Steve MacEachern is an American Statistician. MacEachern is a Distinguished Arts & Sciences Professor of Statistics at the Ohio State University. [1] He received his B.A. in Mathematics from Carleton College in 1982 and his Ph.D. in Statistics from the University of Minnesota in 1988. His doctoral work focused on nonparametric Bayesian methods under the guidance of Don Berry. MacEachern joined the faculty at Ohio State in 1988 and has been a member of the Department of Statistics ever since. He has a courtesy appointment as a Professor in the Department of Psychology. He is best known for Bayesian modeling and computation, with a particular emphasis on dependent Dirichlet processes. He has published extensively in leading statistical journals, and his work has had a significant impact on the field. [2] [3] [4] [5]
MacEachern has received numerous honors throughout his career, including being elected as a Fellow of the American Statistical Association [6] in 2006, of the International Society for Bayesian Analysis [7] in 2020 and of the Institute of Mathematical Statistics [8] in 2021. He served as President of the International Society for Bayesian Analysis in 2016. [9]
George Edward Pelham Box was a British statistician, who worked in the areas of quality control, time-series analysis, design of experiments, and Bayesian inference. He has been called "one of the great statistical minds of the 20th century".
Zoubin Ghahramani FRS is a British-Iranian researcher and Professor of Information Engineering at the University of Cambridge. He holds joint appointments at University College London and the Alan Turing Institute. and has been a Fellow of St John's College, Cambridge since 2009. He was Associate Research Professor at Carnegie Mellon University School of Computer Science from 2003–2012. He was also the Chief Scientist of Uber from 2016 until 2020. He joined Google Brain in 2020 as senior research director. He is also Deputy Director of the Leverhulme Centre for the Future of Intelligence.
Jayanta Kumar Ghosh was an Indian statistician, an emeritus professor at Indian Statistical Institute and a professor of statistics at Purdue University.
Larry Alan Wasserman is a Canadian-American statistician and a professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University.
David Brian Dunson is an American statistician who is Arts and Sciences Distinguished Professor of Statistical Science, Mathematics and Electrical & Computer Engineering at Duke University. His research focuses on developing statistical methods for complex and high-dimensional data. Particular themes of his work include the use of Bayesian hierarchical models, methods for learning latent structure in complex data, and the development of computationally efficient algorithms for uncertainty quantification. He is currently serving as joint Editor of the Journal of the Royal Statistical Society, Series B.
David L. Banks is an American statistician at Duke University.
Noel Andrew Cressie is an Australian and American statistician. He is Distinguished Professor and Director, Centre for Environmental Informatics, at the University of Wollongong in Wollongong, Australia.
Alan Enoch Gelfand is an American statistician, and is currently the James B. Duke Professor of Statistics and Decision Sciences at Duke University. Gelfand’s research includes substantial contributions to the fields of Bayesian statistics, spatial statistics and hierarchical modeling.
Merlise Aycock Clyde is an American statistician known for her work in model averaging for Bayesian statistics. She is a professor of Statistical Science and immediate past chair of the Department of Statistical Science at Duke University. She was president of the International Society for Bayesian Analysis (ISBA) in 2013, and chair of the Section on Bayesian Statistical Science of the American Statistical Association for 2018.
Sonia Petrone is an Italian mathematical statistician, known for her work in Bayesian statistics, including use of Bernstein polynomials for nonparametric methods in Bayesian statistics.[RBP][BDE][CPP] With Patrizia Campagnoli and Giovanni Petris she is the author of the book Dynamic Linear Models with R .[DLM]
Yee-Whye Teh is a professor of statistical machine learning in the Department of Statistics, University of Oxford. Prior to 2012 he was a reader at the Gatsby Charitable Foundation computational neuroscience unit at University College London. His work is primarily in machine learning, artificial intelligence, statistics and computer science.
Raquel Prado is a Venezuelan Bayesian statistician. She is a professor of statistics in the Jack Baskin School of Engineering of the University of California, Santa Cruz, and has been elected president of the International Society for Bayesian Analysis for the 2019 term.
Dipak Kumar Dey is an Indian-American statistician best known for his work on Bayesian methodologies. He is currently the Board of Trustees Distinguished Professor in the Department of Statistics at the University of Connecticut. Dey has an international reputation as a statistician as well as a data scientist. Since he earned a Ph.D. degree in statistics from Purdue University in 1980, Dey has made tremendous contributions to the development of modern statistics, especially in Bayesian analysis, decision science and model selection. Dey has published more than 10 books and edited volumes, and over 260 research articles in peer-refereed national and international journals. In addition, the statistical methodologies that he has developed has found wide applications in a plethora of interdisciplinary and applied fields, such as biometry and bioinformatics, genetics, econometrics, environmental science, and social science. Dey has supervised 40 Ph.D. students, and presented more than 200 professional talks in colloquia, seminars and conferences all over the world. During his career, Dey has been a visiting professor or scholar at many institutions or research centers around the world, such as Macquarie University, Pontificia Universidad Católica de Chile,, University of São Paulo, University of British Columbia, Statistical and Applied Mathematical Sciences Institute, etc. Dey is an elected fellow of the American Association for the Advancement of Science, the American Statistical Association, the Institute of Mathematical Statistics, the International Society for Bayesian Analysis and the International Statistical Institute.
Robert E. Kass is the Maurice Falk Professor of Statistics and Computational Neuroscience in the Department of Statistics and Data Science, the Machine Learning Department, and the Neuroscience Institute at Carnegie Mellon University.
Siddhartha Chib is an econometrician and statistician, the Harry C. Hartkopf Professor of Econometrics and Statistics at Washington University in St. Louis. His work is primarily in Bayesian statistics, econometrics, and Markov chain Monte Carlo methods.
Linda Hong Zhao is a Chinese-American statistician. She is a Professor of Statistics and at the Wharton School of the University of Pennsylvania. She is a Fellow of the Institute of Mathematical Statistics. Zhao specializes in modern machine learning methods.
Charles "Chuck" Joel Stone was an American statistician and mathematician.
Michael David Escobar is an American biostatistician who is known for Bayesian nonparametrics, mixture models.
Mike West is an English and American statistician. West works primarily in the field of Bayesian statistics, with research contributions ranging from theory to applied research in areas including finance, commerce, macroeconomics, climatology, engineering, genomics and other areas of biology. Since 1999, West has been the Arts & Sciences Distinguished Professor of Statistics & Decision Sciences in the Department of Statistical Science at Duke University.
Emily Beth Fox is an American data scientist and statistician, a professor of statistics at Stanford University, and an executive for drug discovery firm insitro. Her research applies Bayesian modeling of time series, Hierarchical Dirichlet processes, and Monte Carlo methods to problems in health and neuroscience.
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