**Vwani Roychowdhury**

## Mining the connections between** physics, biology, engineering, and society **

## using** computational and information science **

## to** shape our future**

From the way we **prevent serial killers**, to the **size of our cell phones**, to how we **care for our children**, the *most exciting advances* in our world emerge from the dynamic union of diverse bodies of knowledge, as captured through a common and interacting set of computational principles. These advances are both described by and enabled through the work I do to create interdisciplinary models of our reality.

My work has always tapped the **energy generated by the intersection of diverse fields**, both describing their synergy and catalyzing their transformation.

Indeed, in my view,** every discipline is an information processing and computing system, driven by a succinct set of universal laws**.

Hence, if one uses mathematical models to describe the foundations of different fields, the models can refine and inform one another. As models cultivate understanding of each discipline, they also create the ability to direct the discipline’s growth.

### Search my site (including all published papers) here, or click below to explore by subject.

# Fields:

My group has initiated a new area of study: How to use publicly available trace data (obtained via search engines and the Internet) about human behavior and perception, to discover **stochastic models of propagation of information, fame and sentiments in society. ** It uses analytical tools from statistical physics, Bayesian statistics, and applied mathematics.

As an alternative to the classical computing paradigms, I got into the field of **Quantum Computing and Information Processing** in its infancy. Along with my colleague, Prof. Eli Yablonovitch we received multi-million dollar grants from DARPA and the ARO and formed an internationally recognized quantum-computing group at UCLA.

We looked at a number of fundamental issues in this field, including the **capacity of Neural Networks to both learn and compute,** and **online learning** algorithms using stochastic gradient algorithms. For example, **we studied the role of depth, which is now a critical parameter in Deep Learning.** We showed how depth plays a critical role in determining the size of a network, and also in ensuring the emergence of certain patterns.

In my continuing quest to find alternative models of computation and **biological and nature inspired computing,** I started exploring how robust and highly-adaptive emergent structures and functionalities appear in self-organized systems, and how to build engineering systems based on such principles. This led to pioneering work on **modeling organic structures **and the processes that led to the development of** the World Wide Web (WWW), Peer-to- Peer (P2P) networks, **and other emergent systems such as **social networks, **and** online auctions.**