David Banks obtained an M.S. in Applied Mathematics from Virginia Tech in 1982, followed by a Ph.D. in Statistics in 1984. He won an NSF Postdoctoral Research Fellowship in the Mathematical Sciences, which he took at Berkeley. In 1986 he was a visiting assistant lecturer at the University of Cambridge, and then joined the Department of Statistics at Carnegie Mellon in 1987. In 1997 he went to the National Institute of Standards and Technology, then served as chief statistician of the U.S. Department of Transportation, and finally joined the U.S. Food and Drug Administration in 2002. In 2003, he returned to academics at Duke University.
David Banks was the coordinating editor of the Journal of the American Statistical Association. He co-founded the journal Statistics and Public Policy and served as its editor. He co-founded the American Statistical Association's Section on National Defense and Homeland Security, and has chaired that section, as well as the sections on Risk Analysis and on Statistical Learning and Data Mining. In 2003 he led a research program on Data Mining at the Statistical and Applied Mathematical Sciences Institute; in 2008, he led a research program at the Isaac Newton Institute on Theory and Methods for Complex, High-Dimensional Data; in 2012, he led another SAMSI research program, on Computational Advertising. He has published 74 refereed articles, edited eight books, and written four monographs.
David Banks is past-president of the Classification Society, and has twice served on the Board of Directors of the American Statistical Association. He is currently the president of the International Society for Business and Industrial Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. He recently won the American Statistical Association's Founders Award.
His research areas include models for dynamic networks, dynamic text networks, adversarial risk analysis (i.e., Bayesian behavioral game theory), human rights statistics, agent-based models, forensics, and certain topics in high-dimensional data analysis.
R. Dale Hall, FSA, CERA, CFA, MAAA, is managing director of Research for the Society of Actuaries (SOA), a position he has held since December 2013. In his role, Hall coordinates the SOA’s strategic research partnerships, oversees SOA experience studies, coordinates research across the SOA’s wide variety of actuarial practice areas, and directs the SOA’s data-driven in-house research initiatives. He is a frequent speaker at insurance and retirement industry meetings to highlight SOA research, including presentations to the actuarial task forces of the National Association of Insurance Commissioners and testimony to the House Ways and Means Subcommittee on Select Revenue Measures on pension plan mortality rates.
Prior to joining the SOA, Hall spent more than 20 years in the U.S. insurance industry, primarily as chief actuary for the Life/Health companies of COUNTRY Financial. While at COUNTRY, he was active in industry as a member of the American Council of Life Insurers' Actuarial Committee, and was an adjunct professor in the actuarial science program at Illinois State University.
Hall is a fellow of the SOA, a Chartered Enterprise Risk Analyst, a CFA charterholder, and a member of the American Academy of Actuaries.
Iliyan Iliev studies political behavior and attitudes, particularly in textual data, using advanced statistical methods. His work combines various methodologies such as Bayesian time series, natural language processing, and dynamic clustering. He has developed various algorithms that deal with pattern recognition, text processing, and data visualization. He is currently working on a novel algorithm for the analysis of Arabic in social media with the aim to quantify the attitudes of Arab political and religious leaders, and assess the political network structures between leaders and their followers. These dynamic political networks are then linked with various terror events. He is working on a number of projects researching various aspects of terrorism, social unrest, political networks, and others. He received his PhD in Political Science from the University of Texas at Dallas.
Edward Melnick is Professor of Statistics and former Chair of the Department of Statistics and Operations Research at New York University Stern School of Business. Professor Melnick's research focuses on the formulation of analytical models and the development of statistical methodology needed to analyze them. His research has been primarily in the study of order statistics and the analysis and modeling of time series data. The applications of this work have been in the area of risk -- the occurrence of low probability events that result in catastrophic consequences.
His publications appear in such journals as the Journal of Time Series Analyses, Management Science, Journal of Advertising Research, Journal of Applied Probability and IEEE Transactions on Information Theory, Contingency.
Richard L. Smith is Mark L. Reed III Distinguished Professor of Statistics and Professor of Biostatistics in the University of North Carolina, Chapel Hill. He is also Director of the Statistical and Applied Mathematical Sciences Institute, a Mathematical Sciences Institute supported by the National Science Foundation. He obtained his PhD from Cornell University and previously held academic positions at Imperial College (London), the University of Surrey (Guildford, England) and Cambridge University. His main research interest is environmental statistics and associated areas of methodological research such as spatial statistics, time series analysis and extreme value theory. He is particularly interested in statistical aspects of climate change research, and in air pollution including its health effects. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, an Elected Member of the International Statistical Institute, and has won the Guy Medal in Silver of the Royal Statistical Society, and the Distinguished Achievement Medal of the Section on Statistics and the Environment, American Statistical Association. In 2004 he was the J. Stuart Hunter Lecturer of The International Environmetrics Society (TIES). He is also a Chartered Statistician of the Royal Statistical Society.
Clarice Weinberg, Ph.D., is Acting Branch Chief and a principal investigator in the Biostatistics and Computational Biology Branch. She holds a secondary appointment in the NIEHS Epidemiology Branch, and adjunct professorships at the University of North Carolina at Chapel Hill in both Epidemiology and Biostatistics.
Epidemiology is one of the best tools for studying human health effects of environmental exposures. However, this tool is inherently imperfect and prone to imprecision and biases. Weinberg's research has focused on the development of improved methods for design and analysis that account for sources of bias, missing data, response heterogeneity and mismeasurement in epidemiologic studies. Methodologic research is most fruitful when it arises in the context of real applications to epidemiology. Her extensive collaborations with epidemiologists at NIEHS have inspired nearly all of this work.
She is also developing improved designs and methods of analysis to elucidate joint etiologic roles of multiple genetic variants and environmental susceptibility factors. Complex diseases, such as birth defects, heart diseases, neurodegenerative disease and cancer, are caused by time and the combined action of genetic susceptibility factors and exposures. Of particular interest is the interplay between genetic factors, both maternal and fetal, and maternal exposures in influencing fetal survival, embryologic development and postnatal long-term health. Methods being developed in this area will be applied to data from an international study of oral clefting, to a family-based study of young-onset breast cancer and to a study of osteosarcoma, a bone cancer that occurs in childhood.
John Wambaugh is a Physical Scientist with the National Center for Computational Toxicology (NCCT). John's areas of active research include high throughput methods for exposure, toxicokinetics, and toxicology. With Dr. Kristin Isaacs of EPA’s National Exposure Research Laboratory, John co-leads the EPA Rapid Exposure and Dosimetry (RED) project, which supports “exposure forecasting” or “ExpoCast” research. John is also a member of the ToxCast research team. John develops and evaluates predictive models using mathematics, machine learning, and applied statistics. He also collaborates on the design of new experiments (including non-targeted chemical analyses and in vitro methods) to refine models and reduce uncertainty. John is an Associate Editor at Environmental Health Perspectives and the Journal of Exposure Science and Environmental Epidemiology and he has co-authored more than fifty peer-reviewed papers. John received his Ph.D. in physics from Duke University in 2006. He trained as a post post-doctoral researcher with NCCT, where he studied toxicokinetics and statistical analysis of biological models with an emphasis on Bayesian methods and data fusion.