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||Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia
|Wednesday, October 13||Lily Wang||George Mason University
||Big spatial data learning: a parallel solution|
|Wednesday, October 20||Mary Gray||American University||Pfizer Colloquium|
|Monday, October 25||Zhengqing Ouyang
||University of Massachusetts||Joint UCONN/UMASS STATISTICS COLLOQUIUM|
|Wednesday, October 27||Alex Shkolnik||University of California, Santa Barbara||James Stein estimation for Principle Component Analysis|
|Wednesday, November 3||Rong Chen||Rutgers University||Analysis of Matrix and Tensor Time Series|
|Wednesday, November 10||Susan Murphy||Harvard University||Robert W. Makuch Distinguished Lecture in Biostatistics|
|Wednesday, November 17||Fernanda Schumacher||Campinas State University
||Robust Mixed-Effects Models for Longitudinal Data|
|Wednesday, December 1||David Bergman||UConn Business School||On Integration of Analytics Techniques for Algorithmic Sports Betting|
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|
WEDNESDAY, October 6, 2021 4:00 P.M. EDT, 1-HOUR DURATION
FEASIBLE WEIGHTED PROJECTED PRINCIPAL COMPONENT ANALYSIS FOR FACTOR MODELS WITH AN APPLICATION TO BOND RISK PREMIA
I develop a feasible weighted projected principal component (FPPC) analysis for factor models in which observable characteristics partially explain the latent factors. This novel method provides more efficient and accurate estimators than existing methods. To increase estimation efficiency, I take into account both cross-sectional dependence and heteroskedasticity by using a consistent estimator of the inverse error covariance matrix as the weight matrix. To improve accuracy, I employ a projection approach using characteristics because it removes noise components in high-dimensional factor analysis. By using the FPPC method, estimators of the factors and loadings have faster rates of convergence than those of the conventional factor analysis. Moreover, I propose an FPPC-based diffusion index forecasting model. The limiting distribution of the parameter estimates and the rate of convergence for forecast errors are obtained. Using U.S. bond market and macroeconomic data, I demonstrate that the proposed model outperforms models based on conventional principal component estimators. I also show that the proposed model performs well among a large group of machine learning techniques in forecasting excess bond returns.
Bio: Dr. Choi’s research focuses on the development of new tools for use with big data, machine learning, and forecasting. The tools primarily involve the development of new theoretical methods for use in both estimation and statistical inference using high-dimensional panel datasets. His research interests include econometric theory, financial econometrics, machine learning, and forecasting with a concentration in high-dimensional data, large panel data and factor models.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mecaac42b5d392af8ad37adb59cb3682c|
|Call-in option:||US Toll +1-415-655-0002 Access code: 2620 886 9037|
|Meeting number and password:||Meeting number (access code): 2620 886 9037
Meeting password: HUuFrM922hy
|Date and time:||Wednesday, October 6, 2021 4:00 P.M. EDT|
WEDNESDAY, October 13, 2021 4:00 P.M. EDT, 1-HOUR DURATION
BIG SPATIAL DATA LEARNING: A PARALLEL SOLUTION
Nowadays, we are living in the era of “Big Data.” A significant portion of big data is big spatial data captured through advanced technologies or large-scale simulations. Explosive growth in spatial and spatiotemporal data emphasizes the need for developing new and computationally efficient methods and credible theoretical support tailored for analyzing such large-scale data. Parallel statistical computing has proved to be a handy tool when dealing with big data. In general, it uses multiple processing elements simultaneously to solve a problem. However, it is hard to execute the conventional spatial regressions in parallel. This talk will introduce a novel parallel smoothing technique for generalized partially linear spatially varying coefficient models, which can be used under different hardware parallelism levels. Moreover, conflated with concurrent computing, the proposed method can be easily extended to the distributed system. Regarding the theoretical support of estimators from the proposed parallel algorithm, we first establish the asymptotical normality of linear estimators. Secondly, we show that the spline estimators reach the same convergence rate as the global spline estimators. The proposed method is evaluated through extensive simulation studies and an analysis of the US loan application data.
Bio:Lily Wang is a professor of Statistics at George Mason University. She received her PhD in Statistics from Michigan State University in 2007. Prior to joining Mason in 2021, she was on the faculty of Iowa State University (2014-2021) and the University of Georgia (2007-2014).
