Keynote: Dr. Xihong Lin
We are excited to announce that Dr. Xihong Lin will present the MSSISS 2021 keynote address. Dr. Xihong Lin is a Professor and former Chair of the Department of Biostatistics, Coordinating Director of the Program in Quantitative Genomics at the Harvard T. H. Chan School of Public Health, and Professor of the Department of Statistics at the Faculty of Arts and Sciences of Harvard University, and Associate Member of the Broad Institute of Harvard and MIT.
Date: February 26th, 3:10 – 4:00PM
Zoom ID: 979 7179 8346
Title: Learning from COVID-19 Data on Transmission, Health Outcomes, Interventions and Vaccination
COVID-19 is an emerging respiratory infectious disease that has become a pandemic. In this talk, I will first provide a historical overview of the epidemic in Wuhan. I will then provide the analysis results of 32,000 lab-confirmed COVID-19 cases in Wuhan to estimate the transmission rates using Poisson Partial Differential Equation based transmission dynamic models. This model is also used to evaluate the effects of different public health interventions on controlling the COVID-19 outbreak, such as social distancing, isolation and quarantine. I will present the results on the epidemiological characteristics of the cases. The results show that multi-faceted intervention measures successfully controlled the outbreak in Wuhan. I will next present transmission regression models for estimating transmission rates in USA and other countries, as well as factors including intervention effects using social distancing, test-trace-isolate strategies that affect transmission rates. I will discuss estimation of the proportion of undetected cases, including asymptomatic, pre-symptomatic cases and mildly symptomatic cases, the chances of resurgence in different scenarios, and the factors that affect transmissions. I will also present the US county-level analysis to study the demographic, social-economic, and comorbidity factors that are associated with COVID-19 case and death rates. I will also present the analysis results of >500,000 participants of the HowWeFeel project on symptoms and health conditions in US, and discuss the factors associated with infection, behavior, and vaccine hesitancy. I will provide several takeaways and discuss priorities.
Dr. Xihong Lin is a highly accomplished Statistician and Scientist. Dr.Lin is Professor and former Chair of the Department of Biostatistics, Coordinating Director of the Program in Quantitative Genomics at the Harvard T. H. Chan School of Public Health, Professor of the Department of Statistics at the Faculty of Arts and Sciences of Harvard University, and Associate Member of the Broad Institute of Harvard and MIT. Dr. Lin holds many prestigious titles and awards. Dr.Lin is an elected member of the National Academy of Medicine. She received the 2002 Mortimer Spiegelman Award from the American Public Health Association, the 2006 Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award, and the 2017 COPSS FN David Award. Dr. Lin is also an elected fellow of the American Statistical Association (ASA), Institute of Mathematical Statistics, and International Statistical Institute. Dr. Lin’s research focuses on developing and applying statistical and computational methods to analyze massive data from the genome, exposome, and phenome. She is also interested in scalable statistical inference and learning for big genomic, epidemiological, and health data. Dr. Lin’s statistical methodological research has been supported by the MERIT Award (R37) (2007-2015) and the Outstanding Investigator Award (OIA) (R35) (2015-2022) from the National Cancer Institute (NCI).
Title: Predictive Analytics for IoT Enabled Systems
Internet of things (IoT) enabled systems have become increasingly available in practice. Examples include GM’s OnStar® tele-service system, the InSite® telemonitoring system from GE, smart home appliances, and various personalized remote patient monitoring systems. The unprecedented data availability in such connected systems has ushered in the present-day era of Industry 4.0, where smart data analytics drive smart decisions. In this talk, I focus on predictive analytics and discuss both opportunities and challenges that IoT presents in building a unified predictive framework. I then shed light on the state-of-the-art efforts to improve generalization performance of both kernel-based and deep learning predictive models.
Raed Al Kontar is an Assistant Professor in the Industrial & Operations Engineering department at the University of Michigan, Ann Arbor. He received his Ph.D. in Industrial and Systems Engineering in 2018 and M.S in Statistics in 2017 from the University of Wisconsin Madison. He also received his B.S in Civil and Environmental Engineering with a minor in Mathematics from the American University of Beirut (AUB) in 2014. Raed’s main research interest is data science using probabilistic models where he aims to understand the foundations of such models in extracting interpretable knowledge and generalizing to new data. Raed also focuses on data science applications within Internet of Things (IoT) enabled systems, specifically in tele-service settings. Raed has recently received best paper awards at INFORMS quality statistics & reliability section (2018, 2019) and data mining section (2020), and the Quality Control and Reliability Engineering section (2019) from IISE.