
The Duke probability group is a collection of faculty spanning the different divisions of the university who are interested in probability theory and the application of stochastic modeling.
The group runs a weekly seminar which loosely alternates between the theory and applications of probability and stochastic modeling. If you are interested in getting email about stochastic happenings at Duke, please subscribe to our mailing list.
Others: List by titles alphabetically photos Richard T. Durrett, James B. Duke Professor of Mathematics
 Jonathan C. Mattingly, Professor of Mathematics and Statistical Science and Chair of Mathematics
Jonathan Christopher Mattingly grew up in Charlotte, NC where he attended Irwin Ave elementary and Charlotte Country Day. He graduated from the NC School of Science and Mathematics and received a BS is Applied Mathematics with a concentration in physics from Yale University. After two years abroad with a year spent at ENS Lyon studying nonlinear and statistical physics on a Rotary Fellowship, he returned to the US to attend Princeton University where he obtained a PhD in Applied and Computational Mathematics in 1998. After 4 years as a Szego assistant professor at Stanford University and a year as a member of the IAS in Princeton, he moved to Duke in 2003. He is currently a Professor of Mathematics and of Statistical Science.
His expertise is in the longtime behavior of stochastic system including randomly forced fluid dynamics, turbulence, stochastic algorithms used in molecular dynamics and Bayesian sampling, and stochasticity in biochemical networks.
He is the recipient of a Sloan Fellowship and a PECASE CAREER award. He is also a fellow of the IMS and the AMS.
 Sayan Mukherjee, Professor of Statistical Science and Computer Science and Mathematics
 James H. Nolen, Associate Professor of Mathematics
I study partial differential equations, which have been used to model many phenomena in the natural sciences and engineering. In many cases, the parameters for such equations are known only approximately, or they may have fluctuations that are best described statistically. So, I am especially interested in equations modeling random phenomena and whether one can describe the statistical properties of the solution to these equations. For example, I have worked on nonlinear partial differential equations that describe waves and moving interfaces in random media. This work involves ideas from both analysis and probability. I also study the asymptotic behavior of stochastic processes.
 Dalia PatinoEcheverri, Gendell Family Associate Professor of Environmental Sciences and Policy and Associate Professor of Energy Systems and Public Policy and Assistant Professor in the Division of Earth and Ocean Sciences and Faculty Network Member of The Energy Initiative and Affiliate of the Duke Initiative for Science & Society
Dr. PatinoEcheverri’s research focuses on public policy design for energy systems, with a particular emphasis on managing the risks arising from the uncertainties influencing the outcomes of government actions. Much of her current work focuses on the policies that affect capital investment decisions within the electricity industry, and the corresponding costs to society of electricity and airemissions levels. Her models explore the effects of different government policies by representing the industry’s decisions under uncertainty on future technological advancements, fuel prices, and emissions regulations.
 Amilcare Porporato, Adjunct Professor of Civil and Environmental Engineering and Professor in the Division of Environmental Sciences and Policy
Amilcare Porporato earned a Master Degree in Civil Engineering (summa cum laude) in 1992 and his Ph.D. in 1996 from Polytechnic of Turin. He was appointed Assistant Professor in the Department of Hydraulics of the Polytechnic of Turin, and he moved to Duke University in 2003, where he is now Full Professor in the Department of Civil and Environmental Engineering with a secondary appointment with the Nicholas School of the Environment.
In June 1996, Porporato received the Arturo Parisatti International Price, awarded by the Istituto Veneto di Scienze, Lettere e Arti. He was Research Associate at the Texas A&M University (USA) in 1998 and Visiting Scholar at Princeton University (USA), Department of Civil and Environmental Engineering, from 1999 to 2001. In 20082009 he was the first Landolt & Cie Visiting Chair in “Innovative Strategies for a sustainable Future” at Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. He was awarded the 2007 Professor Senol Utku’ award, the 2010 Earl Brown II Outstanding Civil Engineering Faculty Award, and in 2011 he received a Lagrange fellowship from the Polytechnic of Turin, the CRT bank and the ISI (Institute for
Scientific Interchange). In 2012 he was elected an AGU fellow.
His main research interests regard nonlinear and stochastic dynamical systems, hydrometeorology and soilatmosphere interaction, soil moisture and plant dynamics, soil biogeochemistry, and ecohydrology.
Porporato has been Editor of Water Resources Research (AGU) (20042009), and he is currently editor for Hydrological Processes. He is also member of the editorial board of Advances in Water Resources and the Hydrologic Science Journal. Among other things, he was chairman and convener of the Ecohydrology sessions of the AGU Spring Meeting in 2001 and 2002 and of the EGU in 20042006. Porporato has been part of the Italian research groups of Turbulence and Vorticity and of Climate, Soil and Vegetation Interaction, an adviser for realtime forecasting in the Piedmont Region (Italy), and ecohydrology (US National Academy).
