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Vijay Balasubramanian

investigates the principles underlying computation and communication in the brain.  He considers how large-scale functions and behaviors emerge from the collective activity of very large numbers of neurons of many functional types interacting in complex networks.  He uses theoretical and computational methods applied to data gathered from the labs of collaborators. His work on the early visual system has established that many aspects of the structural and functional organization of the retina can be understood as adaptations to the statistical structure of natural scenes subject to biophysical constraints.  His early work on statistical inference examined tradeoffs between the complexity and accuracy of models, and he now applies these ideas to both machine and animal learning. His current work in theoretical neuroscience addresses diverse functions of the brain including vision, olfaction, audition, spatial cognition, motor control, decision making and learning.   He is also working on immune systems in animals and bacteria (CRISPR) seen as an adaptive dynamical system for sensing and remembering pathogenic environments.  [Personal Website

Philip Nelson

has worked extensively on the physics of artificial biomembranes, biopolymers such as DNA, and other “soft” condensed matter systems. He is currently thinking about a variety of problems in systems biology. He is especially interested in learning and adaptation by biological networks at many scales, ranging from signaling and regulatory networks in single cells to decision-making networks of neurons in the brain. [Personal Website ➤]

Kamesh Krishnamurthy

is a graduate student in the neuroscience graduate group interested in theoretical and computational approaches to problems in systems neuroscience.

Serena Bradde

received her PhD from SISSA, Italy and did postdocs at the Memorial Sloan Kettering Cancer Center in New York, the Institut Pasteur in Paris, and the Initiative for the Theoretical Sciences at the Graduate Center of the City University of New York.   She is currently an editor at the Physical Review and a long-term visitor in the group at Penn.   She has always been fascinated by the ability of living systems to adapt to changing environments.  Her research is presently focused on understanding the principles shaping the survival strategies of bacterial behavior and, more generally, of unicellular organisms.   She has developed theoretical models that describe how bacteria modulate their size according to nutrient availability, and, more recently, how microbes can acquire adaptive immunity against phage infection.

Tiberiu Tesileanu

received a PhD in theoretical physics at Princeton University and held a postdoctoral fellowship in the Systems Biology group at the Institute for Advanced Study in Princeton.  He is currently a postdoctoral fellow in the Initiative for Theoretical Sciences at the Graduate Center of the City University of New York, and a long-term visitor in the Computational Neuroscience Initiative at Penn.  He is  interested in problems in theoretical neuroscience ranging from learning to sensory representation and processing in the brain. He is currently working on motor learning in songbirds, texture perception in the visual cortex, and olfactory representations in the piriform cortex. Apart from neuroscience, he is also interested in modeling adaptive immunity, in particular the CRISPR system in bacteria.

Vijay Singh

received his PhD in theoretical physics from Emory University and is presently a Fellow in the Computational Neuroscience Initiative at Penn.    He is interested in understanding information processing in complex biological systems.   His current work focuses on collective signal processing in the olfactory and visual systems.

Gaia Tavoni

received her PhD in Statistical Physics from the École Normale Supérieure in Paris and she is currently a postdoctoral fellow in the Penn Computational Neuroscience Initiative. She develops theories and models to explain how the brain processes information to learn, memorize and predict.  Gaia is a Swartz Foundation Fellow in Theoretical Neuroscience.

Alex Keinath

is a graduate student in Psychology who studies spatial representation in the broader hippocampal formation. To do so, she employs a range of techniques including in vivo rodent electrophysiological recording, computational modeling, and behavioral experiments in both rodents and humans. Her recent projects focus on understanding how boundaries shape and anchor spatial representations and navigation behavior at multiple levels of explanation.

Jordan Lei

is a sophomore studying Computer Science and Finance in the Jerome Fisher Program in Management and Technology at the University of Pennsylvania​. He is interested in the application of Neural Networks and Deep Learning in modeling and classification. His current work is focused on applying Deep Neural Networks to infer the architecture of the visual pathway.

Chetan Parthiban

is a sophomore in the School of Engineering and Applied Sciences at Penn.  In the lab he is working on a project applying deep learning to the output of the retina.

David Kersen

is an MD/PhD student in Bioengineering who is working on network computation and neurogenesis in the olfactory bulb.                                                              

Clelia de Mulater

received her PhD in Statistical Physics from the Université de Paris-Saclay in Paris, France and then held a postdoctoral fellowship at the International Center for Theoretical Physics (ICTP) in Trieste, Italy.  She is a Simons Foundation Mathematical Modeling in Living Systems postdoctoral fellow who is working on a broad array of problems in biophysics and statistical physics.

Eugenio Piasini

received his PhD from University College London, after which he held a postdoctoral fellowship at the Italian Institute of Technology. He is currently a Fellow at the Computational Neuroscience Initiative at Penn. He is interested in a range of topics in computational neuroscience, including sensory processing and decision making.

Eve Armstrong

received her PhD in physics from the University of California in San Diego (UCSD).  After a postdoctoral fellowship in computational biophysics, also at UCSD, she came to Penn as a Fellow of the Computational Neuroscience Initiative.  She takes a dynamical systems approach to modeling neurons and neuronal networks.  In particular, she has modeled the songbird system, and has examined how sparse patterns of neuronal activity can give rise to a highly stereotyped and robust behavior.  In parallel, she uses optimization techniques to determine the experimental measurements that are - in principle - required to complete and test these models.  At Penn Eve is particularly interested in collaborating with experimentalists, and on a modeling approach geared toward optimizing experimental designs.

Alicia Zeng

is a Visiting Research Scientist in the lab.  She received her bachelor's degrees in Physics and in Philosophy from Vanderbilt University and is currently working on problems in theoretical neuroscience.