The conference program includes a diverse array of keynote speakers, all of whom are prominent in their respective fields. There are five Lubar Business school keynote speakers: Andreas Buja, Lisha Chen, Dawn Iacobucci, Andrey Rzhetsky, and Eshan Soofi. These speakers are generously sponsored by the Lubar School of Business. There are "presidential" keynotes by the current president of the classification society, Rebecca Nugent, and the president-elect, Doug Steinley. A list of keynote topics along with speaker bios is given below and is arranged in alphabetical order of speaker names.
Stress Functions for Nonlinear Dimension Reduction, Proximity Analysis, and Graph Drawing (abstract)
Andreas Buja and Lisha Chen
Andreas Buja, University of Pennsylvania
Andreas Buja is a statistician with interests in data visualization, multivariate data analytic methods, proximity analysis, machine learning, and more recently in statistical inference after EDA and model selection. His Ph.D. is from the Swiss Federal Institute of Technology in Zurich, Switzerland (1981). He held positions at the Children's Hospital in Zurich (postdoc), Stanford University and Stanford Linear Accelerator Center (visiting faculty, 1981-82), University of Washington (Asst. and Assoc. Prof., 1982-87), and, after a short stint at Salomon Brothers in 1987, spent many years at various telecom labs: Bellcore/Telcordia (now part of Ericsson), AT&T Bell Labs, and subsequently AT&T Labs. He joined the Statistics Department at Wharton in January 2002.
Lisha Chen, Yale University
Lisha Chen received her B.A. in Statistics from Beijing University in 2001, and her Ph.D in Statistics from University of Pennsylvania in 2006. She has been an Assistant Professor at Yale University since 2006. She spent a year at SAMSI as a research fellow from 2009 to 2010. Her current research interests include dimension reduction, data visualization, variable selection and imbalance learning.
Mediation Analyses and Business School Rankings Data (abstract)
Dawn Iacobucci, Vanderbilt University
Dawn Iacobucci is the E. Bronson Ingram Professor of Marketing at the Owen Graduate School of Management, Vanderbilt University (Sr. Associate Dean 2008-2010), previously professor at the Kellogg School of Management, Northwestern University (1987-2004), the University of Arizona (2001-2002), and Wharton, at the University of Pennsylvania (2004 to 2007). She received her M.S. in Statistics, and M.A. and Ph.D. in Quantitative Psychology from the University of Illinois at Urbana-Champaign. Her research focuses on social networks, customer satisfaction, and mediation methods. She has published in the Journal of Marketing, the Journal of Marketing Research, Harvard Business Review, Marketing Science, Psychometrika, Psychological Bulletin, and Social Networks, and was past editor of the Journal of Consumer Research and the Journal of Consumer Psychology.
Mixture Model Component Trees: A Tool for Merging Clusters (and much more!) (abstract)
Rebecca Nugent, Carnegie Mellon University
Rebecca Nugent is the current President of the Classification Society. She previously was on the CS Board of Directors and represents the CS in the International Federation of Classification Societies. She received the IFCS Chikio Hayashi Young Promising Researcher Award in 2009. She is currently faculty in the Department of Statistics at Carnegie Mellon University. Her PhD in Statistics is from the University of Washington (2006). She also received a Master's in Statistics from Stanford (2001) and a Bachelor's in Mathematics, Statistics, and Spanish from Rice University (1999). Her research interests include high-dimensional clustering and classification methods, record linkage and disambiguation, graphics and visualization, and cognitive diagnosis models.
Phylogenetics of Disease: Big Data Analysis (abstract)
Andrey Rzhetsky, University of Chicago
Andrey Rzhetsky is a Professor of Medicine and Human Genetics, at the University of Chicago. He is also a Pritzker Scholar, and a Senior Fellow of both the Computation Institute, and the Institute for Genomics and Systems Biology at the University of Chicago. His research is focused on computational analysis of complex human phenotypes in context of changes and perturbations of underlying molecular networks. The input data for these studies is supplied by large-scale mining of free text, computation over clinical records, and high-throughput systems biology experiments.
Sample Information and Importance of Predictors (abstract)
Ehsan Soofi, University of Wisconsin –
Ehsan S. Soofi is a University of Wisconsin-Milwaukee Distinguished Professor at the Lubar School of Business. He is an elected member of International Statistical Institute (ISI), and a Fellow of the American Statistical Association. Professor Soofi’s research interests are in information-theoretic and Bayesian approaches to distribution theory and statistics, and their applications in reliability, economics and management sciences. He has published widely in the leading journals of statistics, econometrics, applied probability and operations research, engineering, information systems, and marketing. He is the Guest Editor of Econometric Reviews, Special Issue on Bayesian Inference and Information: In Memory of Arnold Zellner, and an Associate Editor of Econometric Reviews. He served as an Associate Editor of JASA (1990-2005) and chaired the Leonard J. Savage Thesis Award Committee (1992-2002). He is the Chair-elect of the Industrial Statistics Section of the International Society for Bayesian Analysis (ISBA), served as the Secretary (1997-1999), Vice President (1999-2001) and the Chair of Publication Committee (2001-2005) of International Association for Statistical Computing, and on the ISI Publication Committee (2003-05). Dr. Soofi received his Ph.D. in Applied Statistics from the University of California, Riverside, MA in statistics from the University of California, Berkeley, and BA in mathematics from UCLA.
Combining Discrete and Continuous Latent Variable Modeling to Better Understand the Structure of Data (abstract)
Doug Steinley, University of Missouri
Dr. Steinley’s research focuses on multivariate statistical methodology, with a primary interest in cluster analysis and social network analysis. His research in cluster analysis focuses on both traditional cluster analytic procedure (e.g., k-means cluster analysis) and more modern techniques (e.g., mixture modeling). In that the formulation of the general partitioning problem can be thought of in a graph theoretic nature; Dr. Steinley’s research also involves combinatorics and social network analysis, with applications of all these techniques to alcohol use and abuse.