Vijay Balasubramanian

Viijay Balasubramanium grew up in India and Indonesia and attended high school at the Jakarta International School. He has B.Sc. degrees in Physics and Computer Science from M.I.T. He received an M.S. in Computer Science from M.I.T. and a Ph.D. in Theoretical Physics from Princeton University. He was a Junior Fellow of the Harvard Society of Fellows and a Fellow-at-Large of the Santa Fe Institute.

Vijay is the Cathy and Marc Lasry Professor of Physics at the University of Pennsylvania where he received the Ira H. Abrams Memorial Award for Distinguished Teaching. He was a Penn Fellow (2012) and during the 2012-2013 academic year he was a Visiting Professor at the École Normale Supérieure (ENS), Paris supported by a fellowship from the Fondation Pierre Gilles de Gennes. He is currently a Visiting Professor at the Vrije Universiteit Brussel (VuB) in Belgium. He is a General Member of the Aspen Center for Physics and a Visiting Staff Member at the International Center for Theoretical Physics (ICTP) in Trieste, Italy.

Vijay is interested in how natural systems manipulate and process information, producing new forms of self-organization. As a theoretical physicist, he pursues questions about the fundamental nature of space and time.  He has worked on the the apparent loss of quantum information in the presence of black holes and the origin of entropy and thermodynamics in gravitating systems.  He has discussed how the familiar smooth structure of space-time can emerge as a long-distance effective description of more complex underlying physical constructs. He has shown how some dimensions of space can be regarded as emergent, arising from the quantum entanglement and information structure of an underlying lower-dimensional theory.

As a biophysicist, Vijay pursues these questions primarily in neuroscience. He thinks about the brain as a statistical computational device and seeks to uncover the principles that underlie the organization of neural circuits across scales from cells to the whole brain. He has worked on systems in the brain that support many different functions: vision, audition, olfaction, spatial cognition, motor control and decision making.  Applying lessons about adaptive molecular sensing from the olfactory system, he has also written about the functional organization of the adaptive immune system in vertebrates and bacteria (CRISPR).

Finally, he has written on problems in statistical inference and machine learning, and in particular on “Occam’s Razor”, i.e., the tradeoff between simplicity and accuracy in quantitative models. He is interested in this question because all scientific theories involve fitting models to data, and there is a fundamental tradeoff between the complexity of models and their ability to generalize correctly to new situations. This tradeoff influences how scientists infer models of the world, how machines learn the structure in data, and how living things from the scale of single cells to entire organisms with brains adapt to their environment over timescales from milliseconds to evolutionary time.

His publications are available through Google Scholar.