Information about past colloquia is available here.
|Wednesday, September 8||Yong Chen||University of Pennsylvania Biostatistics||Non-standard problems in statistical inference: Bartlett identity, boundary, identifiability issues
|Wednesday, September 22||Yize Zhao||Yale Biostatistics||Genetic underpinnings of brain structural connectome for young adults|
|Wednesday, September 29||Snigdhansu Chatterjee||UMN Statistics||Bayesian equation selection and statistical learning of stochastic dynamical systems|
|Wednesday, October 6||Sung Hoon Choi||UConn Economics|
|Wednesday, October 13||Lily Wang||George Mason|
|Wednesday, October 20||Mary Gray||American University||Pfizer Colloquium|
|Monday, October 25||Zhengqing Ouyang
||University of Massachusetts||UConn-UMass Joint Colloquium|
|Wednesday, October 27|
|Wednesday, November 3||Rong Chen||Rutgers University|
|Wednesday, November 10||Susan Murphy||Harvard University||Makuch Lecture
|Wednesday, November 17||Fernanda Schumacher||Campinas State University
|Wednesday, December 1||David Bergman||UConn Business School|
YONG CHEN, ASSOCIATE PROFESSOR, DEPARTMENT OF BIOSTATISTICS, EPIDEMIOLOGY AND INFORMATICS, PERELMAN SCHOOL OF MEDICINE, UNIVERSITY OF PENNSYLVANIA
WEDNESDAY, SEPTEMBER 8, 2021 4:00 P.M. EDT, 1-HOUR DURATION
NON-STANDARD PROBLEMS IN STATISTICAL INFERENCE: BARTLETT IDENTITY, BOUNDARY, IDENTIFIABILITY ISSUES
In this talk, I will cover a few ideas in tackling non-standard problems in statistical inference, including Bartlett identity, boundary and identifiability issues. I will show that these considerations are critical in model robustness, statistical power, and validity. I will also present implications of these ideas in addressing some challenges in biomedical research including analysis of DNA methylation data and drug/vaccine safety data.
Bio: Dr. Yong Chen is an Associate Professor of Biostatistics at the Department of Biostatistics, Epidemiology and Informatics (DBEI), the Perelman School of Medicine, University of Pennsylvania (Penn). He is the PI of the PennCIL lab, which is aiming to tackle key challenges in the modern data rich era, including heterogeneity, complexity, suboptimal quality, reproducibility, privacy, and high-dimensionality of biomedical data. He has published over 100 papers. His research has been continuously funded by NIH, PCORI and AHRQ.
Dr. Chen holds joint appointments at the Institute of Biomedical Informatics, the Center for Evidence-based Practice, and the Applied Mathematics & Computational Science Program at the University of Pennsylvania. He obtained his Ph.D. in Biostatistics from the Johns Hopkins University. He has received numerous award including the Elected Fellow of American Statistical Association in 2020.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=ed42733144e48f22f5ae7175a0fb20eba|
|Call-in option:||US Toll +1-415-655-0002 Access code: 2623 607 5687|
|Date and time:||Wednesday, September 8, 2021 4:00 P.M. EDT|
YIZE ZHAO, ASSISTANT PROFESSOR, DEPARTMENT OF BIOSTATISTICS, YALE SCHOOL OF PUBLIC HEATH, YALE UNIVERSITY
WEDNESDAY, SEPTEMBER 22, 2021 4:00 P.M. EDT, 1-HOUR DURATION
GENETIC UNDERPINNINGS OF BRAIN STRUCTURAL CONNECTOME FOR YOUNG ADULTS
With distinct advantages in power over behavioral phenotype, brain imaging traits have become emerging endophenotypes to dissect molecular contribution to behaviors and neuropsychiatric illness. Among different imaging features, brain structural connectivity (i.e. structural connectome) which summarizes whole brain anatomical neural connections is one of the most cutting edge while under-investigated traits; and the genetic influence on the shifts of structural connectivity remains highly elusive. Relying on a landmark imaging genetics study for young adults, we develop a biologically plausible brain network response shrinkage model to comprehensively characterize the relationship between high dimensional genetic variants and the structural connectcome phenotype. Under a unified Bayesian framework, we accommodate the topology of brain network and biological architecture within genome; and eventually establish a mechanistic mapping between genetic biomarkers and the associated brain sub-network units. An efficient expectation-maximization algorithm is developed to estimate the model parameters and ensure computing feasibility. We show the superiority of our method in extensive simulations. In the application to the Human Connectome Project. Young Adult (HCP-YA) data, we establish the genetic underpinnings which are highly interpretable under functional annotation and brain tissue eQTL analysis, for the brain white matter tract sub-networks concentrating on hippocampus and between hemispheres.
