Vijay Bhattiprolu

Vijay Bhattiprolu serves as an Assistant Professor in the Department of Combinatorics & Optimization at the University of Waterloo, where his research bridges theoretical computer science and advanced mathematical disciplines. His work focuses on fundamental questions in computational complexity and optimization theory, with significant implications for algorithm design and analysis.

Dr. Bhattiprolu's academic journey includes a Ph.D. from Carnegie Mellon University (2019) under Venkat Guruswami, a postdoctoral fellowship at Princeton University and the Institute for Advanced Study (2019-2022) as part of the Simons Collaboration on Algorithms and Geometry, and undergraduate studies at the University of Illinois at Urbana-Champaign (2014) with research mentorship from Sariel Har-Peled and Mahesh Viswanathan.

• Ph.D. in Computer Science, Carnegie Mellon University (2014-2019)
• Postdoc, Princeton University / Institute for Advanced Study (2019-2022)
• B.Sc., University of Illinois at Urbana-Champaign (2011-2014)

His research program centers on approximation algorithms, hardness of approximation, and their deep connections to functional analysis and convex geometry. Key themes include polynomial maximization over convex sets, spectral theory, asymptotic convex geometry, and sum of squares methods, with applications to fundamental optimization problems. This interdisciplinary approach leverages mathematical tools to establish tight bounds on computational efficiency and develop novel algorithmic frameworks.

Dr. Bhattiprolu's publication record reveals consistent contributions to theoretical foundations of optimization, particularly in matrix norm approximation, quadratic form maximization, and geometric methods in algorithm design. His work frequently appears in premier venues including STOC, FOCS, and SICOMP, demonstrating sustained impact on understanding computational limits and advancing approximation techniques.

He currently mentors three graduate students: Yang Xiao (Ph.D., co-advised with Chaitanya Swamy), Jacob Skitsko (Ph.D., co-advised with Kostya Pashkovich), and Martin Liu (Masters). His teaching portfolio includes advanced courses in approximation algorithms (CO759) and core optimization theory (CO250, CO370), reflecting his commitment to training the next generation of theoretical computer scientists.