CATS, established in 1978, promotes the statistical sciences, statistical education, statistics applications, and related issues affecting the statistics community. The mission and scope of CATS evolved over time as interdisciplinary collaboration increasingly shaped the character of scientific research. After a brief hiatus, CATS was reconstituted in 2011 and has since focused on improving the visibility and practice of statistics within government agencies not well connected to statistics, increasing attention to statistical issues of big data and data science, and helping agencies identify bottlenecks impairing their analysis capabilities. Its multidisciplinary members are experts from statistics and related fields and leaders in diverse areas of interdisciplinary research, including the analysis of large-scale data, computational biology and bioinformatics, spatial data, environmental science, neuroscience, health care policy, and complex computer experiments.
Read past updates by visiting our email archive
Upcoming CATS Meetings and Events
September 14, 2017
Symposium and Webcast: Principles of Data-Driven Decision Making
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
Recent CATS Activity
Sites We Like
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Alfred O. Hero III, 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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Alicia Carriquiry (NAM)
Iowa State University
Alicia Carriquiry is Distinguished Professor of Liberal Arts and Sciences and Professor of Statistics at Iowa State University, where she directs the Center for Statistics and Applications in Forensic Evidence (CSAFE), a NIST Center of Excellence. She is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association, a Fellow of the Institute of Mathematical Statistics, and a Fellow of the International Society for Bayesian Analysis. She was recently elected to the National Academy of Medicine. Currently, she chairs the Committee to Evaluate Access to Mental Health Resources for Veterans Offered by the VA, and serves in the Advisory Board for DBASSE (Division of Behavioral and Social Sciences and Education) of the National Academies. Carriquiry’s research is in applications of statistics in human nutrition, bioinformatics, forensic sciences and traffic safety. She has published over 100 peer-reviewed articles in journals in statistics, economics, nutrition, bioinformatics, mathematics, animal genetics, and several other areas. Carriquiry was born in Uruguay, where she graduated as an engineer in 1982. After coming to the United States, she received an MSc in animal sci¬ence from the University of Illinois (1985), and an MSc in statistics (1986) and a PhD in statistics and animal genetics (1989) from Iowa State University.
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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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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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David Madigan is the Executive Vice President and Dean of Faculty of Arts and Sciences and Professor of Statistics at Columbia University. Prior to his position at Columbia, he worked for several technology companies and universities, including AT&T Inc. and the University of Washington. Dr. Madigan’s research interests and publications include topics such as Bayesian statistics, text mining, Monte Carlo methods, pharmacovigilance and probabilistic graphical models. He received a bachelor’s degree in Mathematical Sciences and Ph.D. in Statistics from Trinity College Dublin. Dr. Madigan is an elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and has served as Editor-in-Chief of Statistical Sciences.
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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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University of Toronto
Nancy Reid is a Professor of Statistical Sciences and Canada Research Chair in Statistical Theory and Applications at the University of Toronto. She has held visiting positions at University College, London, École Polytechnique Fédérale de Lausanne, Harvard Biostatistics, and the University of Texas at Austin. Dr. Reid’s research interests are in theoretical statistics and on the application of statistical science to social and scientific problems. She received a B.Math from University of Waterloo, a M.Sc. from the University of British Columbia, a Ph.D. from Stanford University, and a D. Math Honoris Causa from the University of Waterloo. Dr. Reid is the Director of the Canadian Statistical Sciences Institute, a Foreign Associate of the National Academy of Sciences and an elected fellow of the Royal Society of Canada and the Royal Society of Edinburgh.
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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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Carnegie Mellon University
Aarti Singh is an associate professor and former A. Nico Habermann Junior Faculty Chair in the Machine Learning Department at Carnegie Mellon University. Prior to her position at CMU, Dr. Singh was a postdoctoral research associate at the Program in Applied and Computational Mathematics at Princeton University. Her research interests include understanding and designing algorithms that consider the tradeoffs between computational efficiency and statistical optimality. Dr. Singh is also interested in interactive algorithms that assess data acquisition, storage, and processing. She received a B.E. in Electronics and Communication Engineering from the University of Delhi, and a M.S. and Ph.D. in Electrical Engineering from the University of Wisconsin-Madison. She has served as a Program Chair for the International Conference on Artificial Intelligence and Statistics and Institute of Mathematical Statistics New Researchers Conference, and she is a recipient of the United States Air Force Office of Scientific Research Young Investigator Award and the National Science Foundation CAREER Award.
PH: (202) 334-1378
Rodney N. Howard
Committee on Applied and Theoretical Statistics
The National Academies
500 Fifth Street NW
Washington, DC 20001