The Roychowdhury Group
Professor Vwani P. Roychowdhury
UCLA

computer science, Engineering, Physics,
Biology & Society

Computational & Information Science
to Shape our Future

The most exciting advances in today’s world emerge from the dynamic union of diverse knowledges as captured through a common & interacting set of computational principles. This affects quantum computer design, the size of our cellphones, and even the way in which we care for our children. These advances are both described by & enabled through our work to create interdiscinplinary models of our realities.

Our work taps into the energy generated wherever diverse fields intersect. It not only describes their synergy, it catalyzes their transformation.

Every discipline is a system that processes and computes information, and it is driven by a succint set of universal laws.

When Mathematical Models are used to describe the foundations of diverse fields, they both refine and inform each other. And they enable us to direct the very growth of the discipline.

Learn about Our fields of interrogation

Statistical & mathematical social science

The roychowdhury group has initiated a new area of study.  We  Explore The means by which to use publicly available trace data (from search engines & the internet) that is related to human behavior & perception.  In effect, we discover stochastic models of the propagation of information, fame & sentiments in society. We employ analytical tools from statistical physics, Bayesian statistics & applied mathematics.

Quantum computing

We entered the field of Quantum Computing & Information processing in its infancy. Along with our colleague, Prof eli yablonovitch from uc berkeley, we have received multi-million dollar grants from darpa & aro to form an internationally-recognized quantum computing group at ucla.

Machine learning & applications

To build on our pioneering work in artificial neural networks—or deep Learning—from the mid-90’s, we focus on learning from internet-scale data sets, also known as big data.

Complex Emergent systems / network science

In our continuing Quest to find alternative models of computation, as well as computing inspired by biology & the natural world, we explore how robust and highly adaptive emergent structures and functionalities appear in self-organized systems.We then discover how to build engineering systems based on such principles.This led to our pioneering work on modeling organic structures, and the processes that have led to the development of the World Wide Web (www), peer-to-peer (P2P) networks, and other emergent systems such as social networks & online auctions

Artificial neural networks

Here we look at the capacity of neural networks to both learn & compute, as well as online learning algorithms using stochastic gradient algorithms.For example, we study the role of depth, which is now a critical parameter in deep learning.We show how depth plays a crucial role in determining the size of a network, and ensuring the emergence of certain patterns.

Bio / Background

Born near Kolkata, india, Vwani studied at iit Kanpur (BS 1982), and went on to receive a Phd in Electrical engineering from stanford (Phd 1988). he then joined the faculty at purdue (1991-1996), where he became associate professor.  In 1996 he Joined Ucla’s Dept of Electrical engineering, where he is currently professor & Director of The Roychowdhury group in computational science.

contact:  vwani@ucla.edu