Robert L. Wolpert
Professor of Statistical Science
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 Likelihood Principle, 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
- 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
- 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.
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.
Collaborative Research: Using Precursor Information to Update Probabilistic Hazard Maps awarded by National Science Foundation (Principal Investigator). 2018 to 2020
GRB Pulse Decomposition Using Bayesian Droplets awarded by Cornell University (Principal Investigator). 2019 to 2020
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 (Principal Investigator). 2015 to 2019
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 2019
EMSW21-RTG: Geometric, Topological and Statistical Methods for Analyzing Massive Datasets awarded by National Science Foundation (Key Faculty). 2011 to 2018
Collaborative Research: Statistical And Computational Models and Methods for Extracting Knowledge from Massive Disparate Data for Quantifying Uncertain Hazards 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 (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
Berger, James, et al. “Statistische und Probabilistische Methoden der Modellwahl.” Oberwolfach Reports, European Mathematical Society Publishing House, 2005, pp. 2611–704. Crossref, doi:10.4171/owr/2005/47. Full Text Open Access Copy