ISI Regional Webinar #4 - The Global Voice of Statistics, Voices from the USA

Tolulope Adeyina is a passionate and dedicated Ph.D. candidate in Data Science at the University of Texas at El Paso, El Paso Texas. His research focuses on the innovative application of Graph Neural Networks in addressing intricate challenges within the biological domain. 

Tolu’s academic journey has been marked by a profound commitment to statistical sciences and its dynamic intersections with cutting-edge technologies. His areas of expertise span across Deep Learning, Natural Language Processing, Machine Learning, and Graph Theory. He has employed mathematical modelling to understand infectious disease dynamics, machine learning and advanced computational methods to identify biomarkers for drug discovery, and statistical and machine learning methods to enhance credit card fraud models. The last of these was conducted through a summer internship with Capital One.

Linda is Chief Mathematical Statistician and Director of Research and Development of USDA’s National Agricultural Statistics Service (NASS). She works with others within and outside of NASS to continually improve the methodology underpinning the Agency’s collection and dissemination of data on every facet of U.S. agriculture. 

After obtaining her Ph.D. in Statistics from Oklahoma State University, Linda served on the faculties of three land grant universities: Oklahoma State University, University of Nebraska, and the University of Florida. She has been active in statistics education from kindergarten through post doctorate training. A major component of her research work has been collaborative with researchers in the agricultural, ecological, environmental, and medical sciences. Her recent research has focused on the use of open source data, capture-recapture methodology, and integrated survey and non-survey data to produce survey estimates. Linda has authored or co-authored three books and more than 100 publications in over 50 different journals, constituting a mixture of statistics and subject-matter journals. 

Linda is an elected member of the International Statistical Institute (ISI), a fellow of the American Statistical Association (ASA) and a fellow of the American Association for the Advancement of Science (AAAS). She has been the editor of the Journal of Agricultural, Biological and Environmental Statistics, and served in a broad range of offices within the professional statistical societies, including the ISI, IBS, ASA and NISS. 

Abel is a Professor of Statistics at the University of Washington, and an affiliate member of the eScience Institute at the Center for Statistics in the Social Sciences. Currently, he also serves as the Chair of the Department of Statistics. 

Abel’s research interests include Bayesian statistics and machine learning, especially nonparametric methods, spatio-temporal models, network analysis and extreme value theory. He has employed this research to address a range of problems including studies of justices’ decisions in the US Supreme Court, spatial voting patterns, financial trading, cognitive social structures and record linkage, among many others. Abel is an Elected Member of the International Statistical Institute and a Fellow of the American Statistical Association. He also serves as Associate Editor for the Journal of the American Statistical Association, the Annals of Applied Statistics, Bayesian Analysis, and the International Statistical Review.

Andrew Gelman is a professor of statistics and political science at Columbia University. His research spans a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; the effects of incumbency and redistricting; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics.

Andrew has published a range of popular and influential books, including Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin), Teaching Statistics: A Bag of Tricks (with Deborah Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina), and Regression and Other Stories (with Jennifer Hill and Aki Vehtari). 

Andrew has received the Outstanding Statistical Application award three times from the American Statistical Association, the award for best article published in the American Political Science Review, the Mitchell and DeGroot prizes from the International Society of Bayesian Analysis, and the Council of Presidents of Statistical Societies award.