"Signal processing, representation, and communication in the neural systems"
I am interested in the way we perceive the world. For example, we see colors. What is this "seeing color" precisely, in terms of physical variables and their transformations? How is this computation realized in our brain, using a population of neurons that are individually unreliable and simple? Why do we process sensory input in a specific manner and what are the underlying principles of this computation?
My approach is to derive theoretical models that solve tasks of the neural systems and to compare these models to experimental data. I have been working in the interdisciplinary field that includes neuroscience, signal processing, information theory, and communication theory. One important aspect of my research is to work with experimental data, both collecting data myself and collaborating with experts of experiments. I am also involved in methodologies for the experimental test of theories.
The feat of such an approach is its generalization. It not only serves to understand the way one perceives, but also understand the individual variation of perception caused by different genetic and environmental background. The same framework can be applied to the other animals as well as to designing engineering systems. Given the basic settings are valid, these theories are applicable to any computational systems in any modalities.
Current advisers and collaborators
- Eero Simoncelli (adviser), New York University
- Liam Paninski (co-adviser), Columbia University
- EJ Chichilnisky, Salk institute
- Jeff Gauthier, Salk institute
- Greg Field, Salk institute
- Jon Shlens, New York University/Salk institute
- Mike Lewicki (ex-adviser), Carnegie Mellon University