Research Teams for Graduation with Distinction

Students considering pursuing Graduation with Distinction should connect with potential faculty advisors during the Spring semester of your junior year and must identify an advisor by the end of that semester. You should explore the web pages of the Statistical Science faculty to get the flavor of their current research activities and contact the professors directly for discussion and further information. To help get you started, department faculty have provided example topics below, and they are always interested in hearing about your own project ideas as well.

Note: Students working with visiting faculty should also identify a co-advisor from the regular Statistical Science faculty.

Professor & Potential Project Topics

David Banks
  • Agent-based models
  • Bayesian game theory
  • Metabolomics
Jim Berger
  • Incorrect use of p-values for testing precise hypotheses
  • Imprecise probabilities versus full probabilistic modeling
  • Do Bayes factors help to mitigate the effect of model bias?
Mine Çetinkaya-Rundel
  • Statistics education
  • Spatial statistics
Cliburn Chan
  • Computational immunology
  • Statistical methodology for immunological laboratory techniques
  • Informatics of the immune system
James Clark
  • Forest responses to global change-exchange of water, CO2, and energy
  • Impact of co-infection of multiple pathogens on multiple hosts
  • Inference on demography and health of natural populations
Merlise Clyde
  • Model uncertainty: model selection & model averaging
  • Applications in neuroscience, chemisitry, genectic-epidemiology, astronomy, environmental statistics
David Dunson
  • Statistical models for studying exposure disease relationships
Alan Gelfand
  • Modelling and analysis for spatial data
  • Statistics in environmental policy studies
  • Statistics for ecological processes
Alex Hartemink
  • Bayesian networks and computational biology
Elizabeth Hauser
  • Statistical genetics
Katherine Heller
  • Machine learning
Ed Iversen
  • Statistical methods in genetics and genomics
  • Cancer risk assessment
  • Radiation Bio-Dosimetry
Fan Li
  • Causal inference in observational studies
Li Ma 
  • High-dimensional inference
  • Adaptive multi-resolution inference
  • Applications in genetics
Anthea Monod (Vis. Fac.)
  • Applied & random topology
  • Topological data analysis
  • Manifold learning
  • Spatial analysis
  • Point processes
Jonathan Mattingley
  • Probability and statistics on networks
Sayan Mukherjee
  • Statistics and genomics
  • Statistical machine learning
Galen Reeves
  • Signal processing, statistics, and information theory
Jerry Reiter
  • Statistics in policy and government
  • Methods for protecting data confidentiality
  • Methods for handling missing data
Colin Rundel
  • Spatial statistics with an emphasis on biological and ecological systems
  • Computational methods
Scott Schmidler
  • Statistical estimation in scientific models: biology, chemistry, and physics
  • Statistics in finance and decision making/portfolio theory
  • Simulation and statistical inference for SDEs
Dalene Stangl 
  • Statistical methods in clinical trials
  • Meta-analysis(combining information from many studies)
  • Applied decision theoretic statistics
  • Statistics education
Beka Steorts
  • Record linkage
  • Machine learning
  • Privacy
  • Clustering
Surya Tokdar
  • Nonparametric statistics
  • Statistics in the neurosciences
  • Counting missing hurricanes
Mike West 
  • Time series, decision analysis and econ/financial applications
Robert Wolpert
  • Heavy-tailed distributions and statistical extremes
  • Spatial statistics and random fields