Interview with Professor Barna Saha

Can you tell us a bit about your childhood? Where did you grow up? What was it like there?

I grew up in a middle-class family in a suburban setting in India, where infrastructure and facilities tend to be city-centric. The educational opportunities were very limited with no platforms to compete in national and international level examinations. But there was fresh air to breath and green fields to play which are even rarer in India! I had a very happy childhood. My mother was a chemistry teacher in a local high school, and my father was a mathematics professor. My mother taught my sister and me everything from literature to science. Her dedication towards our education is the reason behind our success. My dad told us stories of great mathematicians and scientists. Today, my sister is a doctor and I am a professor.

Tell us a bit about your education background and your journey to Berkeley.

After completing my undergraduate program in Kolkata, India, I wanted to learn more and attend a PhD program in the US. I first joined the Indian Institute of Technology Kanpur to do my Masters, and was eventually admitted to the great computer science program at University of Maryland, College Park. I had a very fulfilling experience there with a very supportive PhD advisor. I then spent three years as a senior researcher at the AT&T Shannon Research Laboratory and then five years at the University of Massachusetts Amherst as an Associate Professor before getting an offer from Prof. Ken Goldberg to join UC Berkeley’s faculty — a very humbling experience indeed. I am incredibly grateful towards those who helped me get this far. It’s been a long journey but it’s far from completion!

How did you get interested in math and computers? How did you stay committed and focused on these areas?

I was good at math, and when you are good at something, it’s easy to develop interest. My interest in computers is a little different, though. I first saw a computer after graduating from high school. I wanted to study chemistry, but fate had other plans. My dad was critically injured in a road accident just before my high school graduation, and it was unclear if he could continue working. Studying engineering was the only way to ensure that I could get a job soon after undergrad and sustain my family economically. That led me to choose computer science. However, it didn’t take me long to fall in love with it! Fortunately, my family’s situation improved during my undergraduate studies, and I was able to continue studying.

Tell us about your research – what domain areas are you interested in? Where did you start and what are you working on now?

My research interest spans different facets of algorithm design. I specifically work on understanding how different complexity measures like time, space, and query complexity trade off with accuracy. Designing more efficient algorithms can help refute certain hypotheses related to solving NP-Hard Satisfiability Problems. This connection between a low-polynomial time problem and an NP-Hard problem is surprising, and at the core of fine-grained complexity. My recent works are in this area of fine-grained approximation algorithms, where near-optimal results can significantly improve running time.

You recently received the Presidential Early Career Award for Scientists and Engineers. Can you tell us a bit about the award and why you were nominated?

Established in 1996, the PECASE is the highest honor given to young researchers in the fields of science and engineering. I was nominated by the National Science Foundation (NSF) with the following citation:
“For pioneering a unified theory of fine-grained algorithm design leading to enhanced computation efficiency and practical solutions for a range of problems, and for outstanding dedication to mentoring and training a diverse and interdisciplinary scientific workforce.”

How do you think your research interests might change over the next 5-10 years?

This is an interesting and hard question to answer! I want to work on solving some long-standing open questions on fine-grained complexity but who knows how many years it will take! I’d also like to increase my involvement in the computational biology field, especially after experiencing this pandemic. Similarly, fairness in computation, and energy-efficient computations are two areas that you may see me working in near future.