Research Teams for Graduation with Distinction

Students considering pursuing Graduation with Distinction should connect with potential faculty advisors during the Spring semester of their 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 a 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

Filippo Ascolani

Computational methods for dimensional problems
Scalable MCMC algorithms
Bayesian nonparametric models for complex data

David Banks

Agent-based models
Bayesian game theory
Text Mining
Dynamic Network Models

Mine Çetinkaya-Rundel

Statistics and data science education
Statistical computing
Data visualization
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, chemistry, genetic-epidemiology, astronomy, environmental statistics

David Dunson

Statistical methods for studying exposure disease relationships
Brain connection networks
Statistical ecology - studying species communities & biodiversity
Methods for dimensionality reduction - inferring latent structure in data
Models for dependent data - multivariate, temporal, spatial, etc

Alexander Fisher

Bayesian phylogenetics
Scalable algorithms

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

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

Jason Xu

Inference for stochastic processes
Optimization and Machine Learning
Clustering