时 间：2021年9月30日（周四）8：30-12：00
地 点：线上，腾讯会议ID：599 656 042
题 目：Our Recent Development on Cost Constraint Machine Learning Models
报告人：Haoda Fu professor ,University of North Carolina at Chapel Hill
主持人：方方 教授
摘 要：
Suppose we can only pay $100 to diagnose a disease subtype for selecting best treatments. We can either measure 10 cheap biomarkers or 2 expensive ones. How can we pick the optimal combinations to achieve highest diagnostic accuracy?
This is a nontrivial problem. For a special case, as each variable costs the same, the total cost constraint will be reduced to an L0 penalty which is the best subset selection problem. Until recently, there is no good solution even for this special case. Traditional algorithms can only solve up to ~35 variables for best subset selections. Thanks to the algorithms breakthrough in the field of optimization research. We have modified and extended a recently developed algorithm to handle our cost constraint problems with thousands of variables.
In this talk, we will talk about the background of this problem, methods development, theoretical results. We will also show you an impressive example on dynamic programming. It will tell a story on how algorithms can make a difference on computing. I hope that through this talk, you can feel the modern statistics which combined computer science, statistics, and algorithms.
报告人简介：
Dr. Haoda Fu is a Research Fellow and an Enterprise Lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association). He is also an adjunct professor of biostatistics department, Univ. of North Carolina Chapel Hill and Indiana university school of medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics methodology research. He has more than 90 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrika, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session.