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.

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.

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.

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.

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.

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.

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.