The National Research Council established the Committee on Applied and Theoretical Statistics (CATS) in 1978 to provide a locus of activity and concern for the statistical sciences, statistical education, use of statistics, and issues affecting the field. CATS occupies a pivotal position in the statistical community, providing expertise in methodology and policy formation.
Upcoming CATS Meetings and Events
February 26-27, 2015
Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results Workshop
NAS Building, Lecture Room
June 1-2, 2015
Meeting of the Committee on Applied and Theoretical Statistics
Keck Building, Room 106
October 12-13, 2015
Meeting of the Committee on Applied and Theoretical Statistics
Keck Building, Room 206
Recent CATS Activities
Dr. Gatsonis joined Brown University in 1995 and became the founding Director of the Center for Statistical Sciences. He is a leading authority on the design and analysis of clinical trials of diagnostic and screening modalities and has extensive involvement in methodologic research in medical technology assessment and in health services and outcomes research. He is Group Statistician of the American College of Radiology Imaging Network (ACRIN), an NCI-funded collaborative group conducting multi-center studies of diagnostic imaging and image-guided therapy for cancer. In his ACRIN work, Dr. Gatsonis is the chief statistician of the Digital Mammography Imaging Screening Trial (a national study comparing digital to film mammography) and is also the chief statistician for ACRIN’s arm of the National Lung Screening Trial (NLST). Dr Gatsonis was the lead statistician of the International Breast MRI Consortium and of the Radiologic Diagnostic Oncology Group (RDOG). He is the founding editor-in-chief of Health Services and Outcomes Research Methodology and serves as a deputy editor of Academic Radiology and a member of the editorial board of Clinical Trials. Dr Gatsonis is an elected Fellow of the American Statistical Association and of the Association for Health Services Research.
Dr. Agarwal is Director of Engineering at LinkedIn where he leads the Applied Relevance Science group, whose focus is to bridge the gap between science and products by deploying cutting-edge methods to power various recommendation systems at LinkedIn. His group works on computational advertising, content optimization, stream relevance, experimental design, and distributed computing methods for large-scale machine learning. Previously, he was a Principal Research Scientist at Yahoo! Research where he worked on content optimization for Yahoo! media properties and computational advertising for Yahoo! Premium display and RightMedia exchange. He developed methods that were deployed in production and was awarded the Yahoo! superstar award for this effort. Dr. Agarwal has published extensively in top-tier conferences. He was program co-chair for KDD 2012 and regularly serves on various program committees. He is currently an associate editor for two flagship journals in statistics, the Journal of the American Statistical Association and Annals of Applied Statistics. He also serves on the executive committee of SIGKDD.
Katherine Bennett Ensor
Dr. Ensor is Professor of Statistics in the George R. Brown School of Engineering and director of the Center for Computational Finance and Economic Systems (CoFES) at Rice University. She also serves as the faculty lead for the Professional Science Masters program in Environmental Analysis and Decision Making. She served as chair of the Department of Statistics from 1999 through 2013. Ensor develops statistical techniques to answer important questions in science, engineering and business with specific focus on the environment, energy and finance. She is an expert in multivariate time series, categorical data, spatial-temporal and general stochastic processes. She is an elected fellow of the American Statistical Association, the American Association for the Advancement of Science and has been recognized for her leadership, scholarship, service, and mentoring. She holds a BSE (1981) and MS (1982) in Mathematics from Arkansas State University and a PhD in Statistics from Texas A&M University (1986).
Montserrat (Montse) Fuentes
North Carolina State University
Dr. Fuentes is Head and Full professor of Statistics (with tenure) at North Carolina (NC) State. Dr. Fuentes received her B.S. in Mathematics and Music (piano performance) from the University of Valladolid (Spain), and her Ph.D. in Statistics from the University of Chicago (1999). Dr. Fuentes has authored over 60 scientific publications & served as principal investigator (or co-PI) on 20 research grants, with total funding of more than $15 million. Dr. Fuentes was named an American Statistical Association (ASA) fellow (2008) for outstanding contributions to research in spatial statistics, for excellence in the development and application of statistical methodology in atmospheric sciences, air pollution and oceanography; and for service to the profession. She is the editor of the Journal of Agricultural, Biological, and Environmental Statistics (JABES), of the International Biometrics Society. Dr. Fuentes is a member-elect of the International Statistical Institute, and has been a member of the Regional Advisory Board (RAB) for the Eastern North American Region (ENAR) of the International Biometric Society. Dr. Fuentes is a member of the Science Advisory Board (SAB) Integrated Human Exposure Committee of the U.S. Environmental Protection Agency, and the U.S. representative in the Board of Directors of the International Environmetrics Society. She was a member of the Biostatistical Methods and Research Design (BMRD) study section of the National Institutes for Health, and she is currently a member of the scientific review committee of Health Canada. She has also worked for the U.S. Department of Justice as an expert witness, and was a member of a committee of the National Research Council of the National Academies working on the impact of ozone on mortality. She is a senior leader of the ADVANCE-NSF Developing Diverse Departments program at NCSU.
