报告人: Prof. Nevin L. Zhang
Department of Computer Science & Engineering
The Hong Kong University of Science & Technology
Traditional Chinese medicine (TCM) is an important avenue for disease prevention and treatment for the Chinese people and is gaining popularity among others. However, many remain skeptical and even critical of TCM because a number of its shortcomings. One key shortcoming is the lack of a solid foundation. We endeavor to alleviate this shortcoming and use machine learning techniques to establish a statistical foundation for TCM. When viewed as a black box, TCM diagnosis is simply a classifier that classifies patients into different classes based on their symptoms and signs. A fundamental question is: Do those classes exist in reality? To seek an answer from the machine learning
perspective, one would naturally use cluster analysis. Previous clustering methods are unable to handle the complexity of TCM. We have therefore developed a new clustering method in the form of latent tree models.
In this talk, we provide a concise summary of research work on latent tree models, discuss a learning algorithm, and present a case study to show how latent tree models can help in establishing a statistical foundation for TCM.
About the speaker:
Nevin L. Zhang is an associate professor at The Hong Kong University of Science & Technology (HKUST). He obtained his first PhD degree in Applied Math from
Beijing Normal University and his second PhD degree in Computer Science from University of British Columbia. He started his academic career at HKUST in 1994 and was substantiated in 2000. His general research interests lie in the field of reasoning and decision under uncertainty. He co-authored the variable elimination inference algorithm for Bayesian networks and the incremental pruning algorithm for partially observable Markov decision processes. Both algorithms are fundamental to their respective areas. Since substantiation,
his research activities have focused on latent structure models and statistical foundation for traditional Chinese medicine. He served on the editorial board of Journal of Artificial Intelligence Research from 1999 to 2002 and as an associate editor from 2003 to 2005. He currently serves on the advisory board of the journal. He has also lent his service to other journals and conferences such as UAI and AAAI.