Porporato's didactic experience comprises courses in Environmental Fluid Mechanics, Hydraulics, Hydraulic Constructions, Statistical and Physical Hydrology, Ecohydrology, Nonlinear Dynamics and Stochastic Processes. He has also been the didactic coordinator for the International School "Hydroaid: Water for Development", coorganized by the Polytechnic of Turin and the Italian Ministry of Foreign Affairs.
Porporato is author of more than 140 peerreviewed papers, several publications presented at national and international conferences and invited talks. He is also coauthor of the book "Ecohydrology of water controlled ecosystems" (Cambridge Univ. Press, 2004) and the edited the book "Dryland Ecohydrology" (Springer, 2005).
 Scott C. Schmidler, Associate Professor of Statistical Science and Computer Science
 George E. Tauchen, William Henry Glasson Professor of Economics
George Tauchen is the William Henry Glasson Professor of Economics and professor of finance at the Fuqua School of Business. He joined the Duke faculty in 1977 after receiving his Ph.D. from the University of Minnesota. He did his undergraduate work at the University of Wisconsin. Professor Tauchen is a fellow of the Econometric Society, the American Statistical Association, the Journal of Econometrics, and the Society for Financial Econometrics (SoFie). He is also the 2003 Duke University Scholar/Teacher of the Year. Professor Tauchen is an internationally known time series econometrician. He has developed several important new techniques for making statistical inference from financial time series data and for testing models of financial markets. He has given invited lectures at many places around the world, including London, Paris, Beijing, Taipei, Hong Kong, and Sydney. His current research (with Professor Li of Duke) examines the impact of large jumplike moves in stock market returns on the returns of various portfolios and individual securities. He is a former editor of the Journal of Business and Economic Statistics (JBES) and former associate editor of Econometrica, Econometric Theory, The Journal of the American Statistical Association (JASA), and JBES. He is currently CoEditor of the Journal of Financial Econometrics.
 Surya T. Tokdar, Associate Professor of Statistical Science and Director or Graduate Studies of Statistical Science and Faculty Network Member of Duke Institute for Brain Sciences
 Mike West, Arts and Sciences Professor of Statistics and Decision Sciences and Member of Duke Cancer Institute
Go to my personal web page for links and info on my teaching, publication list (sortable and searchable  just click on table headers), current research, current & past students, software, etc.
 Robert L. Wolpert, Professor of Statistical Science and Professor in the Division of Environmental Sciences and Policy
I'm a stochastic modeler I build computerresident mathematical models for complex systems, and invent and program numerical algorithms for making inference from the models. Usually this involves predicting things that haven't been measured (yet). Always it involves managing uncertainty and making good decisions when some of the information we'd need to be fully comfortable in our decisionmaking is unknown.
Originally trained as a mathematician specializing in probability theory and stochastic processes, I was drawn to statistics by the interplay between theoretical and applied research with new applications suggesting what statistical areas need theoretical development, and advances in theory and methodology suggesting what applications were becoming practical and so interesting. Through all of my statistical interests (theoretical, applied, and methodological) runs the unifying theme of the <STRONG>Likelihood Principle</STRONG>, a constant aid in the search for sensible methods of inference in complex statistical problems where commonlyused methods seem unsuitable. Three specific examples of such areas are:
* Computer modeling, the construction and analysis of fast small Bayesian statistical emulators for big slow simulation models; * Metaanalysis, of how we can synthesize evidence of different sorts about a statistical problem; and * Nonparametric Bayesian analysis, for applications in which common parametric families of distributions seem unsuitable.
Many of the methods in common use in each of these areas are hard or impossible to justify, and can lead to very odd inferences that seem to misrepresent the statistical evidence. Many of the newer approaches abandon the ``iid'' paradigm in order to reflect patterns of regional variation, and abandon familiar (e.g. Gaussian) distributions in order to reflect the heavier tails observed in realistic data, and nearly all of them depend on recent advances in the power of computer hardware and algorithms, leading to three other areas of interest:
* Spatial Statistics, * Statistical Extremes, and * Statistical computation.
I have a special interest in developing statistical methods for application to problems in Environmental Science, where traditional methods often fail. Recent examples include developing new and better ways to estimate the mortality to birds and bats from encounters with wind turbines; the development of nonexchangeable hierarchical Bayesian models for synthesizing evidence about the health effects of environmental pollutants; and the use of highdimensional Bayesian models to reflect uncertainty in mechanistic environmental simulation models. <P> My current (20152016) research involves modelling and Bayesian inference of dependent time series and (continuoustime) stochastic processes with jumps (examples include work loads on networks of digital devices; peak heights in mass spectrometry experiments; or multiple pollutant levels at spatially and temporally distributed sites), problems arising in astrophysics (Gamma ray bursts) and highenergy physics (heavy ion collisions), and the statistical modelling of risk from, e.g., volcanic eruption.
*=primary specialty 