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

Affiliated Members

François Baccelli

Departements of Mathematics and Electrical Engineering
François Baccelli is Simons Math+X Chair in Mathematics and ECE at UT Austin.  His research directions are at the interface between Applied Mathematics (probability theory, stochastic geometry, dynamical systems) and Communications (network science, information theory, wireless networks). He is co-author of research monographs on point processes and queues (with P. Brémaud); max plus algebras and network dynamics (with G. Cohen, G. Olsder and J.P. Quadrat); stationary queuing networks (with P. Brémaud); stochastic geometry and wireless networks (with B. Blaszczyszyn).
Lab website

Risto Miikkulainen

Department of Computer Science
The goal of Risto's lab is to understand how cognitive abilities, such as sentence and story processing, lexicon, episodic memory, pattern and object recognition, and sequential decision making, emerge through evolution and learning.  The research involves developing new methods for self-organization and evolution of neural networks, as well as verifying them experimentally on human subjects, often in collaboration with experimentalists and medical professionals.  Examples of current work include understanding and inferring the semantics of words and sentences in fMRI images, impaired story telling in schizophrenia, rehabilitation in bilingual aphasia, and evolution of communication in simulated agents.

Alex Huk

Department of Neuroscience and Department of Psychology
Alex Huk's research focuses on visual motion, using it as a model system for investigating how the brain integrates information over space and time. His lab employs a variety of methods, including single-unit and multi-unit electrophysiology, causal perturbations of neural activity, psychophysics, and computational modeling. Recent work has focused on applications of generalized linear models (GLMs) and other single-trial amenable analytic frameworks to dissect the multitude of sensory, cognitive, and motor factors that drive many of the brain areas often studied in primates. Ongoing projects seek to extend applications of these tools to large-scale neurophysiological recordings, as well as more mechanistic studies of individual neurons and small circuits.
Lab website

Dan Johnston

Department of Neuroscience
Research in my laboratory is primarily directed towards understanding the cellular and molecular mechanisms of synaptic integration and long-term plasticity of neurons in the medial temporal lobe. We have focused our attention on the hippocampus and prefrontal cortex, areas of the brain that play important roles in learning, memory and decision-making. Our research uses quantitative electrophysiological, optical-imaging, and computer-modeling techniques.  Most of our projects involve trying to understand how dendritic ion channels, and in particular dendritic channelopathies, impact neuronal and network computations in normal and diseased brain.
Lab website

Kristen Harris

Department of Neuroscience and Center for Learning and Memory
Kristen Harris' laboratory studies structural synaptic plasticity in the developing and mature nervous system. Her group has been among the first to develop computer-assisted approaches to analyze synapses in three dimensions through serial section electron microscopy (3DEM) under a variety of experimental and natural conditions. These techniques have led to new understanding of synaptic structure under normal conditions as well as in response to experimental conditions such as long-term potentiation, a cellular mechanism of learning and memory. The body of work includes novel information about how subcellular components are redistributed specifically to those synapses that are undergoing plasticity during learning and memory, brain development, and pathological conditions including epilepsy. Theoretical and computational methods include computational vision for 3D EM reconstruction, high-dimensional spline methods, and molecular simulations of neurotransmitter signaling across the synaptic cleft.
Lab website

Laura Colgin

Department of Neuroscience
Laura Colgin is an Associate Professor in the Department of Neuroscience at the University of Texas at Austin. She received her PhD from the Institute for Mathematical Behavioral Sciences at the University of California at Irvine, and she completed her postdoctoral training in the laboratory of Nobel Laureates Edvard and May-Britt Moser. Her research uses state-of-the-art multisite recording and multivariate analysis techniques to address several key questions in systems neuroscience, including how the hippocampus stores and retrieves memories and how neuronal computations in the entorhinal-hippocampal network create the spatial component of these memories.
Lab website

