That's the pronunciation of my name in IPA.
I did most of my schooling in New Delhi at Mount St. Mary's. I spent four years at the Indian Institute of Technology, Kanpur getting a B.Tech in Computer Science, and then another five years at Stanford University getting my Ph.D with Rajeev Motwani and Jean-Claude Latombe.
I spent another seven years at AT&T Labs -- Research, and then made my way to Utah, where I've been since. If you are looking for my official CV, you're in the right place.
Suresh Venkatasubramanian is a professor at the University of Utah. His background is in algorithms and computational geometry, as well as data mining and machine learning. His current research interests lie in algorithmic fairness, and more generally the problem of understanding and explaining the results of black box decision procedures. Suresh was the John and Marva Warnock Assistant Professor at the U, and has received a CAREER award from the NSF for his work in the geometry of probability, as well as a test-of-time award at ICDE 2017 for his work in privacy. His research on algorithmic fairness has received press coverage across North America and Europe, including NPR’s Science Friday, NBC, and CNN, as well as in other media outlets. He is a member of the Computing Community Consortium Council of the CRA, a member of the board of the ACLU in Utah, and a member of New York City’s Failure to Appear Tool (FTA) Research Advisory Council.
My background is in theoretical computer science: the study of the intrinsic complexity of computations. Over the years, I’ve become more interested in problems of data analysis (which I like to think of as a special case of high dimensional geometry !). I’m also a sci-fi fan, and many years spent reading cyberpunk thrillers got me thinking about our machine-enhanced future. A few years ago, I started imagining what it would be like to live in a world where all my decisions were controlled (or nudged) by algorithms that learnt things about me, and it didn’t take long to realize that we’re barreling into a future driven by algorithms that aren’t that smart, and aren’t particularly fair.