应beat365官方网站邀请，加拿大Alberta大学Michael Li教授2016年10月18日访问我校进行科研合作交流活动，在此期间将做以下学术报告。

        报告题目：Model-Based Data Analysis: Detecting and Resolving Nonidentifiability
        报告时间：2016年10月18日（星期二）上午10:00            
        报告地点：理科楼407

        Abstract: 
        When a mathematical model is confronted with data, many sensitive parameters can not be estimated directly from the data and need to be estimated indirectly through model fitting. One of the main challenges in model fitting is the nonidentifiability issue: infinitely many parameter values can produce the same quality fit. This may not sound like a serious issue since we can use any of these values. However, if the goal of modeling is to estimate
quantities that are not observable, then it is very often the case that two different parameter values with the same model fit with data can produce drastically different estimations on the unobservable quantities. It is essential to detect and resolve nonidentifiability  when performing model fitting.  
        After introducing different notions of nonidentifiability, I will review some existing methods for detecting and ranking nonidentifiable parameters. I will introduce a new method using singular value decomposition and variance decomposition, which has several advantages over exising methods. I will use model-based HIV estimation as an example to illustrate the issues and their solutions.

        报告人简介: Dr. Michael Y. Li received his PhD from the University of Alberta, Canada, in 1994. He had postdoctoral research experiences at the Universite de Montreal in Canada and Georgia Institute of Technology in the USA. He is currently a professor of applied mathematics at the University of Alberta. Dr. Li’s research interest and expertise are in analytical and numerical  investigations of mathematical models that describe the complex transmission dynamics of infectious diseases. His research has been funded by the NSF (US), NSERC, CFI, NCE-MITACS, MPrime, PIMS, and the Province of Alberta. One of Dr. Li’s most significant contributions to the mathematical modeling of infectious diseases was the development of a set of effective mathematical tools for proving global stability. His work in this area has now known as the Li-Muldowney theory. During the past 5 years, Dr. Li has devoted his research to the integration of mathematical modeling with public health research.  At the University of Alberta, he is leading several interdisciplinary modeling projects on ART resistance of HIV, estimation of undiagnosed HIV positive population, transmission of TB among indigenous populations, and cost effects analysis of vaccination programs for pneumococcal diseases.

        欢迎感兴趣的师生参加！