Aditya Bhaskara

Aditya Bhaskara is an Associate Professor in the School of Computing at the University of Utah, where he is part of the Theory Group and the Utah Center for Data Science. His office is located in MEB 3470.

Education:

• Ph.D. in Computer Science, Princeton University (2012)
• B. Tech in Computer Science and Engineering, IIT Bombay, India
• Post-doctoral researcher, Google NYC (2013-2015)
• Post-doctoral researcher, EPFL (2012-2013)

Dr. Bhaskara's research spans theoretical computer science and machine learning. He has a strong focus on algorithm design, particularly approximation and online algorithms. On the machine learning side, he investigates robustness of learning models and domain shifts from a theoretical perspective. His work often blends theory and ML, exploring how to leverage ML-based predictions in classical algorithm design and other beyond worst-case models. His research has significant applications in data streaming, dimensionality reduction, and graph analysis.

His recent publications demonstrate a clear trend toward bridging theoretical computer science with practical machine learning applications. Many papers focus on spectral algorithms, robustness in network models, and optimization techniques for large-scale data. There is also significant work in wireless communications and spectrum management, showing how his theoretical work translates to real-world problems in telecommunications and data science.

Scientific Awards:

• NSF CAREER award
• AF Small grant
• Grants from NRDZ and FMiTF programs
• Google Faculty Research Award

Dr. Bhaskara actively advises students with strong mathematical backgrounds interested in theoretical computer science and machine learning. He has received significant research funding from the National Science Foundation and Google. He is co-organizing the Data Science Lecture Series at the University of Utah and has served on prestigious program committees including SODA 2024, STOC 2023, ICALP 2023, and ITCS 2022. His teaching portfolio includes advanced courses on algorithms, machine learning theory, and probability.

As part of the Theory Group and the Utah Center for Data Science, Dr. Bhaskara collaborates with researchers across disciplines to advance theoretical foundations of computing and their practical applications in data science. His work contributes to both the theoretical understanding of algorithms and their real-world implementation in various domains including wireless networks, data analysis, and machine learning systems.