Insights into an Award-Winning Career

An interview with the 2025 Charles Babbage Award Winner Srinivas Aluru

By IEEE Computer Society Team on

January 16, 2025

Headshot of Srinivas Aluru Be selective about the problems that you choose to work on. In fact, making the right choice of problems to work on is as important as what you end up contributing by solving those problems. I'm a proponent of what I call the lunchtime concept in parallel computing. When you're working in parallel computing, there's always a way to solve the problems on ordinary computers, and if you speed something that can be done in an hour down to one minute, nobody really cares. If it can process in the hour they got to lunch, there’s no need to speed it up. However, if you bring 60 days to one day, people will use it because they don't want to wait for two months for an answer, and if you take something that would take 60 years and bring it down to months or days, then people will absolutely love it because they don't have to wait their entire lives waiting for the answer. To make more impact, work on high-end problems with bigger data sets and more complicated analysis.If a problem can be solved on an ordinary computer in an hour or lunchtime, basically, don't bother working on it.

Evaluate an opportunity on its own merits, and don't worry that there aren't a lot of people looking at it. If you always fall for the comfort of working on something that is already well researched, there may be the danger that you're settling for derivative work. Identify an opportunity to be a free thinker. Not everything is going to turn out well, but unless you are willing to try new things, your work will be run of the mill.

You have had a tremendous impact on both biology and computer science and engineering. How do you view your role in these diverse, but interconnected, fields?

Even though the impact of my work is really felt in biology, much of my research constitutes foundational advances in parallel computing. In this field, there is a preference towards publishing in life sciences, because that's where your clients are, and if they read it and use it, you have wider dissemination. But I have always argued that it's very important to publish in computer science and engineering conferences and journals—I have served as the editor-in-chief of IEEE/ACM Transactions on Computational Biology and Bioinformatics until recently—so the computer science advances are shared very openly.

That's how others in computing can read and understand the technical aspects of the work, see what they would like to work on, and what additional contributions they can make. I have long argued that our community needs to know about the underlying algorithms on each of these contributions so that they don't end up reinventing the wheel, and they also know when they want to work on something.

Much of my work constitutes foundational advances in parallel computing, and they're fun and worth reading, even for people who are outside the field because the DNA sequences are modeled as strings. The interconnectedness of these sequences and the biological entities that interact with each other are modeled as graphs.

It opens up a lot of new fundamental problems dealing with strings and graphs and so forth. And these problems, they're rooted in biology. But they're genuine computer science problems in their own right. One could learn about the importance of new problems, formulate them, and solve them.

My group sometimes accidentally ended up solving long-standing theory problems that we would not have solved if we had been focused on them in the first place. We were working on applications, and in a few instances, the application context led us to interesting ways to solve the problems. So, it’s important to be curious and creative at all stages of your career.

Srinivas Aluru is Regents' Professor in the School of Computational Science and Engineering and the Senior Associate Dean in the College of Computing at Georgia Institute of Technology. From 2016 to 2024, he served as Executive Director of the Institute for Data Engineering and Science (IDEaS), a campuswide interdisciplinary research institute. Previously, he held faculty positions at Iowa State University (1999-2013), Indian Institution of Technology Bombay (2009-2014), New Mexico State University (1996-1999), and Syracuse University (1994-1996). He received his B. Tech degree in Computer Science from the Indian Institute of Technology Madras in 1989, and his M.S. and Ph.D. degrees in Computer Science from Iowa State University in 1991 and 1994, respectively.

Aluru is known for his pioneering work in parallel computational biology, spanning both fundamental algorithms and compelling applications. He led genome assembly efforts for maize, the first economically important crop sequenced in the United States, on the IBM Blue Gene/L supercomputer. This work led to the discovery of 350 novel genes, and the assembly algorithms were recognized with an IPDPS best paper award in 2006. His group was the first to develop parallel algorithms for inference and analysis of genome-scale networks, leveraging experimental data deposited in public repositories by researchers worldwide. During the early years of big data research, Aluru led an NSF-NIH project to comprehensively develop parallel string and graph algorithms that underpin modern genomics. This work won both research and software reproducibility awards at Supercomputing conferences. More recently, his group developed FastANI, a method for estimating pairwise distances between microbial genomes. The software is downloaded more than 100K times and is being used for species classification. To promote education and research training in the field, Aluru edited the first comprehensive handbook in Computational Molecular Biology in 2005. He was a past chair of ACM SIGBIO (2015-2021) and served as editor-in-chief of the IEEE/ACM Transactions on Computational Biology and Bioinformatics (2021-2024).