I am an Associate Professor in the School of Computing at the University of Utah. I am part of the Theory Group and the Utah Center for Data Science.
Contact: bhaskaraaditya AT gmail, or if you are at Utah, Rm 3470 MEB
(2022-23) I am on a sabbatical this year. In Fall 2022, I will be visiting the Simons Center in Berkeley, and Northwestern University as an Eschbach Scholar. In Spring 2023, I will be at TTI Chicago. I will be teaching a short course Learning Augmented Online Algorithms at Northwestern.
I am interested in theoretical computer science and machine learning. On the theory side, I am broadly interested in algorithm design, with a focus on approximation and online algorithms. On the ML side, I am interested in developing provably efficient algorithms, and in understanding the complexity of learning. I also work on topics at the intersection, e.g., leveraging ML-based predictions in classical algorithm design, and other beyond worst case models.
For more information, please see my research page or my CV.
For students. If you have a strong mathematical background and are interested in working with me, please send me email. Please include your Resume and information about your (relevant) past projects.
If you are looking for information about the data track, I am no longer the track director. Please contact Shandian Zhe (zhe@cs) if you have questions.
Selected program committees: STOC 2023, ICALP 2023, ALT 2023, ITCS 2022, ESA 2019, SODA 2019, WWW 2018, FOCS 2017.
Education and Background:
-- Post doctoral researcher, Google NYC (2013-2015)
-- Post doctoral researcher, EPFL (2012-2013)
-- Ph.D. in Computer Science, Princeton University (2012) [thesis]
-- B. Tech in Computer Science and Engineering, IIT Bombay, India.
I would like to thank the National Science Foundation (NSF) for supporting my research by an NSF CAREER award, an AF Small grant, and grants from the NRDZ and FMiTF programs. Thanks also to Google for a Faculty Research Award.
