant professor at Michigan
State University. He joined Nanyang Technological University at Jun 2016. His research focused on
Mathematical AI for molecular sciences. He has published >60 papers and has been PI and Co-PI
for 15 grants (>3.0M SGD).