Mike Mauk

Department of Neuroscience
We study information processing and learning in the cerebellum.  Our main experimental approach involves the use of eyelid conditioning as a way to control cerebellar inputs and monitor cerebellar output in vivo.  Through behavioral analysis, in vivo recordings and other manipulations such as stimulation and inactivation we try to understand what the cerebellum computes and the mechanisms that implement these computations.  We augment these studies with computational approaches that include large-scale computer simulations and mathematical models.  The large-scale simulations have been under development for over 25 years.  They involve building conductance-based spiking representations of each cerebellar cell type, developing algorithms to interconnect these neurons in ways that represent cerebellar synaptic organization, and testing them using inputs derived from our empirical studies.  Current versions involve over one million neurons implemented on GPU-based workstations.  These simulations, along with simpler mathematical models when useful, allow us to generate new, empirically testable predictions, to understand our data better and to determine the computational principles that make up cerebellar function.  Big questions include how inputs are transformed to improve learning and to implement stimulus-temporal coding required for the well-timed learning the cerebellum mediates.  We are also interested in the role of feedback in neural system function and in neural/system adaptations that make learning more efficient and that improve performance in the face of noisy inputs.
Lab website

Nicholas Priebe

Department of Neuroscience
Nicholas Priebe received his Ph.D. in Physiology from the University of California, San Francisco in 2001 after studying adaptation in motion-selective neurons with Stephen Lisberger. Dr. Priebe was a postdoctoral fellow with David Ferster at Northwestern University, investigating the mechanisms underlying neronal responses in primary vusual cortex. The massive expansion of cerebral cortex is a hallmark of the human brain. We know that the cortex plays an essential role in our perceptions and actions. Sensory inputs from the periphery are transformed in the cortex, allowing us to generate appropriate motor outputs. Dr. Priebe's lab studies the cortical circuitry and the computations that underlie such transformations, using vision as a model system. In visual cortex, neuronal circuitry performs the computations that extract motion, orientation and depth information about the visual environment from subcortical inputs. For example, primary visual cortex (V1) is the cortical location in which information from the two eyes is first integrated, ultimately allowing us to perceive depth in our visual field. By understanding the circuitry that underlies these kinds of computations, we gain insight into similar computations that occur throughout cortex.
Lab website


Postdoctoral Fellow
Ingmar Kanitscheider

Center for Theoretical and Computational Neuroscience
After obtaining my PhD in theoretical physics at the University of Amsterdam I was a postdoc in the lab of Alexandre Pouget, first at the University of Rochester and later at the University of Geneva in Switzerland. I joined UT Austin in August 2014. My work deals with a broad range of issues around probabilistic computation and representation in neurons, the impact of noise correlations on information, and probabilistic neural algorithms for navigation and map-building.

Jens-Oliver Muthmann
I studied physics at the University of Freiburg and did my diploma work on doubly stochastic point processes under guidance of Stefan Rotter. Afterwards, I joined the Erasmus Mundus EuroSPIN programme for a joint PhD between the groups of Upinder Bhalla (NCBS, Bangalore) and Matthias Hennig (University of Edinburgh). My research there focused on spike detection in recordings with high density microelectrode arrays and network dynamics in dissociated cultures. I joined the group of Alex Huk in 2017. I am currently interested in neural representations of visual stimuli in the dorsal stream during natural tracking behavior. In particular, I aim to study the representation of information that arrives with different temporal delays, the response to saccadic eye movements and to what extent stimuli are predicted by the brain.

Manyi Yim

Center for Theoretical and Computational Neuroscience
I joined the Center for Theoretical and Computational Neuroscience at UT Austin as a postdoctoral research fellow in 2017, working with Ila Fiete and Thibaud Taillefumier on theoretical approaches to understand how the brain computes. My research focus is to study the functions of brain circuits as well as neural correlates of cognition based on experimental data, and develop models and theories to understand the underlying mechanisms. I graduated from the Chinese University of Hong Kong with a BSc degree in Physics and a minor in Mathematics, and a MPhil degree in Physics. Afterwards, I did my PhD in Computational Neuroscience in Ad Aertsen’s group in the Bernstein Center Freiburg, Germany. Prior to joining UT Austin, I was an independent postdoc at University of Hong Kong, and then a postdoc with Xiao-Jing Wang and Xinying Cai at NYU Shanghai, working on the neural mechanisms underlying value-based decision making.

Rishidev Chaudhuri

Center for Theoretical and Computational Neuroscience
My research interests lie at the intersection of neuroscience and applied mathematics: building theoretical frameworks to understand computation in the brain; developing statistical tools to analyze experimental data; and modeling the dynamics of distributed neural systems. At the moment I’m investigating representations of space and context in the entorhinal cortex and hippocampus, the computational properties of dynamical systems inspired by error-correcting codes, and the use of random networks for modeling cortical dynamics, with a particular focus on data from electrocorticography (ECoG).