報告題目：Mathematical AI for molecular data analysis
報 告 人：夏克林 教授 Nanyang Technological University
報告時間：2022年7月22日 10：00-11：00
報告地點：騰訊會議 ID：330-230-674
點擊鍊接入會，或添加至會議列表：https://meeting.tencent.com/dm/zIMBtOkOB1Ai
校内聯系人：郭斌 bguo@jlu.edu.cn
報告摘要： 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 AI-based 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 Hom-complex), hypergraphs, and Tor-algebra-based models. Molecular descriptors are systematically generated from various persistent invariants, including persistent homology, persistent Ricci curvature, persistent spectral, and persistent Tor-algebra. 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.
報告人簡介：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).