Bio: Dr. Zhao is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health and affiliated with Yale Center for Analytical Sciences. Her main research focuses on the development of statistical and machine learning methods to analyze large-scale complex data (imaging, -omics, EHRs), Bayesian methods, feature selection, predictive modeling, data integration, missing data and network analysis. She has strong interests in biomedical research areas including mental health, cancer and cardiovascular diseases, etc. Dr. Zhao received her Ph.D. in Biostatistics from Emory University and postdoc training in Statistical and Applied Mathematical Sciences Institute (SAMSI) and University of North Carolina at Chapel Hill. Prior to coming to Yale, she was an Assistant Professor in Biostatistics at Cornell University, Weill Cornell Medicine.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=me9a5358121a5be68da6ca57f5bcc7422|
|Call-in option:||US Toll +1-415-655-0002 Access code: 26235708141|
|Meeting number and password:||Meeting number (access code): 2623 570 8141
Meeting password: MWcnq9Bch36
|Date and time:||Wednesday, September 22, 2021 4:00 P.M. EDT|
SNIGDHANSU CHATTERJEE, DIRECTOR, INSTITUTE FOR RESEARCH IN STATISTICS AND ITS APPLICATIONS (IRSA), PROFESSOR, SCHOOL OF STATISTICS, UNIVERSITY OF MINNESOTA
WEDNESDAY, SEPTEMBER 29, 2021 4:00 P.M. EDT, 1-HOUR DURATION
BAYESIAN EQUATION SELECTION AND STATISTICAL LEARNING OF STOCHASTIC DYNAMICAL SYSTEMS
Natural processes adhere to laws of science, that are often describable as systems of (partial, stochastic) differential equations, or stochastic dynamical systems. We present a Bayesian framework for discovering such dynamical systems under assumptions that align with real-life scenarios, including the availability of relatively sparse data. We investigate different modeling and computational strategies that may be used for teasing out the important details about the dynamical system. These include direct Bayesian modeling of the observed data, functional data approaches and Bayesian deep learning approaches. The proposed framework can be used for evaluation and validation of scientific and computational models of complex natural phenomena, and in turn, for obtaining precise and accurate predictions in tasks related to precision medicine, climate modeling and other applications. We present theoretical and methodological details and examples from two use cases: on modeling cancer and on modeling climate.
Bio: Dr. Chatterjee is Professor of Statistics and the Director of the Institute for Research in Statistics and its Applications (IRSA) at the University of Minnesota. He is also a fellow of the Institute on the Environment and member of the Minnesota Population Center at the University of Minnesota. His research interests include statistical foundations of data science, geometry of high dimensional data, Bayesian statistics, applications of statistics, machine learning to several domains. Dr. Chatterjee graduated from the Indian Statistical Institute in 2000.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=maf06c7da88eddc5420c9ae92bfb72f93|
|Call-in option:||US Toll +1-415-655-0002 Access code: 26246092570|
|Meeting number and password:||Meeting number (access code): 2624 609 2570
Meeting password: iHpcU6Tcx27
|Date and time:||Wednesday, September 29, 2021 4:00 P.M. EDT|