题目：Machine learning in drug development
报告人：Jian Zhang（Professor, University of Kent, Canterbury, UK）
The traditional drug discovery is based on the so-called trial-and-error paradigm. Over the past two decades, this paradigm has been dramatically changed due to the availability of large biomedical and pharmacological data and the rapid growth in computing power. In particular, this change has triggered a wave of developing advanced machine learning algorithms to make sense of these data, i.e. identifying low-dimensional patterns in high-dimensional data space. In this talk, I will review our recent developments in this field.
Jian Zhang is a Chair Professor and Head of Statistics in School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, UK. He obtained his PhD in 1990 in Mathematical Statistics at Institute of systems Science, Chinese Academy of Sciences, Beijing. He was working as an Associate Professor and Full Professor in the above Institute until 2004. He was a Chair Professor and Head of Statistics, University of York from 2008 to 2010, a Chair Professor in Statistics in University of Kent since 2010 and Head of Statistics since 2018. He is an expert in nonparametric statistics, statistical genetics, neuroimaging and high dimensional statistics. The representative work include those on nonparametric testing and Wilks phenomenon, model-based overlapped clustering and statistical theory for neuroimaging.