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Program Director:

meyersLauren Meyers
Professor of Integrative Biology and Statistics & Data Sciences
Website: http://www.bio.utexas.edu/research/meyers/

Lauren Ancel Meyers received her B.A. degree in Mathematics and Philosophy from Harvard University in 1996 and her Ph.D. from the department of Biological Sciences at Stanford University in 2000. She joined the faculty at the University of Texas at Austin in 2003 where she was recently promoted to Full Professor and awarded a Donald D. Harrington Faculty Fellowship. She has also been an active member of the external faculty of the Santa Fe Institute since 2003 and now serves on its Scientific Advisory Board. Lauren has developed new mathematical methods for forecasting and optimizing the control of infectious diseases including meningitis, HIV, influenza, walking pneumonia, and SARS. Her research has been published in over 45 peer-reviewed publications and funded by research grants from National Institutes of Health, the National Science Foundation, and the James S. McDonnell Foundation. The Wall Street Journal, Newsweek, the BBC, and other news sources have highlighted Lauren's work; and a number of government agencies have sought her expertise, including the Centers for Disease Control and Prevention (CDC), the Biomedical Advanced Research and Development Authority (BARDA), and the US National Intelligence Council. In 2004, the MIT Technology Review named Lauren as one of the top 100 global innovators under age 35. 



dhillonInderjit Dhillon
Professor of Computer Science and Mathematics
Website: https://www.cs.utexas.edu/~inderjit/

Inderjit Dhillon is the Gottesman Family Centennial Professor of Computer Science and Mathematics at UT Austin, where he is also the Director of the ICES Center for Big Data Analytics. His main research interests are in big data, machine learning, network analysis, linear algebra and optimization. He received his B.Tech. degree from IIT Bombay, and Ph.D. from UC Berkeley. Inderjit has received several prestigious awards, including the ICES Distinguished Research Award, the SIAM Outstanding Paper Prize, the Moncrief Grand Challenge Award, the SIAM Linear Algebra Prize, the University Research Excellence Award, and the NSF Career Award. He has published over 140 journal and conference papers, and has served on the Editorial Board of the Journal of Machine Learning Research, the IEEE Transactions of Pattern Analysis and Machine Intelligence, Foundations and Trends in Machine Learning and the SIAM Journal for Matrix Analysis and Applications. Inderjit is an IEEE Fellow, a SIAM Fellow and an ACM Fellow.


Scott JamesJames Scott
Associate Professor of Statistics & Data Sciences and IROM
Website: http://jgscott.github.com

James Scott received a Ph.D in statistics from Duke University in 2009 after completing his master’s degree in mathematics from the University of Cambridge as a Marshall Scholar.   His research interests include statistical model selection, spatial statistics, statistical computing, and other topics in Bayesian statistics.  James is the winner of the 2016 Susie Bayarri Award for the top Bayesian statistician under 35, as well as the recipient of an NSF CAREER Award.  He has also done collaborative statistical work in infectious disease modeling, obstetrics, neuroscience, transportation research, astronomy, finance, political science, linguistics, genetics, and molecular biology.

 Faculty MENTORS:

bajaj imgChandrajit Bajaj,
Professor of Computer Science
Website: http://www.cs.utexas.edu/~bajaj/cvc/index.shtml

Chandrajit L. Bajaj earned his Ph.D. in computer science from Cornell University. He is director of the ICES Computational Visualization Center, professor of computer sciences and holds the Computational Applied Mathematics Chair in Visualization. He is also an affiliate faculty member of the departments of mathematics, electrical and computer engineering, biomedical engineering, the Center for Perceptual Systems, the Institute for Cellular and Molecular Biology, and the Center for Learning and Memory.

His research interests span the algorithmic and computational mathematics underpinnings of image processing, geometric modeling, computer graphics, visualization, structural biology and bioinformatics. He applies these algorithms to: (a) structure elucidation and reconstruction of spatially realistic models of molecules, organelles, cells, tissues, and organs, from electron microscopy, and bio-imaging, (b) fast high-dimensional search/scoring engines for identifying energetically favorable molecular binding conformations (e.g. virtual screening for anti-viral
drugs), and (c) integrated approaches to computational modeling, mathematical analysis and interrogative visualization of the dynamics of electrical signaling and oscillations (3–10 Hz) among neurons in the hippocampus (the central area of learning and memory in the human brain).   

