I am currently Principal : AI and Intelligent Automation at SLK Software leading multiple AI platform initiatives. I work with a team of extremely passionate scientists and technologists who fundamentally believe in transformative capability technology can have. Prior to this I worked in an AI startup V-Labs, where I headed the high performance computing team.

I started my higher education with Bachelor in Engineering in Civil Engineering from the wonderful B.M.S College of Engineering. This was followed by direct PhD in Indian Institute of Science (IISc) under the supervision of Prof. Debraj Ghosh, my area of research was in uncertainty quantification, numerical analysis and high performance computing. Post my PhD I was a research associate in Numerical Analysis group of Department of Mathematics, The University of Manchester. Initially I worked with Prof. Jack Dongarra and later with Prof. Nick Higham developing numerical linear algebra algorithm and software.

Initially I created this website to share my academic publications, and had stopped maintaining it once I started my industry position. Going further I intend on updating the website on a regular basis.

Publications

15. Michael P. Connolly, Nicholas J. Higham, and Srikara Pranesh. "Randomized low rank matrix approximation: Rounding error analysis and a mixed precision algorithm." (2022). MIMS Pre-Print

14. Ahmad Abdelfattah, Hartwig Anzt, Erik G. Boman, Erin Carson, Terry Cojean, Jack Dongarra, Mark Gates, Thomas Grützmacher, Nicholas J. Higham, Sherry Li, Neil Lindquist, Yang Liu, Jennifer Loe, Piotr Luszczek, Pratik Nayak, Srikara Pranesh, Siva Rajamanickam, Tobias Ribizel, Barry Smith, Kasia Swirydowicz, Stephen Thomas, Stanimire Tomov, Yaohung M. Tsai, Ichitaro Yamazaki, Urike Meier Yang. "A Survey of Numerical Linear Algebra Methods Utilizing Mixed Precision Arithmetic." (2021), International Journal of High Performance Computing Applications, 35(4), 344-369.

13. Desmond J. Higham, Nicholas J. Higham, and Srikara Pranesh. "Random Matrices Generating Large Growth in LU Factorization with Pivoting." (2021), SIAM Journal On Matrix Analysis and Applications, 42(1), 185-201.

12. Massimiliano Fasi, Nicholas J. Higham, Mantas Mikaitis, and Srikara Pranesh. "Numerical Behavior of the NVIDIA Tensor Cores." (2021), PeerJ. Computer Science,7:e330.

11. Erin Carson, Nicholas J. Higham, Srikara Pranesh. "Three-Precision GMRES-Based Iterative Refinement for Least Squares Problems." (2020), SIAM Journal On Scientific Computing, 42(6), A4063–A4083. codes available here

10. Nicholas J. Higham, Srikara Pranesh. "Exploiting Lower Precision Arithmetic in Solving Symmetric Positive Definite Linear Systems and Least Sqaures Problems." (2021), SIAM Journal On Scientific Computing, 43(1), A258-A277 . codes available here

9. Pierre Blanchard, Nicholas J. Higham, Florent Lopez, Theo Mary, and Srikara Pranesh. "Mixed Precision Block Fused Multiply-Add: Error Analysis and Application to GPU Tensor Cores."(2020), SIAM Journal on Scientific Computing 42(3),C124-C141.

8. Srikara Pranesh. "Backward Error and Condition number of a generalized Sylvester equation, with application to the stochastic Galerkin method." (2019), Linear Algebra and Its Applications 594 (2020): 95-116. codes available here

7. Nicholas J. Higham, Srikara Pranesh. "Simulating Low Precision Floating-Point Arithmetic." (2019), SIAM Journal on Scientific Computing 41(5), C585–C602. codes available here

6. Nicholas J. Higham, Srikara Pranesh, and Mawussi Zounon. "Squeezing a Matrix into Half Precision, with an Application to Solving Linear Systems." (2019), SIAM Journal on Scientific Computing 41.4 (2019): A2536-A2551. codes available here

5. Azzam Haidar, Ahmad Abdelfattah, Mawussi Zounon, Panruo Wu, Srikara Pranesh, Stanimire Tomov, and Jack Dongarra. "The Design of Fast and Energy-Efficient Linear Solvers: On the Potential of Half-Precision Arithmetic and Iterative Refinement Techniques." In International Conference on Computational Science, pp. 586-600. Springer, Cham, 2018.

4. Srikara Pranesh, and Debraj Ghosh. "Cost reduction of stochastic Galerkin method by adaptive identification of significant polynomial chaos bases for elliptic equations." Computer Methods in Applied Mechanics and Engineering 340 (2018): 54-69..

3. Srikara Pranesh, and Debraj Ghosh. “A FETI-DP based parallel hybrid stochastic finite element method for large stochastic systems.” Computers & Structures 195 (2018): 64-73.

2. Srikara Pranesh, and Debraj Ghosh. “Addressing the curse of dimensionality in SSFEM using the dependence of eigenvalues in KL expansion on domain size.” Computer Methods in Applied Mechanics and Engineering 311 (2016): 457-475.

1. Srikara Pranesh, and Debraj Ghosh. “Faster computation of the Karhunen - Loeve expansion using its domain independence property.” Computer Methods in Applied Mechanics and Engineering 285 (2015): 125-145.

Contact

psrikara@gmail.com

Last updated : December 26, 2024