Wang is highly regarded internationally for her work on non/semi-parametric regression methods. She has broad interests across statistical learning of data objects with complex features, methodologies for functional data, spatiotemporal data, imaging data, and survey sampling. Working at the interface of statistics, mathematics, and computer science, she is also interested in developing cutting-edge statistical methods for solving issues related to data science and big data analytics. The methods she developed have a wide application in economics, engineering, neuroimaging, epidemiology, environmental studies, and biomedical science.
She is a fellow of both the Institute of Mathematical Statistics (2020) and the American Statistical Association (2021) and an Elected Member of the International Statistical Institute (2008). She is the recipient of multiple NSF awards, SEC Research Fellowship (2019-2020) and ASA/NSF/BLS Senior Research Fellowship (2020-2011), Mid-Career Achievement in Research Award (2021), COVID-19 Exceptional Effort Research Impact Award (2021) from Iowa State University and the M. G. Michael Research Award (2012) from the University of Georgia.
Wang serves on the editorial boards of Journal of the Royal Statistical Society, Series B, Journal of Nonparametric Statistics, and Statistical Analysis and Data Mining.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m1bba1abb98d34a7402a37d27b8512037|
|Call-in option:||US Toll +1-415-655-0002 Access code: 2622 735 1106|
|Meeting number and password:||Meeting number (access code): 2622 735 1106
Meeting password: maF2iJC8qf8
|Date and time:||Wednesday, October 13, 2021 4:00 P.M. EDT|
ZHENGQING OUYANG, ASSOCIATE PROFESSOR, DEPARTMENT OF BIOSTATISTICS & EPIDEMIOLOGY, SCHOOL OF PUBLIC HEALTH AND HEALTH SCIENCES, UNIVERSITY OF MASSACHUSETTS, AMHERST
Monday, October 25, 2021 1:00 P.M. EDT, 1-HOUR DURATION
STATISTICAL LEARNING FOR 3D GENOME ORGANIZATION
Genome organization in three-dimension (3D) is emerging as key to understanding crucial biological functions in health and disease. While high-throughput 3D genome technologies, such as Hi-C, have generated large-scale datasets, new approaches are needed to dissect the 3D chromatin structure in more depth. I will introduce our recent advancement on measuring high-resolution chromatin structure and elucidating distinct types of chromatin domains. Few statistical methods have been proposed for effectively estimating the 3D structure of the genome. I will introduce new statistical approaches to estimating 3D chromatin structures from high-throughput experimental data. Through analysis of diverse cell types and organisms, we demonstrate accurate and robust reconstruction of 3D chromatin structure at high-resolution and genome-wide scale. If time permits, I will mention ongoing work on detecting chromosomal abnormalities using 3D genomics data.
|Date and time:||Monday, October 25, 2021 1:00 P.M. EDT|
ALEX SHKOLNIK, ASSISTANT PROFESSOR, DEPARTMENT OF STATISTICS AND APPLIED PROBABILITY, UNIVERSITY OF CALIFORNIA, SANTA BARBARA
WEDNESDAY, October 27, 2021 4:00 P.M. EDT, 1-HOUR DURATION
JAMES STEIN ESTIMATION FOR PRINCIPLE COMPONENT ANALYSIS
The Stein paradox has played an influential role in the field of high dimensional statistics. This result warns that the sample mean, classically regarded as the “usual estimator”, may be suboptimal in high dimensions. The development of the James-Stein estimator, that addresses this paradox, has by now inspired a large literature on the theme of “shrinkage” in statistics. In this direction, we develop a James-Stein type estimator for the first principal component of a high dimension and low sample size data set. This estimator shrinks the usual estimator, an eigenvector of a sample covariance matrix under a spiked covariance model, and yields superior asymptotic guarantees. Our derivation draws a close connection to the original James-Stein formula so that the motivation and recipe for shrinkage is intuited in a natural way. Time permitting, we will explore the performance of the estimator on numerical examples and discuss several extensions including arbitrary shrinkage targets, weighted procedures and connections to quadratic programming.