Alfred O. Hero III
University of Michigan
Dr. Hero is the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan. He received the B.S. (summa cum laude) from Boston University (1980) and PhD from Princeton University (1984), both in Electrical Engineering. His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. In 2008 he was awarded the Digiteo Chaire d'Excellence, sponsored by Digiteo Research Park in Paris, located at the Ecole Superieure d'Electricite, Gif-sur-Yvette, France. He is an IEEE Fellow and several of his research articles have received best paper awards. Prof. Hero was awarded the University of Michigan Distinguished Faculty Achievement Award (2011). He received the IEEE Signal Processing Society Meritorious Service Award (1998) and the IEEE Third Millenium Medal (2000). He was President of the IEEE Signal Processing Society (2006-2008) and was on the Board of Directors of the IEEE (2009-2011) where he served as Director of Division IX (Signals and Applications). Dr. Hero’s recent research interests have been in detection, classification, pattern analysis, and adaptive sampling for spatio-temporal data. Of particular interest are applications to network security, multi-modal sensing and tracking, biomedical imaging, and genomic signal processing.
David M. Higdon
Los Alamos National Laboratory
Dr. Higdon is a member of the Statistical Sciences Group at the Los Alamos National Laboratory. He is an internationally recognized expert in Bayesian statistical modeling of environmental and physical systems. He has also led numerous programmatic efforts at LANL in the quantification of margins and uncertainties and uncertainty quantification. His recent research has focused on simulation-aided inference in which physical observations are combined with computer simulation models for prediction and inference. His research interests include space-time modeling; inverse problems in physics, hydrology, and tomography; inference based on the combining of deterministic and stochastic models; multiscale models; parallel processing in posterior exploration; statistical modeling in physical, environmental, and biological sciences; and Monte Carlo and simulation-based methods. He received a B.A. and M.A. in Mathematics from University of California, San Diego and a Ph.D. in Statistics from University of Washington.
Robert E. Kass
Carnegie Mellon University
Dr. Kass is a Professor in the Department of Statistics at Carnegie Mellon University. He received his Ph.D. in Statistics from the University of Chicago. Kass has served as Chair of the Section for Bayesian Statistical Science of the American Statistical Association, Chair of the Statistics Section of the American Association for the Advancement of Science, Executive Editor of the international review journal Statistical Science, and founding Editor-in-Chief of the journal Bayesian Analysis. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He has been recognized by the Institute for Scientific Information as one of the 10 most highly cited researchers, 1995-2005., in the category of mathematics. In 1991 he began the series of workshops Case Studies in Bayesian Statistics, which are held at Carnegie Mellon every odd year, and was co-editor of the six proceedings volumes that were published by Springer. He is coorganizer of the workshop series Statistical Analysis of Neuronal Data, which began in 2002 and is held at Carnegie Mellon on even years. Kass has been been on the faculty of the Department of Statistics at Carnegie Mellon since 1981 and served as Department Head from 1995 to 2004; he joined the Center for the Neural Basis of Cognition in 1997, and the Machine Learning Department in 2007.
The University of Chicago
Dr. Lafferty is the Louis Block Professor in the Departments of Statistics, Computer Science, and the College at The University of Chicago. His research area is machine learning, with a focus on computational and statistical aspects of nonparametric methods, high-dimensional data, graphical models, and applications. An associate editor of the Journal of Machine Learning Research, Dr. Lafferty served as progam co-chair and general co-chair of the Neural Information Processing Systems Foundation conferences in 2009 and 2010. Dr. Lafferty received his doctoral degree in mathematics from Princeton University, where he was a member of the Program in Applied and Computational Mathematics. Prior to joining the University of Chicago in 2011, he was Professor of Computer Science, Machine Learning, and Statistics at Carnegie Mellon University, where he is currently an Adjunct Professor.
