Vijaya Raghavendra Bollapragada

Raghu Bollapragada is an Assistant Professor in the Operations Research and Industrial Engineering program within the Mechanical Engineering department at The University of Texas at Austin. He is affiliated with the Machine Learning Laboratory, the Oden Institute for Computational Engineering and Sciences, and the Center for Dynamics and Controls of Material (an NSF MRSEC), focusing on algorithmic solutions for large-scale optimization challenges in machine learning and computational physics.

His educational background includes:

• PhD in Industrial Engineering and Management Sciences, Northwestern University
• MS in Industrial Engineering and Management Sciences, Northwestern University
• Visiting Researcher at INRIA, Paris during graduate studies

Bollapragada's research centers on nonlinear optimization, with expertise in constrained, stochastic, and distributed optimization frameworks. He develops algorithms that leverage modern computational infrastructure to solve complex problems in logistics, control systems, and machine learning, emphasizing scalability and hardware-aware efficiency. His work bridges theoretical convergence guarantees with practical implementation.

Recent publications reveal dominant trends in decentralized optimization (gradient tracking, network pruning) and stochastic methods (adaptive sampling, finite-difference estimation), with strong connections to machine learning applications. Key themes include communication-computation tradeoffs, non-convex optimization, and second-order acceleration techniques across 10+ high-impact publications (2024-2025).

His scientific awards include:

• IEMS Nemhauser Dissertation Award for best dissertation
• IEMS Arthur P. Hurter Award for outstanding academic excellence
• McCormick terminal year fellowship
• Walter P. Murphy Fellowship

Bollapragada mentors PhD students (including Cem Karamanli, recent thesis defense) and undergraduate researchers (Marissa Llamas, STARS Conference presenter), with research funded by NSF grant DMS-2324643, Argonne National Laboratory, and Lawrence Livermore National Laboratory. His SANDOPT and ZOAdaQN GitHub repositories provide open-source implementations of novel optimization algorithms.

He leads the Optimization, Inversion, Machine Learning, and Uncertainty for Complex Systems research group within UT Austin's Machine Learning Laboratory ecosystem, collaborating with the Center for Scientific Machine Learning on cross-disciplinary projects.