Bio: Dr. Alex Shkolnik is an assistant professor in the Department of Statistics and Applied Probability at UC Santa Barbara. He obtained his Ph.D. in Computational Mathematics & Engineering from Stanford University in 2015. His thesis work centered on computational methods for models used in the quantification and management of credit risk. Dr. Shkolnik’s expertise lies in transform and Monte Carlo methods for the estimation and prediction of these risks. In particular, his ongoing focus is on the development of importance sampling techniques for complex systems encountered in finance and other research areas.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=md2b7fc11fb37e6d33068fd9f75403190|
|Call-in option:||US Toll +1-415-655-0002 Access code: 2625 361 3191|
|Meeting number and password:||Meeting number (access code): 2625 361 3191
Meeting password: 3FYppMV3NJ4
|Date and time:||Wednesday, October 27, 2021 4:00 P.M. EDT|
Wednesday, November 3, 2021 4:00 P.M. EDT, 1-HOUR DURATION
ANALYSIS OF MATRIX AND TENSOR TIME SERIES
Time series observations in matrix and tensor (multi-dimensional array) forms have been encountered more and more often in applications. In this talk we present an overview of some new developments in analyzing matrix/tensor time series and dynamic networks, with applications ranging from economics, finance, international trade, and others. Specifically, we will discuss Matrix/Tensor Autoregressive Models and Matrix/Tensor Factor Models in Tucker and CP forms. Focus will be on motivations, model formulations, interpretations and examples, with only brief sketches on the estimation procedures and their theoretical properties.
Bio: Dr. Rong Chen is a Distinguished Professor and Chair of the Department of Statistics at Rutgers University. He is an elected fellow of the Institute of Mathematical Statistics and American Statistical Association. The research interests of Dr. Chen include nonlinear and multivariate time series analysis Monte Carlo methods, statistical computing and Bayesian analysis statistical applications in science, engineering and business.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m4fa02284f4bbe9e1130c5921bbe12129|
|Call-in option:||US Toll +1-415-655-0002 Access code: 2623 844 6872|
|Meeting number and password:||Meeting number (access code): 2623 844 6872
Meeting password: JDpFaPqc887
|Date and time:||Wednesday, November 3, 2021 4:00 P.M. EDT|
SUSAN A. MURPHY, MALLINCKRODT PROFESSOR OF STATISTICS AND COMPUTER SCIENCE, RADCLIFFE ALUMNAE PROFESSOR AT THE RADCLIFFE INSTITUTE, HARVARD UNIVERSITY
Wednesday, November 10, 2021 4:00 P.M. EST, 1 hour 30 minutes
INFERENCE USING ADAPTIVELY COLLECTED DATA
Bandit algorithms are increasingly used in real-world sequential decision-making problems. Associated with this is an increased desire to be able to use the resulting datasets to answer scientific questions like: Did one type of ad lead to more purchases? In which contexts is a mobile health intervention effective? However, there is a lack of general methods for conducting statistical inference using more complex models on data collected with (contextual) bandit algorithms; for example, current methods cannot be used for valid inference on parameters in a logistic regression model for a binary reward. We discuss our work on theory justifying the use of M-estimators—which includes estimators based on empirical risk minimization as well as maximum likelihood—on data collected with adaptive algorithms, including(contextual) bandit algorithms. Specifically, we show that M-estimators, modified with particular adaptive weights, can be used to construct asymptotically valid confidence regions for a variety of inferential targets.
Bio: Dr. Susan A. Murphy is a Radcliffe Alumnae Professor at Harvard Radcliffe Institute and a professor of statistics and computer science at the Harvard John A. Paulson School of Engineering and Applied Sciences. A 2013 recipient of a MacArthur Fellowship, she was previously the H. E. Robbins Distinguished University Professor of Statistics, a research professor at the Institute for Social Research, and a professor of psychiatry, all at the University of Michigan.
Murphy earned her BS from Louisiana State University and her PhD from the University of North Carolina at Chapel Hill. Her research focuses on analytic methods to design and evaluate medical treatments that adapt to individuals, including some that use mobile devices to deliver tailored interventions for drug addicts, smokers, and heart disease patients, among others. She is a member of the National Academy of Medicine and of the National Academy of Sciences.
Susan A. Murphy awarded Van Wijngaarden Award (Harvard Gazette, 9/24/21)
About Dr. Makuch: Robert Makuch is a Professor in the Department of Biostatistics at the Yale School of Public Health and Director of the Regulatory Affairs Track. A graduate of the University of Connecticut (BA), University of Washington (MA – mathematics), and Yale University (MPhil, PhD), Professor Makuch worked at the National Cancer Institute (NCI) and the World Health Organization’s International Agency for Research on Cancer early in his career. He also worked for six months at the National Cancer Research Center in Tokyo, Japan.
He also was heavily involved in HIV research from the mid 80’s through the early-mid 90’s. He participated on the data monitoring committee for the original AZT vs. placebo randomized clinical trial in AIDS patients, and served on numerous committees for the NCI and the National Institute of Allergy and Infectious Diseases. He also worked closely with the Food and Drug Administration (FDA), developing and implementing more than 200 HIV studies. He also served as a Special Government Employee (SGE) to the FDA. He returned to Yale in 1986, and has worked extensively on methodologic issues in clinical trials and large population-based studies since. Another area of current interest involves detection of rare adverse drug events, especially in the post-marketing environment.
