Robert L. Wolpert
Professor of Statistical Science
I'm a stochastic modeler-- I build computer-resident mathematical modelsfor complex systems, and invent and program numerical algorithms for makinginference from the models. Usually this involves predicting things thathaven't been measured (yet). Always it involves managing uncertainty andmaking good decisions when some of the information we'd need to be fullycomfortable in our decision-making is unknown.
Originally trained as a mathematician specializing in probability theory andstochastic processes, I was drawn to statistics by the interplay betweentheoretical and applied research- with new applications suggesting whatstatistical areas need theoretical development, and advances in theory andmethodology suggesting what applications were becoming practical and sointeresting. Through all of my statistical interests (theoretical, applied,and methodological) runs the unifying theme of the <STRONG>LikelihoodPrinciple</STRONG>, a constant aid in the search for sensible methods ofinference in complex statistical problems where commonly-used methods seemunsuitable. 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 orimpossible to justify, and can lead to very odd inferences that seem tomisrepresent the statistical evidence. Many of the newer approachesabandon the ``iid'' paradigm in order to reflect patterns of regionalvariation, and abandon familiar (e.g. Gaussian) distributions in order toreflect the heavier tails observed in realistic data, and nearly all ofthem depend on recent advances in the power of computer hardware andalgorithms, leading to three other areas of interest:
* Spatial Statistics, * Statistical Extremes, and * Statistical computation.
I have a special interest in developing statistical methods for applicationto problems in Environmental Science, where traditional methods often fail.Recent examples include developing new and better ways to estimate themortality to birds and bats from encounters with wind turbines; thedevelopment of nonexchangeable hierarchical Bayesian models forsynthesizing evidence about the health effects of environmental pollutants;and the use of high-dimensional Bayesian models to reflect uncertainty inmechanistic environmental simulation models. <P> My current (2015-2016)research involves modelling and Bayesian inference of dependent time seriesand (continuous-time) stochastic processes with jumps (examples includework loads on networks of digital devices; peak heights in massspectrometry experiments; or multiple pollutant levels at spatially andtemporally distributed sites), problems arising in astrophysics (Gamma raybursts) and high-energy physics (heavy ion collisions), and the statisticalmodelling of risk from, e.g., volcanic eruption.
Collaborative Research: SI2-SSI: Jet Energy-loss Tomography with a Statistically and Computationally Advanced Program Envelope (JETSCAPE) awarded by National Science Foundation (Co-Principal Investigator). 2016 to 2020
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
Collaborative Research: Advances Statistical Surrogates for Linking Multiple Computer Models with Disparate Data for Quantifying Uncertain Hazards awarded by National Science Foundation (Principal Investigator). 2016 to 2018
EMSW21-RTG: Geometric, Topological awarded by National Science Foundation (Key Faculty). 2011 to 2017
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
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)
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
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
Wolpert, RL, Clyde, MA, and Tu, C. "Stochastic expansions using continuous dictionaries: Lévy adaptive regression kernels." Annals of Statistics 39.4 (2011): 1916-1962. Full Text Open Access Copy
Wolpert, RL, Ickstadt, K, and Hansen, MB. "A nonparametric Bayesian approach to inverse problems." 2003.
Ickstadt, K, and Wolpert, RL. "Spatial regression for marked point processes." 1999.
Wolpert, RL, and Lavine, M. "Markov random field priors for univariate density estimation." 1996. Full Text
WARRENHICKS, WJ, and WOLPERT, RL. "PREDICTIVE MODELS OF FISH RESPONSE TO ACIDIFICATION - USING BAYESIAN-INFERENCE TO COMBINE LABORATORY AND FIELD-MEASUREMENTS." 1994.