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
May 1, 2017
Roundtable on Data Science Post-Secondary Education: Meeting #3
June 5-6, 2017
Meeting of the Committee on Applied and Theoretical Statistics
October 20, 2017
Roundtable on Data Science Post-Secondary Education: Meeting #4
Chicago, IL (tentative)
December 8, 2017
Roundtable on Data Science Post-Secondary Education: Meeting #5 Washington, DC
Recent CATS Activities
Sites We Like
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Constantine Gatsonis, Chair
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.
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Alfred O. Hero III, Vice Chair
University of Michigan
Alfred O. Hero III is co-Director of the Michigan Institute for Data Science (MIDAS) at the University of Michigan, where he is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering. He also has faculty appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics and is affiliated with graduate programs in Bioinformatics, Applied and Interdisciplinary Mathematics (AIM), and Applied Physics. Alfred Hero received the BS (summa cum laude) from Boston University (1980) and PhD from Princeton University (1984), both in electrical engineering. From 2008-2012 he held 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 Institute of Electrical and Electronics Engineers (IEEE) Fellow and several of his research articles have received best paper awards. Professor Hero received the University of Michigan Distinguished Faculty Achievement Award (2011). He was awarded the IEEE Third Millenium Medal (2000). In 2015 received the Society Award, which is the highest distinction awarded by the IEEE Signal Processing Society. He has also received the Technical Achievement Award (2013), and the Meritorious Service Award (1998), from the IEEE Signal Processing Society. 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). Professor Hero served on the IEEE TAB Nominations and Appointments Committee (2012-2014) and is currently a member of the Big Data Special Interest Group (SIG) of the IEEE Signal Processing Society. Since 2011 he has been a member of the Committee on Applied and Theoretical Statistics (CATS) of the US National Academies of Science and is currently the vice chair of this committee. 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, multimodal sensing and tracking, biomedical imaging, and genomic signal processing.
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Michael J. Daniels
University of Texas, Austin
Dr. Daniels is a full professor with a joint appointment between the Department of Integrative Biology and the Department of Statistics and Data Sciences at the University of Texas at Austin; he is also the chair of the Department of Statistics and Data Sciences. He served as the chair of the Department of Statistics at the University of Florida for four years before arriving in Austin. His research focuses on Bayesian methodology for missing data and causal inference, missing data in longitudinal studies, statistical methodology for HIV testing, discovery and evaluation of biomarkers for Duchenne muscular dystrophy, weight management clinical trials in rural settings, and the impact of new Medicare rules on preventable complications in hospitals. He has served as an associate editor of two premier biostatistical journals, Biometrics and Biostatistics, and is currently the co-editor of Biometrics. He was elected a Fellow of the American Statistical Association in 2007 and received the Lagakos Distinguished Alumni Award (from Harvard Biostatistics) in Fall 2014. He also has served in major professional organizations including as treasurer of Eastern North American Region (ENAR) of the International Biometrics Society and the International Society for Bayesian Analysis (ISBA) and chair of the Biometrics section of the American Statistical Association, (ASA) among many other leadership positions. He received his Sc.D. in biostatistics from Harvard University in 1995.
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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).
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University of North Carolina, Chapel Hill
Dr. Herring is Carol Remmer Angle Distinguished Professor of Children’s Environmental Health and Associate Chair of Biostatistics in the Gillings School of Global Public Health at The University of North Carolina (UNC) at Chapel Hill. In addition, Dr. Herring is an elected Faculty Fellow at UNC’s Carolina Population Center, where she conducts research using new statistical methods and innovative applications of statistics in public health and medicine. She is an elected fellow of the American Statistical Association (ASA), chair-elect of the ASA Biometrics Section, and is a past-president of ENAR, the largest professional organization of biostatisticians in North America. Dr. Herring has over 200 peer-reviewed publications related to statistical methodology, public health, and medicine and is currently the Principal Investigator of a 5-year NIH-funded project exploring Bayesian methods for high-dimensional epidemiologic data. Her long-standing research interests include environmental health science, reproductive epidemiology, maternal and child health, neonatology, nutrition and obesity. Dr. Herring earned her Sc.D. in biostatistics at Harvard University.