These areas of methodologic research evolved as a result of his continued interest (since the mid 1980s) in regulatory affairs science. In addition, Makuch developed a regulatory affairs track at YSPH for graduate and post-doctoral level students, and over the past 10 years has been the leader of more than 25 training programs for senior delegations of the Chinese Food and Drug Administration. His areas of medical application include cancer, HIV, arthritis, and cardiovascular disease.
In 2003, Makuch received the American Statistical Association Fellow Award for his numerous contributions to the field. In 2008, Makuch was received a Distinguished Alumni Award from the University of Connecticut. In 2012, Makuch was nominated to serve on the University of Connecticut Dean’s Advisory Board for the College of Liberal Arts and Sciences. He also has been a decades-long member of Phi Beta Kappa. He also developed a 5-year biostatistics training program in Japan, in collaboration with the Japanese government. His primary research interests continue to be methodologic issues in the design, conduct, analysis, and interpretation of clinical and large-population/epidemiologic studies. Design and sample size considerations for Phase IV studies is another active research area, in which a new class of hybrid designs has been proposed for scientific and regulatory purposes to detect rare adverse events.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/onstage/g.php?MTID=ebe298795cdd55f8166f5064a4acc3893|
|Date and time:||Wednesday, November 10, 2021 4:00 p.m. EST|
|Duration:||1 hour 30 minutes|
Wednesday, November 17, 2021 4:00 P.M. ET, 1-HOUR DURATION
ROBUST MIXED-EFFECTS MODELS FOR LONGITUDINAL DATA
In clinical trials, studies often present longitudinal or clustered data and are frequently affected by missing data. These studies are commonly analyzed using linear mixed models (LMMs), and for mathematical convenience, it is usually assumed that both random effect and error term follow normal distributions. However, these restrictive assumptions may result in a lack of robustness against departures from the normal distribution and invalid statistical inferences. In this talk, a flexible extension of LMMs considering the scale mixture of skew-normal class of distributions will be presented, accommodating skewness and heavy-tails and accounting for a possible within-subject serial dependence. The model estimation and evaluation using the R package skewlmm will be illustrated, and two applications to longitudinal data sets, regarding schizophrenia and mouse diet clinical trials, will be discussed. Additionally, some recent and future extensions will be introduced.
Bio: Fernanda Schumacher recently completed a Ph.D. in Statistics at the University of Campinas, Brazil, where she also obtained a master’s degree in Statistics in 2016. Her research interests include robust models, models for censored data, longitudinal data, EM algorithm, and scale mixture of skew-normal distributions.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mddd2477a11e3f1d89135aae258539c86|
|Call-in option:||US Toll +1-415-655-0002 Access code: 2622 741 0574|
|Meeting number and password:||Meeting number (access code): 2622 741 0574
Meeting password: H6rirgbJd35
|Date and time:||Wednesday, November 17, 2021 4:00 P.M. ET|
DAVID BERGMAN, ASSOCIATE PROFESSOR OF OPERATIONS AND INFORMATION MANAGEMENT, UNIVERSITY OF CONNECTICUT, SCHOOL OF BUSINESS
Wednesday, December 1, 2021 4:00 P.M. EST, 1-HOUR DURATION
ON INTEGRATION OF ANALYTICS TECHNIQUES FOR ALGORITHMIC SPORTS BETTING
The integration of machine learning and optimization opens the door to new modeling paradigms that have already proven successful across a broad range of industries. Sports betting is a particularly exciting application area, where recent advances in both analytics and optimization can provide a lucrative edge. In this talk we will discuss three algorithmic sports betting games where the combined use of machine learning and optimization have netted me significant winnings.
Bio: David Bergman is an Associate Professor of Operations and Information Management at the University of Connecticut. David’s work focuses on developing novel algorithms for large-scale automated decision making, both in research and in practice. His research is published in the top journals and his work in consulting has driven impact for organizations across a wide spectrum of industries. He is also a leader in sports analytics, and is the 2020 Draft Kings Fantasy Football World Champion.
|Event address for attendees:||https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mc1e9683c61c564b2e59f6cb04484a246|
|Call-in option:||US Toll +1-415-655-0002 Access code: 2620 932 7315|
|Meeting number and password:||Meeting number (access code): 2620 932 7315
Meeting password: wgFbbBNS656
|Date and time:||Wednesday, December 1, 2021 4:00 P.M. EST|