Robert L. Wolpert

Professor of Statistical Science

External address: 
214 Old Chemistry, Durham, NC 27708-0251
Internal office address: 
Box 90251, Durham, NC 27708-0251
Phone: 
(919) 684-3275
Email: 
Office Hours: 
Vary from term to term.  Check course website.

Overview

I'm a stochastic modeler-- I build computer-resident 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 decision-making 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 commonly-used 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;
* Meta-analysis, 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 high-dimensional Bayesian models to reflect uncertainty in
mechanistic environmental simulation models. <P> My current (2015-2016)
research involves modelling and Bayesian inference of dependent time series
and (continuous-time) 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 high-energy physics (heavy ion collisions), and the statistical
modelling of risk from, e.g., volcanic eruption.

Education & Training

  • Ph.D., Princeton University 1976

  • B.A., Cornell University 1972

Hazards SEES: Persistent volcanic crises -- resilience in the face of prolonged and uncertain risk awarded by State University of New York - Buffalo (Principal Investigator). 2015 to 2018

EMSW21-RTG: Geometric, Topological awarded by National Science Foundation (Key Faculty). 2011 to 2018

Collaborative Research: Statistical And Computational Models and Methods for Extracting Knowledge from Massive Disparate awarded by National Science Foundation (Principal Investigator). 2012 to 2015

Hazards SEES Type 1: Persistent volcanic crises in the USA: from precursors to resilience awarded by University of Hawaii System (Principal Investigator). 2013 to 2015

FRG: Collaborative Research: Prediction and Risk of Extreme Events Utilizing Mathematical Computer Models of Geophysical awarded by National Science Foundation (Principal Investigator). 2008 to 2012

SCREMS: Distributed Environments for Stochastic Computation awarded by National Science Foundation (Co-Principal Investigator). 2004 to 2007

Spatial-temporal Models for Environmental Health Effects awarded by Environmental Protection Agency (Co-Principal Investigator). 2001 to 2006

Sixth World Meeting of the International Society for Bayesian Analysis awarded by National Science Foundation (Principal Investigator). 2000 to 2001

Pages

Clyde, MA, House, L, and Wolpert, RL. "Nonparametric Models for Proteomic Peak Identification and Quantification." Bayesian Inference for Gene Expression and Proteomics. Ed. K-A Do, P Müller, and M Vannucci. Cambridge University Press, 2006. 293-308. (Chapter)

Cao, S, Park, C, Barbieri, RA, Bass, SA, Bazow, D, Bernhard, J, Coleman, J, Fries, R, Gale, C, He, Y, Heinz, U, Jacak, BV, Jacobs, PM, Jeon, S, Kordell, M, Kumar, A, Luo, T, Majumder, A, Nejahi, Y, Pablos, D, Pang, L-G, Putschke, JH, Roland, G, Rose, S, Schenke, B, Schwiebert, L, Shen, C, Sirimanna, C, Soltz, RA, Velicanu, D, Vujanovic, G, Wang, X-N, and Wolpert, RL. "Multistage Monte Carlo simulation of jet modification in a static medium." Physical Review C 96.2 (August 2017). Full Text

Ernst, PA, Brown, LD, Shepp, L, and Wolpert, RL. "Stationary Gaussian Markov processes as limits of stationary autoregressive time series." Journal of Multivariate Analysis 155 (March 2017): 180-186. Full Text

Wolpert, RL, Ogburn, SE, and Calder, ES. "The longevity of lava dome eruptions." Journal of Geophysical Research: Solid Earth 121.2 (February 2016): 676-686. Full Text

Bernhard, JE, Marcy, PW, Coleman-Smith, CE, Huzurbazar, S, Wolpert, RL, and Bass, SA. "Quantifying properties of hot and dense QCD matter through systematic model-to-data comparison." Physical Review C 91.5 (May 2015). Full Text

Bayarri, MJ, Berger, JO, Calder, ES, Patra, AK, Pitman, EB, Spiller, ET, and Wolpert, RL. "PROBABILISTIC QUANTIFICATION OF HAZARDS: A METHODOLOGY USING SMALL ENSEMBLES OF PHYSICS-BASED SIMULATIONS AND STATISTICAL SURROGATES." International Journal for Uncertainty Quantification 5.4 (2015): 297-325. Full Text

Gómez, FA, Coleman-Smith, CE, O'Shea, BW, Tumlinson, J, and Wolpert, RL. "DISSECTING GALAXY FORMATION MODELS WITH SENSITIVITY ANALYSIS—A NEW APPROACH TO CONSTRAIN THE MILKY WAY FORMATION HISTORY." The Astrophysical Journal 787.1 (May 20, 2014): 20-20. Full Text

Novak, J, Novak, K, Pratt, S, Vredevoogd, J, Coleman-Smith, CE, and Wolpert, RL. "Determining fundamental properties of matter created in ultrarelativistic heavy-ion collisions." Physical Review C 89.3 (March 2014). Full Text

Petersen, H, Coleman-Smith, CE, and Wolpert, RL. "Quantifying initial state fluctuations in heavy ion collisions." Acta Physica Polonica B, Proceedings Supplement 6.3 (October 21, 2013): 797-802. Full Text

Wolpert, RL, and Schmidler, SC. "alpha-STABLE LIMIT LAWS FOR HARMONIC MEAN ESTIMATORS OF MARGINAL LIKELIHOODS." STATISTICA SINICA 22.3 (July 2012): 1233-1251. Full Text

Gómez, FA, Coleman-Smith, CE, O'Shea, BW, Tumlinson, J, and Wolpert, RL. "Characterizing the formation history of milky way like stellar halos with model emulators." Astrophysical Journal 760.2 (2012). Full Text

Pages