Xihong Lin is a Professor of Biostatistics at Harvard University. She received a PhD from the University of Washington. Dr. Lin's major statistical research interests lie in developing statistical methods for high-dimensional and correlated data. She is particularly interested in developing statistical and computational methods for "omics" data in population-based studies, such as genetic epidemiology, genetic environmental sciences and clinical studies. She currently serves as the coordinating director of the Program of Quantitative Genomics of Harvard School of Public Health. Dr. Lin's specific areas of statistical research include statistical learning methods for high-dimensional data, dimension reduction, variable selection, nonparametric and semiparametric regression models, measurement error, mixed (frailty) models, estimating equations, and missing data. Dr. Lin's areas of applications include cancer, genetic epidemiology, gene and environment, genome-wide association studies, genomics in population science, biomarkers and proteomics.
José M. F. Moura (NAE)
Carnegie Mellon University
Dr. Moura is the Philip and Marsha Dowd University Professor at Carnegie Mellon University, with the Electrical and Computer Engineering and, by courtesy, the BioMedical Engineering. He is a member of the US National Academy of Engineers, a corresponding member of the Portugal Academy of Science, an IEEE Fellow, and a Fellow of the AAAS. He holds a D. Sc. in Electrical Engineering and Computer Science, M.Sc., and EE degrees all from MIT and an EE degree from Instituto Superior Técnico (IST, Portugal). He was a visiting Professor at MIT (2006-2007, 1999-2000, and 1984-86), a visiting scholar at USC (Summers of 79-81), and was on the faculty of IST (Portugal). In the academic year 2013-14, he will be a visiting Professor with New York University and CUSP, the Center for Urban Science & Progress, on sabbatical leave from CMU. Moura's research interests are in statistical signal and image processing. He is working in the new area of Big Data and network science, with particular emphasis on distributed decision and inference in networked systems and graph based data. Current research projects include signal processing on graphs and analytics for Big Data, distributed detection in sensor networks, robust detection and imaging by time reversal, bioimaging, SPIRAL, DSP on Graphs, SMART, and image/video processing. Besides industrial funding, his work has been sponsored by several DARPA, NIH, ONR, ARO, AFOSR, and NSF grants, and several industrial grants. Moura received the IEEE Signal Processing Society Society Award for outstanding technical contributions and leadership in signal processing, the IEEE Signal Processing Society Technical Achievement Award for fundamental contributions to statistical signal processing. He is on the Board of Directors of the IEEE and serves as IEEE Division IX Director (2012-13). He was the President of the IEEE Signal Processing Society (2008-2009). He was Editor in Chief of the IEEE Transactions on Signal Processing and acting Editor in Chief for the IEEE Signal Processing Letters. He was on the Editorial Board of several Journals, including the ACM Transactions on Sensor Networks and the IEEE Proceedings. He was in the steering committee of the IEEE International Symposium on Bioimaging (ISBI) and is on the steering committee of the ACM/IEEE International Symposium on Information Processing in Sensor Networks (IPSN).
Sharon-Lise T. Normand
Dr. Normand is Professor of Health Care Policy (Biostatistics) in the Department of Health Care Policy at Harvard Medical School and Professor in the Department of Biostatistics at the Harvard School of Public Health. Her research focuses on the development of statistical methods for health services and outcomes research, primarily using Bayesian approaches, including causal inference, provider profiling, item response theory, latent variables analyses, multiple informants analyses, and evaluation of medical devices in randomized and non-randomized settings. She serves on several task forces for the American Heart Association and the American College of Cardiology, was a consultant to the US Food and Drug Administration’s Circulatory System Devices Advisory Panel after serving a four-year term on the panel, is a member of the Medicare Evidence Development and Coverage Advisory Committee, and is Director of Mass-DAC, a data coordinating center that monitors the quality of all adult cardiac surgeries and coronary interventions in all Massachusetts’ acute care hospitals. Dr. Normand was the 2010 President of the Eastern North American Region of the International Biometrics Society and is Vice Chair of the Patient Centered Outcomes Research Institute’s Methodology Committee. Dr. Normand earned her Ph.D. in Biostatistics from the University of Toronto, holds a Masters of Science as well as a Bachelor of Science degree in Statistics, and completed a post-doctoral fellowship in Health Care Policy at Harvard Medical School. She is a Fellow of the American Statistical Association, a Fellow of the American College of Cardiology, a Fellow of the American Heart Association, and an Associate of the Society of Thoracic Surgeons. In 2011, Dr. Normand was awarded the American Statistical Association Health Policy Statistics Section’s Long Term Excellence Award.
