Core Members

Robbe Goris

Departments of Neuroscience and Psychology
Robbe Goris’ research seeks to uncover the neural basis of our visual capabilities. He uses behavioral experiments, computational theory, and monkey electrophysiology to study representation and computation in the primate visual system. Current projects in his lab are focused on the neural representation of sensory uncertainty, and on the relation between natural image statistics and cascaded computation in the visual cortex. Robbe received his Ph.D. in 2009 from KU Leuven (advisors: Johan Wagemans and Felix Wichmann), went on to do a post-doc at NYU (advisors: Tony Movshon and Eero Simoncelli), and joined UT Austin as assistant professor in fall 2016.

Ila Fiete

Department of Neuroscience
Ila Fiete is an Associate Professor in the Department of Neuroscience and the founding director of the Center for Theoretical and Computational Neuroscience at UT Austin. Fiete uses computational and theoretical approaches to understand the nature of distributed coding, error correction, and dynamical mechanisms that underlie representation and computation in the brain. A recent focus is on questions at the nexus of information and dynamics in neural systems, to understand how coding and statistics fundamentally constrain dynamics, and vice-versa. Ila Fiete obtained her Ph.D. at Harvard under the guidance of Sebastian Seung at MIT. Her postdoctoral work was at the Kavli Institute for Theoretical Physics at Santa Barbara, and at Caltech, where she was a Broad Fellow. Ila Fiete is a Howard Hughes Medical Institute Faculty Scholar, a fellow in the Center for Learning and Memory and Center for Perceptual Systems at the University of Texas at Austin, and a Simons Investigator as part of the SCGB.
Lab website

Thibaud Taillefumier

Departments of Neuroscience and Mathematics
Originally trained in Mathematical Physics, Thibaud Taillefumier completed his PhD in Biophysics under the supervision of Professor Marcelo Magnasco at The Rockefeller University. There, he developed novel analytical and computational techniques to characterize different modalities of neural coding and acquired experimental experience by performing electrophysiological recordings. As an Associate Research Scholar at Princeton, he expanded his work on neural assemblies within the framework of stochastic dynamics and non-equilibrium thermodynamics with Professor Curtis G. Callan, Jr. In parallel, he studied bacterial communities from the perspective of information and optimization theory with Professor Ned S. Wingreen. Thibaud Taillefumier is now an assistant professor jointly appointed by the Departments of Mathematics and Neuroscience at UT Austin.
Lab website

Alex Huth

Departments of Neuroscience and Computer Science
Alex Huth's research is focused on how the many different areas in the human brain work together to perform complex tasks such as understanding natural language. Alex uses and develops computational methods in Machine Learning and Bayesian Statistics, and obtain fMRI measures of brain responses from subjects while they do real-life tasks, such as listening to a story, to better understand how the brain functions. Alex earned his PhD in Dr. Jack Gallant's laboratory through the Helen Wills Neuroscience Institute at UC Berkeley. Before that, Alex earned both his bachelor's and master's degrees in computation and neural systems (CNS) at Caltech, where he worked with Dr. Christof Koch and Dr. Melissa Saenz. He received the Burroughs Wellcome Career Award in 2016.
Lab website

Ngoc Mai Tran

Department of Mathematics, UT Austin
Ngoc's interests lie in probabilistic and combinatorial questions arising from tropical geometry and neuroscience. Some of her recent works are on decoding grid cells, commuting tropical matrices, and zeros of random tropical polynomials. After a stint as a W-2 Professor at the University of Bonn, Germany 2015-2017, Ngoc joins as an Assistant Professor in the Department of Mathematics of UT Austin from the summer of 2017.
Lab website

Bill Geisler

Center for Perceptual Systems and Department of Psychology
Geisler’s primary research interests are in vision, computational vision, and visual neuroscience. His research combines behavioral studies, neurophysiological studies, studies of natural stimuli, and mathematical analysis.   Current research is directed at how to perform perceptual tasks optimally (the “theory of ideal observers”), on the relationship between the statistical properties of natural stimuli and the performance of the visual system, on the properties and theory of eye movements in natural tasks, and on the relationship between visual performance and the neurophysiology of the visual system.
Lab website

David Soloveichik

Electrical and Computer Engineering
David's main area of interest is "molecular programming": designing and building molecular systems in which computing and decision-making is carried out by the chemical processes themselves. In particular, he is studying underlying theoretical connections between distributed computing and molecular information processing. David is also interested in understanding how neural networks can execute distributed computing algorithms. Prior to joining Texas ECE, Dr. Soloveichik was a Fellow at the Center for Systems and Synthetic Biology at the University of California, San Francisco. He received his undergraduate and Masters degree from Harvard University in Computer Science. He completed his PhD degree in Computation and Neural Systems at the California Institute of Technology.
Lab website

Dana Ballard

Department of Computer Sciences
Dana's main research interest is in computational theories of the brain with emphasis on human vision and motor control. He is the author of two books at the intersection of compuational neuroscience and artifical intelligence,  Brain Computation as Hierarchical Abstraction and Computer Vision. His current research focuses on eye movements and planning during naturalistic tasks such as driving and making a peanut butter and jelly sandwich. He has long been a proponent of neurons performing predictive coding, explaining extra-classical receptive field properties in these terms. His current focus is modeling multiplexing of several neural processes with gamma frequency spike latencies.  
Lab website