Photo of Bollapragada, Raghavendra

Raghavendra Bollapragada

Assistant Professor

Office: ETC 5.118

Raghu Bollapragada is an assistant professor in the Operations Research and Industrial Engineering graduate program at the University of Texas at Austin (UT). Before joining UT, he was a postdoctoral researcher in the Mathematics and Computer Science Division at Argonne National Laboratory. He received both PhD and MS degrees in Industrial Engineering and Management Sciences from Northwestern University. During his graduate study, he was a visiting researcher at INRIA, Paris. His current research interests are in nonlinear optimization and its applications in machine learning.  He has received the IEMS Nemhauser Dissertation Award for best dissertation, the IEMS Arthur P. Hurter Award for outstanding academic excellence, the McCormick terminal year fellowship for outstanding terminal-year PhD candidate, and the Walter P. Murphy Fellowship at Northwestern University.


  1. Berahas, A. S., Bollapragada, R., & Nocedal, J. An Investigation of Newton-Sketch and Subsampled Newton Methods. Optimization Methods and Software, Pages: 1 - 20, 2020.
  2. Bollapragada, R., Byrd, R., & Nocedal, J. Adaptive Sampling Strategies for Stochastic Optimization. SIAM Journal on Optimization, 28(4): 3312 - 3343, 2019.
  3. Bollapragada, R., Byrd, R., & Nocedal, J. Exact and Inexact Subsampled Newton Methods for Optimization. IMA Journal of Numerical Analysis, 39(2): 545 - 578, 2019.
  4. Berahas, A. S., Bollapragada, R., Keskar, N. S., & Wei, E. Balancing Communication and Computation in Distributed Optimization. IEEE Transactions on Automatic Control, 64(8): 3141 - 3155, 2019.
  5. Bollapragada, R., Scieur, D., & D'Aspremont, A. Nonlinear Acceleration of Primal-Dual Algorithms. In proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Okinawa, Japan, PMLR 89: 739 - 747, 2019.
  6. Bollapragada, R., Mudigere, D., Nocedal, J., Shi, H.J.M., & Tang, P.T.P. A Progressive Batching L-BFGS Method for Machine Learning. In proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR 80, 2018.
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