Andrew Ellington
Professor of Biochemistry
Website: http://ellingtonlab.org/

The Ellington lab works on the directed evolution of molecules and organisms, attempting to mold new phenotypes that go beyond what is naturally available.  In this regard, there are a variety of Big Data projects that involve using the results of NextGen sequencing analyses to better understand what paths evolution can take (or be made to take).  For example, in the directed evolution of polymerases with novel functions, such as the ability to synthesize extremely long tracts of DNA, there is both a great deal of natural phylogenetic data and data from the results of previous directed evolution experiments that can be used to better craft evolutionary paths, ultimately leading to molecules with improved diagnostic and synthetic capabilities.  Such projects have an amusing 'meta' component, in that by improving the molecules that are used to gather biological Big Data in the first place (DNA polymerases involved in NextGen sequencing eperiments) you will essentially be giving Big Data the means to accelerate its rate of acquisition, especially via newer generation single molecule sequencing platforms.  In another project, we have collected enormous amounts of sequencing data on the directed evolution of organismal genomes with altered genetic codes, and are attempting to understand how an entire organism moves into a completely new chemical space and takes advantage of novel amino acids that expands their chemical capabilities.  As with experiments that focus on smaller segments of DNA (such as an individual polymerase gene, as above), the problem is to sieve, cull, and refine mutation data so that deleterious, neutral, and positive features of emerging evolutionary landscapes can be discerned.  This problem ratchets up in difficulty for an entire genome, requiring the insights of data scientists.

Georgiou headshot George Georgiou
Professor of Molecular Biosciences and Chemical and Biomedical Engineering
Website: https://sites.utexas.edu/georgiou/

George Georgiou holds the Laura Jennings Turner Chair in Engineering at the University of Texas at Austin where he serves on the faculties of Chemical Engineering, Biomedical Engineering, Molecular Biosciences, and the Institute for Cell and Molecular Biology. He received his B.Sc. degree from the University of Manchester, U.K. and his M.S. and Ph.D. degrees from Cornell. His group is working in molecular biotechnology with special emphasis on the engineering and preclinical development of therapeutic proteins, on antibody engineering and human B cell immunology. Professor Georgiou is a co-inventor of over 80 issued and pending US patents of which 24 have been licensed to 11 pharmaceutical or biotechnology companies. Professor Georgiou is a member of the National Academy of Engineering (NAE) and of the National Academy of Medicine (NAM) of the National Academy of Sciences as well as the American Academy of Arts and Sciences. He has been elected Fellow of the American Institute for Biological and Medical Engineers, the American Academy of Microbiology and the American Association for the Advancement of Science (AAAS).  Dr. Georgiou is the author of over 200 research publications and has edited 5 books. His honors include the Presidential Young Investigator Award from the National Science Foundation in 1987; the Dow Outstanding Young Faculty Award in 1988; the E. Bergman Prize from the U.S-Israel Science Foundation in 1995; the ACS Biochemical Technology Award in 2003, the AICHE Professional Progress Award for Outstanding Progress in Chemical Engineering, also in 2003 and the AICHE Food, Pharmaceutical & Bioengineering Award in 2005.  In 2008 he was named “one of the top 100 chemical engineers of the modern era” by AIChE and in 2014 he was named as one of the “Top 20 Translational Researchers” in the world by Nature Biotechnology.


HOfmannHHans Hofmann
Professor of Integrative Biology
Website: http://cichlid.biosci.utexas.edu/

Hans Hofmann is Professor of Integrative Biology at The University of Texas at Austin. His research interests include the neuromolecular basis of social behavior and its evolution, with a focus on genomics and bioinformatics. He received his Ph.D. in neurobiology from the University of Leipzig and the Max-Planck Institute in Seewiesen. As a postdoctoral fellow at Stanford University, he began taking advantage of the astonishing diversity and plasticity of cichlid fishes to study how the social environment regulates brain and behavior. While a Bauer Genome Fellow at Harvard University, he pioneered behavioral genomics in cichlid fishes to analyze and understand the molecular and neural basis of social behavior and its evolution. He developed many of the functional genomics resources for cichlids and co-led the cichlid genome consortium. He received the prestigious Alfred P. Sloan Foundation Fellowship (in Neuroscience) and was awarded the Frank A. Beach Early Career Award from the Society for Behavioral Neuroendocrinology. He has also been honored twice with the UT Austin College of Natural Sciences Teaching Award. He has served on the editorial boards of several journals and was an Editor for Behavioral Ecology for five years. Since 2012 he has been the Director of UT's Center for Computational Biology and Bioinformatics where he leads an innovative bioinformatics initiative across the College of Natural Sciences. In 2013, Hofmann was selected as new Co-Director of the Neural Systems & Behavior summer course at the Marine Biological Laboratory in Woods Hole (MA). Since 2016, Hofmann has been the inaugural Director of the Center for Biomedical Core Facilities at UT Austin.


