carvalho2Carlos Carvalho

Associate Professor
Department of Statistics and Data Sciences (SDS)
Department of Information, Risk, and Operations Management (IROM)

I am a statistician and my research centers on the development of methodological aspects of structured probability models for large-scale multivariate problems, with applications ranging from financial time series to high-throughput cancer genomics. My work also pays special attention to the development and improvement of associated computational tools for model selection and inference. My current projects are in financial econometrics and empirical asset pricing problems.

damienPaul Damien

Professor
Department of Information, Risk, and Operations Management (IROM)
Department of Finance


I am interested in Bayesian Methods and Stochastic Optimization.

 

 

 

DanielsMichael Daniels

Professor
Department of Statistics and Data Sciences (SDS)
Department of Integrative Biology (IB)

The focus of my research program revolves around incomplete longitudinal data with special attention to estimation of the dependence structures and more recently, methods for causal inference with applications to mediation. My current collaborations include clinical trials in weight management and muscular dystrophy and questions involving the impact of recent Medicare legislation on 'preventable' hospital outcomes.
http://www.sbs.utexas.edu/mjdaniels/


meyersLauren Ancel Meyers

Director, Department of Statistics and Data Sciences (SDS)
Professor, Department of Integrative Biology (IB)

I lead a large interdisciplinary research team focused on the optimization of infectious disease surveillance systems and control policies, and the development of decision-support software for pandemic preparedness and response. My research on the dynamics and control of diseases including influenza, SARS, HIV, walking pneumonia, and meningitis has been published in over 60 peer-reviewed publications and supported by NIH (MIDAS), NSF, CDC, Association of Public Health Labs, James S. McDonnell Foundation, and Texas Department of State Health Services. My work has been highlighted in The Wall Street Journal, Newsweek, the BBC, and other news sources. My expertise has also been sought by a number of government agencies, including the CDC, Biomedical Advanced Research and Development Authority (BARDA), and US National Intelligence Council.

MuellerPeter Müller

Professor
Department of Statistics and Data Sciences (SDS)
Department of Mathematics

I am interested in methods and applications of Bayesian inference. More specifically, I am working on nonparametric Bayesian inference, decision problems, and applications to biomedical research problems. Nonparametric Bayesian inference refers to prior models for infinite dimensional random quantities, typically random probability measures. Decision problems include particular clinical trial design and multiple comparison procedures. Other applications that I am interested in include inference related to dependence structure, specifically graphical models to formalize inference about dependence for high throughput genomic data. Another large area of application is population pharmacokinetic and pharmacodynamic models, which give rise to many good applications that exploit many of my methodological interests.

pillowJonathan Pillow

Assistant Professor
Department of Psychology
Department of Neuroscience

My research focuses on statistical methods for neural data and the neural mechanisms underlying statistical inference in the brain. I develop Bayesian methods for high-dimensional neural datasets, with an emphasis on point process regression, dimensionality reduction, feature selection, state space models, active learning, information theory, and nonparametric models. Current problems of interest include the "neural code" for decision-making in parietal cortex, multi-neuron spike train codes, and models of intracellular responses in single neurons. My lab also conducts simple experiments investigating the optimality of statistical inference by human observers in perceptual tasks.

RavikumarPradeep Ravikumar

Assistant Professor
Department of Statistics and Data Sciences (SDS)
Department of Computer Science (CS)

My main area of research is in statistical machine learning. The core problem here combines the statistical imperative of inferring reliable conclusions from limited observations or data with the computational imperative of doing so with limited computation. Of particular interest are modern settings where the dimensionality of data is high, and simultaneously achieving these twin objectives is difficult. My recent research has been on the foundations of such statistical machine learning, with particular emphasis on graphical models, high-dimensional statistical inference, and optimization.

sagerTom Sager

Professor
Department of Information, Risk, and Operations Management (IROM)

The major part of my research focuses on the development and application of econometric models to study the empirical behavior of insurance companies. The models involve simultaneously interacting manifest and latent variables interacting in autocorrelated nonlinear panel data structures. I have applied these to the prediction of insurer insolvency, the risks of mortgage-backed securities, the effects of the Affordable Care Act, the assessment of global systemic risk, consequences of regulation, and other issues. I seek to illuminate how agency theory, transactions cost economics, theories of limited and unlimited risk, and other ideas play out to explain insurers’ management of their enterprise risks.

scottJames G. Scott

Assistant Professor
Department of Statistics and Data Sciences (SDS)
Department of Information, Risk, and Operations Management (IROM)

I am a Bayesian statistician. I study problems in model selection and multiple testing; connections between machine learning and Bayesian shrinkage estimation; variable selection and high-dimensional inference in non-linear, non-Gaussian models; and structured models for covariance matrices. Lately I've been thinking about how to make Bayesian-inspired approaches for sparse-signal detection scalable to enormous data sets, where many traditional Bayesian tools simply won't work. Some of my recent collaborations outside statistics include applied work in linguistics, neuroscience, clinical bio-informatics, and political science.

 

shivelyTom Shively

Professor
Department of Information, Risk, and Operations Management (IROM)

My research focuses on nonparametric function estimation and its application in energy economics, finance, marketing and transportation science. Current problems of interest include estimating univariate functions nonparametrically subject to shape constraints such as monotonicity and convexity, developing tests for monotonicity, and extending these results to multivariate functions using Bayesian additive regression trees. I am also interested in MCMC-based computational methods that allow for nonparametric function estimation in a wide range of non-Gaussian models including generalized mixed models, hazard function models and models for extreme values. My recent applied research focuses on modeling electricity prices in the deregulated Australian market using multivariate nonparametric methods.

 

walkerStephen Walker

Professor
Department of Statistics and Data Sciences (SDS)
Department of Mathematics

The main focus of my research is on Bayesian parametric and nonparametric methods. I have worked on applications, methodology,  theory, implementation via MCMC, and foundational issues. My main areas of applications include medical statistics and financial data.Recent work on Bayesian nonparametrics includes constructing time series and regression models.Recent work also includes working with Bayesian models under misspecification and using loss functions as an alternative to probability models within a learning process akin to Bayesian updating.

http://www.ma.utexas.edu/users/s.g.walker/

 

williamsonSinead Williamson

Assistant Professor
Department of Statistics and Data Sciences (SDS)
Department of Information, Risk, and Operations Management (IROM)

My main research focus is the development of nonparametric Bayesian methods for machine learning applications. In particular, I am interested in constructing distributions over correlated measures and structures, in order to model correlated data sets or data with spatio-temporal dependence. Examples include models for documents whose topical composition varies through time, and models for temporally evolving social networks. A key research goal is the development of efficient inference algorithms for such models, and I am currently investigating methods that allow us to apply Bayesian nonparametric techniques to large datasets.

 

zhouMingyuan Zhou

Assistant Professor
Department of Information, Risk, and Operations Management (IROM)

My research lies at the intersection of Bayesian statistics and machine learning. I am interested in developing statistical theory and methods, hierarchical models, and efficient Bayesian inference for big data. I am currently focused on the development of nonparametric Bayesian priors for both count & mixture modeling and dictionary learning. I am building the negative binomial process family to introduce new exchangeable random partitions and novel clustering algorithms.