Amy H. Herring
Dr. Herring is a Professor in the Department of Statistical Science and the Global Health Initiative at Duke University 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.
Tim Hesterberg is a senior statistician at Google. He received his Ph.D. in statistics from Stanford University and his B.A. in math from St. Olaf College. Dr. Hesterberg wrote Mathematical Statistics with Resampling and R (Wiley, 2011) with Laura Chihara of Carleton College, which is a widely used text. His primary role at Google is evaluating the effectiveness of display ads, and he consults on a variety of other projects including work in telecommunications, finance, phramaceuticals, insurance, power systems, and seismic studies. He creates statistical software for bootstrap and other resampling methods, least angle regression, missing data, sequential designs for clinical trials, functional data analysis, time series analysis, and image analysis, funded primarily through NIH and NSF. He is an elected board member of the National Institute of Statistical Sciences and the Interface Foundation of Noarth America (Interface between statistical sciences and computing). He helped write the ASA Guidelines for Undergraduate Statistics Programs and has authored widely used analysis packages includine the “Resample” and “S+Resample” packages in R. Recently, he chaperoned high school students to set up computer labs in Guatemala, Ecuador, and Costa Rica with Technology Service Corps.
Dr. 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.
Dr. 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.
Xiao-Li Meng is dean of the Harvard University Graduate School of Arts and Sciences (GSAS), Whipple V. N. Jones Professor, and former chair of statistics at Harvard. He is well known for his depth and breadth in research, his innovation and passion in pedagogy, and his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng has received numerous awards and honors for the more than 150 publications he has authored in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development; he has delivered more than 400 research presentations and public speeches on these topics, and he is the author of “The XL-Files," a regularly appearing column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, frequentist, and fiducial perspectives; quantify ignorance via invariance principles; multi-phase and multi-resolution inferences), to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling), to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his B.S. in mathematics from Fudan University in 1982 and his Ph.D. in statistics from Harvard in 1990. He was on the faculty of the University of Chicago from 1991 to 2001 before returning to Harvard as professor of statistics, where he was appointed department chair in 2004 and the Whipple V. N. Jones Professor in 2007. He was appointed GSAS dean on August 15, 2012.
José M. Moura
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).
Dr. Prado is professor of statistics in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz (UCSC). Her research lies in the general area of Bayesian modeling for temporal, spatio-temporal and large-dimensional data, with an emphasis on neuroimaging and neuroscience applications. She is and has been principal investigator and co-investigator in interdisciplinary projects funded by the National Science Foundation (NSF) and the National Institutes of Health (NIH). Dr. Prado has delivered invited talks and short courses in several prestigious international conferences and academic institutions around the world. She has been the primary supervisor of more than a dozen graduate students and has also been actively involved in mentoring graduate students from diverse backgrounds, including financially disadvantaged students. She is currently supervising four Ph.D. students and one M.S. student at UCSC. Dr. Prado is a fellow of the American Statistical Association and currently serves as a standing member of the NIH study section on Biostatistical Methods and Research Design (BMRD). Dr. Prado is also president-elect of the International Society for Bayesian Analysis (ISBA).
Nancy M. Reid
Dr. 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.
Dr. 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.
Dr. 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.
Alyson G. Wilson
Dr. Wilson is a professor in the Department of Statistics and principal investigator for the Laboratory for Analytic Sciences at North Carolina State University (NCSU). She is a fellow of the American Statistical Association and the American Association for the Advancement of Science. Her research interests include statistical reliability, Bayesian methods, and the application of statistics to problems in defense and national security. Prior to joining NCSU, Dr. Wilson was a Research Staff Member at the IDA Science and Technology Policy Institute (2011-2013), an associate professor in the Department of Statistics at Iowa State University (2008-2011), a Technical Staff Member in the Statistical Sciences Group at Los Alamos National Laboratory (1999-2008), and a senior statistician and operations research analyst with Cowboy Programming Resources (1995-1999). Dr. Wilson received her Ph.D. in statistics from Duke University, her M.S. in statistics from Carnegie-Mellon University, and her B.A. in mathematical sciences from Rice University.
Benjamin Wender - (Staff Officer)
Ben Wender is a Program Officer with the Board on Mathematical Sciences and Analytics and the Board on Energy and Environmental Systems at the National Academies of Sciences, Engineering, and Medicine. He joined the Academies as a Christine Mirzayan Fellow and has since worked on projects relating to statistical inference, electricity system modernization and resilience, evidenced-based policy making and governance, and data science education. He completed his M.S. and Ph.D. in Civil, Environmental, and Sustainable Engineering at Arizona State University, where he researched environmental decision making under uncertainty. His dissertation applied sensitivity analyses to complex environmental models with uncertain data to prioritize research investments with the greatest potential to improve decision confidence. Over the course of his studies, Ben participated in research internships with the U.S. Army Corps of Engineers, General Electric Global Research Center, and Los Alamos National Laboratory. Ben completed his B.S. degree in physics at Hampshire College.