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

• 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

Sudipta Dasmohapatra

• Statistical models for studying exposure disease relationships

Alan Gelfand

• Modelling and analysis for spatial data
• Statistics in environmental policy studies
• Statistics for ecological processes

Katherine Heller

• Machine learning

Amy Herring

• Health Data Science and Biostatistics
• Applications in Reproductive Health, Maternal and Child Health, and Environmental Health and Longitudinal Data
• Methods for Missing 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
• Health data science
• Missing data 

Li Ma 

• High-dimensional inference
• Adaptive multi-resolution inference
• Applications in genetics

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

• 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

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

• Heavy-tailed distributions and statistical extremes
• Spatial statistics and random fields

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