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

Alessandro Arlotto
  • Applied Probability and Optimization
  • Sequential Decision Making
    David Banks
    • Agent-based models
    • Bayesian game theory
    • Text Mining
    • Dynamic Network Models
    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 (on leave 2019-2020)
    • Statistics and data science 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
    Sudipta Dasmohapatra
    • Statistical models in business applications
    • Unsupervised learning models
    David Dunson
    • Statistical models for studying exposure disease relationships
    • Representation and analysis of complex data
    • Nonparametric Bayes
    • Machine learning
    • Scalable algorithms
    Alan Gelfand
    • Modelling and analysis for spatial data
    • Statistics in environmental policy studies
    • Statistics for ecological processes
    Katherine Heller (on leave 2019-2020)
    • Machine learning
    Amy Herring
    • Health data science and biostatistics
    • Applications in reproductive health, maternal and child health, and environmental health
    • Methods for missing data
    • Dimension reduction methods
    • Longitudinal or correlated data
    Peter Hoff
    • Building network datasets from text
    • Interfacing R with data-gathering sensors
    • Internet Security
    Ed Iversen
    • Statistical methods in genetics and genomics
    • Cancer risk assessment
    • Radiation Bio-Dosimetry
    Fan Li
    • Causal inference
    • Comparative effectiveness research
    • Health policy
    Li Ma 
    • High-dimensional inference
    • Adaptive multi-resolution inference
    • Applications in genetics
    Simon Mak
    • Big data methods
    • Machine learning
    • Experimental design
    • e-commerce
    • Applications in mechanical, biomedical, and financial engineering
      Jonathan Mattingly
      • Probability and statistics on networks
      Ezra Miller
      • Geometric and Topological Data Analysis
      • Statistics of Data Sampled from nonlinear Spaces
      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
      Cynthia Rudin
      • Machine learning and interpretability
      • Causal inference and uncertainty quantification
      • Applications, including social science
      Colin Rundel (on leave 2019-2020)
      • Spatial statistics with an emphasis on biological and ecological systems
      • Computational methods
      Shawn Santo
      • High-dimensional dependent data
      • Statistical computing
      • Sports analytics
      • Statistics pedagogy
      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
      Beka Steorts
      • Record linkage
      • Machine learning
      • Privacy
      • Clustering
      Maria Tackett
      • Statistics Education
      • Statistics in forensics science
      Surya Tokdar
      • Nonparametric statistics
      • Statistics in the neurosciences
      • Counting missing hurricanes
      Alex Volfovsky
      • Social Network Analysis
      • Causal Inference
      Mike West 
      • Time series
      • Decision analysis
      • Economics/financial & business applications
      Robert Wolpert
      • Uncertainty quantification in cosmology & astrophysics
      • Stochastic processes, random fields, & their applications
      • Theory & practice of Bayesian synthesis of evidence & information
      • Modeling of hazard and risk
      Hau-Tieng Wu
      • Physiological signal processing with manifold learning and time-frequency analysis
      • Analyze multimodule physiological signals based on the common manifold model
      • Statistical analysis of the developed machine learning and time-frequency analysis algorithms
      • Application to medical problems from cardiology, sleep, anesthesiology, ICU, etc
      Jason Xu
      • Inference for stochastic inferences
      • Optimization and Machine Learning