Discoveries in our department, based on inferences and decisions from the data all around us, deliver research advances with a real-world impact in health care, finance, public life, technology and science.
- Algorithmic Fairness
- Bayesian Statistics
- Causal Inference
- Longitudinal Analysis
- Monte Carlo and MCMC Methods
- Network Analysis
- Nonparametric Methods
- Spatial and Spatio-Temporal Statistics
- Statistical/Machine Learning
- Time-Series Analysis
Centers, Institutes and Initiatives
Statistics and Data Sciences researchers participate in interdisciplinary efforts across campus to advance insights in areas such as machine learning, epidemiology and population research.
- Center for Health & Environment: Education and Research (CHEER) is a hub for multidisciplinary environmental health sciences research and education, bringing together experts from across UT Austin.
- Good Systems is working to establish a framework for evaluating, developing, implementing and regulating AI-based technologies so they reflect human values. It is a UT Austin Grand Challenge and part of its Bridging Barriers initiative.
- Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) represents a major UT Austin-led research initiative with TACC, the McDonald Observatory and faculty experts from across astronomy, physics, statistics and data sciences and more.
- Machine Learning Laboratory includes computer scientists, engineers, data scientists, statisticians and mathematicians from across campus and serves as the academic home for the NSF-funded Institute for Foundations of Machine Learning.
- Population Research Center includes researchers from across campus in areas such as demography; education, work and inequality; and population and reproductive health.
- Texas Advanced Computing Center (TACC) is home to the world's most powerful university supercomputer.
December 5, 2022 • by Staff Writer
Health benefits of using wind energy instead of fossil fuels could quadruple if the most polluting power plants are selected for dialing down, new study...
July 8, 2022 • by Lauren Macknight
April 5, 2022 • by the Department of Statistics and Data Sciences
February 25, 2022 • by the Department of Statistics and Data Sciences
February 2, 2022 • by Lisa Lawrence
November 3, 2021 • by the Department of Statistics and Data Sciences