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Vaishnavi Gujjula

Assistant Professor

vaishnavi.gujjula@iiitb.ac.in

Education : Ph.D. (IIT Madras)

Vaishnavi Gujjula holds a Ph.D. from the Indian Institute of Technology Madras (Advisor: Dr. Sivaram Ambikasaran, 2023). Her Ph.D. thesis focused on developing fast algorithms that are based on principles from scientific computing and numerical linear algebra. These fast algorithms have applications in solving partial differential equations (PDEs) and developing scalable machine learning algorithms. After receiving her Ph.D., she worked as a postdoctoral fellow at the Indian Institute of Science from 2023 to 2024. Her work has been published in various reputed international journals (SISC, JCP, CiCP, and Numerical Algorithms) and conferences (SIAM AN, ILAS, CIKM, IEEE APS). 

 

Before joining IIITB, she worked as an Assistant Professor at PES University (Bangalore, 2024-2025). Even earlier, she had worked for Bharat Electronics Limited (BEL, Bangalore, 2014-2016). 

 

Her research interests include computational mathematics, fast algorithms, numerical linear algebra, and scalable machine learning. Currently, she is particularly interested in scientific machine learning and scalable machine learning algorithms.

Computational Mathematics, Fast Algorithms, Numerical Linear Algebra, Scalable Machine Learning Algorithms, Scientific Machine Learning

Journal Publications:

 

  1. Vaishnavi Gujjula and Sivaram Ambikasaran, Algebraic Inverse Fast Multipole Method: A fast direct solver that is better than HODLR based fast direct solver, Journal of Computational Physics, 497 (2024): 112627. https://doi.org/10.1016/j.jcp.2023.112627
  2. Vaishnavi Gujjula and Sivaram Ambikasaran, A new Directional Algebraic Fast Multipole Method based iterative solver for the Lippmann-Schwinger equation accelerated with HODLR precondition, Communications in Computational Physics 2022, Volume 32, Issue 4. https://global-sci.com/doi:10.4208/cicp.OA-2022-0103
  3. Kandappan, V. A., Vaishnavi Gujjula, and Sivaram Ambikasaran, HODLR2D: A new class of hierarchical matrices, SIAM Journal on Scientific Computing, 45.5 (2023): A2382-A2408. https://doi.org/10.1137/22M1491253
  4. Kandappan, V. A.*, Vaishnavi Gujjula*, and Sivaram Ambikasaran, HODLR3D: Hierarchical matrices for N-body problems in three dimensions, (* Equal Contribution), Numerical Algorithms (2024). https://doi.org/10.1007/s11075-024-01765-4

 

Conference Proceedings:

 

  1. Naidu, K. V. M., Praveen Gupta, Vaishnavi Gujjula, Network Aware Forecasting for eCommerce Supply Planning, Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pages 1532–1541, October 2022, Atlanta, GA, USA. https://doi.org/10.1145/3511808.3557408
  2. Vaishnavi Gujjula and Dipanjan Gope, Algebraic Inverse Fast Multipole Method based Fast Direct Solver for Capacitance Extraction, 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), pages 507-508, July 2024, Florence, Italy. 10.1109/AP-S/INC-USNC-URSI52054.2024.10687298

 

Preprints:

 

  1. Vaishnavi Gujjula and Sivaram Ambikasaran, A new Nested Cross Approximation. arXiv e-prints, 2022, https://doi.org/10.48550/arXiv.2203.14832