Dr. Parmigiani is a Professor of Biostatistics in the Department of Biostatistics at the Harvard School of Public Health. He is also the Chair of the Department of Biostatistics and Computational Biology at the Dana Farber Cancer Institute and the Associate Director for Population Sciences of the Dana-Farber/Harvard Cancer Center. He received a B.S. in Economics and Social Sciences at Università L. Bocconi, and a M.S. and a Ph.D. in Statistics from Carnegie Mellon University. His research interests include models and software for predicting who is at risk of carrying genetic variants that confer susceptibility to cancer, specifically with respect to breast, ovarian, colorectal, pancreatic and skin cancer. His research covers statistical methods for the analysis of high throughput genomic data: analysis of cancer genome sequencing projects, integration of genomic information across technologies, and cross-study validation of genomics results. He is also interested in statistical methods for complex medical decisions, comprehensive models for lifetime history of chronic disease outcomes, decision trees, and dynamic programming. This includes Bayesian modeling and computation, multilevel models, decision theoretic approaches to inference, sequential experimental design, and Markov chain Monte Carlo methods.
Adrian E. Raftery (NAS)
University of Washington
Dr. Raftery is Professor of Statistics and Sociology at the University of Washington in Seattle. He was born in Dublin, Ireland, and obtained a B.A. in Mathematics (1976) and an M.Sc. in Statistics and Operations Research (1977) at Trinity College Dublin. He obtained a doctorate in mathematical statistics in 1980 from the Université Pierre et Marie Curie in Paris, France under the supervision of Paul Deheuvels. He was a lecturer in statistics at Trinity College Dublin from 1980 to 1986, and then an associate (1986-1990) and full (1990-present) professor of statistics and sociology at the University of Washington. He was the founding Director of the Center for Statistics and Social Sciences (1999-2009). Raftery has published over 170 refereed articles in statistical, sociological and other journals. His research focuses on Bayesian model selection and Bayesian model averaging, model-based clustering, inference for deterministic simulation models, and the development of new statistical methods for sociology, demography, and the environmental and health sciences. He is a member of the NAS, a Fellow of the American Academy of Arts and Sciences, an Honorary Member of the Royal Irish Academy, a member of the Washington State Academy of Sciences, a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and an elected Member of the Sociological Research Association. He has won the Population Association of America's Clifford C. Clogg Award, the American Sociological Association's Paul F. Lazarsfeld Award for Distinguished Contribution to Knowledge, the Jerome Sacks Award for Outstanding Cross-Disciplinary Research from the National Institute of Statistical Sciences, and the Parzen Prize for Statistical Innovation. He is also a former Coordinating and Applications Editor of the Journal of the American Statistical Association and a former Editor of Sociological Methodology. He was identified as the world's most cited researcher in mathematics for the decade 1995-2005 by Thomson-ISI. Twenty-six students have obtained Ph.D.'s working under Raftery's supervision, of whom 18 hold university faculty positions.
Dr. Waller is Rollins Professor and Chair of the Department of Biostatistics and Bioinformatics in the Rollins School of Public Health at Emory University. He received a Ph.D. in Operations Research from Cornell University in 1992. Dr. Waller's research involves the development of statistical methods to analyze spatial and spatio-temporal patterns. Past research involves the assessment of spatial clustering of disease, linking spatial statistics and geographic information systems, statistical assessments of environmental justice, and hierarchical Bayesian methods for modeling small-area health statistics. Recent areas of interest include spatial point process methods in alcohol epidemiology, conservation biology, and hierarchical models in disease ecology. Dr. Waller was the recipient of the 2004 Abdel El-Shaarawi Young Researcher’s Award. Dr. Waller has served on multiple National Academies committees including the National Research Council Committee on the Review of Existing and Potential Stando? Explosives Detection Techniques, the Institute of Medicine Committee on the Utility of Proximity-based Herbicide Exposure Assessments in Epidemiologic Studies in Vietnam Veterans, the National Academies Committee To Assess Potential Health E?ects from Exposures to PAVE PAWS Low-level Phased-array Radiofrequency Energy, and the National Academies Committee on Analysis Of Cancer Risks in Populations Near Nuclear Facilities: Phase 1.
Eugene Wong (NAE)
University of California, Berkeley
Dr. Wong is a Professor Emeritus in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. He received B.S., A.M., and Ph.D. degrees in Electrical Engineering from Princeton University. In 1980, he co-founded Relational Technology, Inc., later renamed the INGRES Corporation, which was a leading provider of database software products. While in Hong Kong from 1994-1996, he was instrumental in building an Internet backbone for Asia, first as CEO of SuperNet, Ltd., and then as founder of the Asia Internet Holding Company. From 1998-2005, he was variously a director, chief scientist, and CEO of Versata, Inc., a public software company serving the distributed enterprise applications market. He served as an Associate Director of OSTP and Associate Director of NSF for Engineering, which gives him a good perspective for evaluating the usefulness of this report in federal policy circles. He is a member of the National Academy of Engine