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David M. Higdon
Virginia Tech University
Dr. Higdon is a professor in the Social Decision Analytics Laboratory at Virginia Tech University. Previously, he spent 10 years as a scientist or group leader of the Statistical Sciences Group at Los Alamos National Laboratory. He is an expert in Bayesian statistical modeling of environmental and physical systems, combining physical observations with computer simulation models for prediction and inference. His research interests include space-time modeling; inverse problems in hydrology and imaging; statistical modeling in ecology, environmental science, and biology; multiscale models; parallel processing in posterior exploration; statistical computing; and Monte Carlo and simulation based methods. Dr. Higdon has served on several advisory groups concerned with statistical modeling and uncertainty quantification and co-chaired the NRC Committee on Mathematical Foundations of Validation, Verification, and Uncertainty Quantification. He is a fellow of the American Statistical Association. Dr. Higdon holds a B.A. and M.A. in mathematics from the University of California, San Diego, and a Ph.D. in statistics from the University of Washington.
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Nicholas Horton is a Professor of Statistics at Amherst College. He has taught a variety of courses in statistics and related fields, including probability, mathematical statistics, regression and design of experiments and is passionate about improving quantitative literacy for students with a variety of backgrounds as well as engagement and mastery of higher-level concepts and capacities to undertake research. He is the Chair of the Committee of Presidents of Statistical Societies and has published more than 150 papers in statistics and biomedical research and four books on statistical computing and data science. He has been the recipient of a number of teaching awards. As an applied biostatistician, Dr. Horton’s work is based squarely within the mathematical sciences, but spans other fields in order to ensure that research is conducted on a sound footing. The real-world research problems that these investigators face often require the use of novel solutions and approaches, since existing methodology is sometimes inadequate. Bridging the gap between theory and practice in interdisciplinary settings is often a challenge, and has been a particular focus of Dr. Horton’s work. Dr. Horton earned his A.B. from Harvard College and his Sc.D. in biostatistics from the Harvard School of Public Health.
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Robert E. Kass
Carnegie Mellon University
Dr. Kass is a Professor in the Department of Statistics, the Machine Learning Department, and the Center for the Neural Basis of Cognition 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. His current research is at the interface of statistics and neuroscience. He is a co-founder and organizer of the workshop series Statistical Analysis of Neuronal Data, which began in 2002 and is held every two years in Pittsburgh. 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.
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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).
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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.
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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.
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Cynthia Rudin is an associate professor of computer science and electrical and computer engineering at Duke University, with secondary appointments in the statistics and mathematics departments. She directs the Prediction Analysis Lab. Her interests are in machine learning, data mining, applied statistics, and knowledge discovery (Big Data). Her application areas are in energy grid reliability, healthcare, and computational criminology. Previously, Prof. Rudin held positions at the Massachusetts Institute of Technology (MIT), Columbia, and New York University. She holds an undergraduate degree from the University at Buffalo where she received the College of Arts and Sciences Outstanding Senior Award in Sciences and Mathematics, and three separate outstanding senior awards from the departments of physics, music, and mathematics. She received a PhD in applied and computational mathematics from Princeton University. She is the recipient of the 2013 and 2016 INFORMS Innovative Applications in Analytics Awards, an National Science Foundation (NSF) CAREER award, was named as one of the “Top 40 Under 40” by Poets and Quants in 2015, was named by Businessinsider.com as one of the 12 most impressive professors at MIT in 2015, and won an Adobe Digital Marketing Research Award in 2016. Her work has been featured in Businessweek, The Wall Street Journal, the New York Times, the Boston Globe, the Times of London, Fox News (“Fox & Friends”), the Toronto Star, WIRED Science, U.S. News and World Report, Slashdot, CIO magazine, Boston Public Radio, and on the cover of IEEE Computer. She serves on committees for the Defense Advanced Research Projects Agency, the American Statistical Association, INFORMS, the National Institute of Justice, and the National Academy of Science. She is presently the chair of the INFORMS Data Mining Section, and will be chair-elect of the Statistical Learning and Data Science section of the American Statistical Association.
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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.
Michelle K. Schwalbe, Ph.D.
PH: (202) 334-1682
PH: (202) 334-1378
Rodney N. Howard