Alex HukAlexander Huk
Associate Professor of Neuroscience and Psychology
Website: motion.cps.utexas.edu

Alex Huk is a Professor of Neuroscience and Director of the Imaging Research Center. He received his Ph.D. from Stanford University in 2001 and did his post-doctoral work at the University of Washington (Seattle) before joining the UT faculty in 2004. Dr. Huk's  primary research program focuses on how the brain processes movement and integrates this information over space and time to guide action in a dynamic world, using an array of techniques including large scale electrophysiological recordings, neuroimaging, and computational modeling.


KirkpatrickMarkMark Kirkpatrick
Professor of Integrative Biology
Website: http://www.sbs.utexas.edu/kirkpatrick_lab/k/home.html

Mark Kirkpatrick is the Painter Professor of Genetics at the University of Texas. He received a BA from Harvard (1978) and a PhD from the University of Washington (1983). His research uses models and statistics to study evolutionary genetics. Topics include evolution of chromosome rearrangements, sex determination, speciation, quantitative genetics, and species ranges. His lab's approaches use stochastic modeling, computer simulation, and a range of statistical frameworks (likelihood, Bayesian inference, ABC, etc.).  Dr. Kirkpatrick is a fellow of the AAAS and has received recognition including the Sewall Wright Award from the ASN. 


Mia Markey 2015 croppedMia Markey
Professor of Biomedical Engineering
Website: http://bmil.bme.utexas.edu

Dr. Mia K. Markey is a Professor of Biomedical Engineering and Engineering Foundation Endowed Faculty Fellow in Engineering at The University of Texas at Austin as well as Adjunct Professor of Imaging Physics at The University of Texas MD Anderson Cancer Center. Dr. Markey earned her Ph.D. in biomedical engineering (2002), along with a certificate in bioinformatics, from Duke University. The mission of Dr. Markey’s Biomedical Informatics Lab is to develop decision support systems for clinical decision making and scientific discovery. Dr. Markey has been recognized for excellence in research and teaching with awards from organizations such as the American Medical Informatics Association, the American Society for Engineering Education, the American Cancer Society, and the Society for Women’s Health Research. She is a Fellow of both the American Association for the Advancement of Science (AAAS) and American Institute for Medical and Biological Engineering (AIMBE), and is a Senior Member of both the IEEE and the SPIE. 


 Photo of Nancy MoranNancy Moran
Professor of Integrative Biology
Website: http://web.biosci.utexas.edu/moran/

Dr. Nancy Moran studies genome evolution, primarily in bacteria and insects. A focus is on the processes leading to genomic divergence, including mutation, horizontal gene transfer, genetic drift and natural selection. Projects in the lab include novel de novo sequencing and assembly of bacterial and insect genomes, metagenomics of bacterial communities, transcriptomic studies. comparative genomic studies, and a wide range of experiments on symbiotic bacteria that coevolve with their hosts.  


MuellerPeter Mueller
Professor of Statistics & Data Sciences and Mathematics
Website: https://www.ma.utexas.edu/users/pmueller/
Peter Mueller is interested in methods and applications of Bayesian inference. More specifically, he is 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 he is 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 his methodological interests.

Peter's undergraduate education is from Universität Wien and Technische Universität Wien, Austria. His Ph.D. is from Purdue University where he worked under Jim Berger on MCMC for constrained parameter problems. Peter spent several years at the Institute of Statistics and Dec Sciences (ISDS), Duke University, and at M.D. Anderson Biostatistics.  


PressWilliamWilliam Press
Professors of Computer Science and Integrative Biology
Website: http://numerical.recipes/whp/

William H. Press holds the Warren J. and Viola M. Raymer Chair in Computer Sciences and Integrative Biology at the University of Texas at Austin. At UT, his affiliations include membership in the Institute for Computational Engineering and Sciences and in the Institute for Cellular and Molecular Biology. Press is also a Senior Fellow (emeritus) at the Los Alamos National Laboratory. He was a member of President Obama's Council of Advisors on Science and Technology (PCAST) and a past president of the American Association for the Advancement of Science. Press has published more than 150 papers in areas of computational biology, theoretical astrophysics, cosmology, and computational algorithms. He is senior author of the Numerical Recipes textbooks on scientific computing, with more than 400,000 hardcover copies in print.  He was elected to the National Academy of Sciences in 1994.


preston aAlison Preston
Associate Professor of Neuroscience and Psychology
Website: http://clm.utexas.edu/preston/

Alison Preston is an Associate Professor of Psychology and Neuroscience, and a fellow of the Center for Learning & Memory. Using brain imaging combined with machine learning and computational methods, Dr. Preston’s work explores how we form new memories, how we remember past experiences, and how our memory for the past influences what we learn in the present. In particular, her work focuses on how interactions between the hippocampus and neocortex support the ability to acquire and retain new conceptual knowledge that extends beyond direct experience. She examines how brain development underlies improvements in memory and conceptual knowledge, which further support children and adolescents' ability to reason about the world.


sacksMichael Sacks
Professor of Biomedical Engineering
Website: https://www.ices.utexas.edu/people/1057/

Michael Sacks is professor of biomedical engineering and holder of the W. A. "Tex" Moncrief, Jr. Endowment in Simulation-Based Engineering and Sciences Chair No. 1. He is also director of the ICES Center for Cardiovascular Simulation-based Engineering. Sacks formerly held the John A. Swanson Chair in the Department of Bioengineering at the University of Pittsburgh. He earned his B.S. and M.S. in engineering mechanics from Michigan State University, and his Ph.D. in biomedical engineering (biomechanics) from The University of Texas Southwestern Medical Center at Dallas. 

In 2006, he was selected as one of the Scientific American top 50 scientists. In 2009, he won the Van C. Mow Medal from the American Society of Mechanical Engineers (ASME) Bioengineering Division and the Chancellor’s Distinguished Research Award at the University of Pittsburgh. He is a fellow of ASME and the American Institute for Medical & Biological Engineering, and an inaugural fellow of the Biomedical Engineering Society. He is currently editor of the “Journal of Biomechanical Engineering,” and serves on the editorial board for 27 other journals.


Scott JamesJames Scott
Associate Professor of Statistics & Data Sciences and IROM
Website: http://jgscott.github.com

James Scott received a Ph.D in statistics from Duke University in 2009 after completing his master’s degree in mathematics from the University of Cambridge as a Marshall Scholar.   His research interests include statistical model selection, spatial statistics, statistical computing, and other topics in Bayesian statistics.  James is the winner of the 2016 Susie Bayarri Award for the top Bayesian statistician under 35, as well as the recipient of an NSF CAREER Award.  He has also done collaborative statistical work in infectious disease modeling, obstetrics, neuroscience, transportation research, astronomy, finance, political science, linguistics, genetics, and molecular biology. 


walkerStephen Walker
Professor of Statistics & Data Sciences and Mathematics
Website: https://www.ma.utexas.edu/users/s.g.walker/

Stephen Walker is professor, Department of Mathematics, and Department of Statistics & Data Science, University of Texas at Austin, and was awarded the PhD in Statistics from Imperial College in 1995. His main research interest is Bayesian Nonparametric Methods and he has published over 200 peer reviewed articles. He has also been principal investigator on a number of UK (EPSRC) and US (NSF) grants. He is currently Associate Editor for 4 journals, including Annals of Statistics and has graduated 16 PhD students.


ClausAug2015 printClaus Wilke 
Professor of Integrative Biology
Website: http://wilkelab.org/

Claus Wilke received his PhD in Theoretical Physics from the University of Bochum in Germany in 1999. He was a postdoc in the Adami lab at Caltech from 2000 to 2005, where he received postdoctoral training in biological physics, evolutionary biology, and artificial life. Claus Wilke has authored or co-authored of over 100 scientific publications covering topics in computational biology, evolutionary biology, bioinformatics, population genetics, and statistics.

williamson sineadSinead Williamson
Assistant Professor of Statistics & Data Sciences and IROM
Website: http://sinead.github.io

Sinead Williamson's main research focus is the development of nonparametric Bayesian methods for machine learning applications. In particular, she is 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 she is currently investigating methods that allow us to apply Bayesian nonparametric techniques to large datasets.