V A Kandappan

207, Academic Building 2

Shiv Nadar University - Chennai

Kalavakkam - 603110

Dr. V.A. Kandappan began his academic journey as an Assistant Professor in the Department of Computer Science Engineering at Shiv Nadar University, Chennai, in October 2023. He pursued his PhD at the Computational and Data Science Department in IISc Bangalore. He later moved to the Department of Mathematics at IIT Madras, where he successfully obtained his Ph.D. His doctoral research, supervised by Dr Sivaram Ambikasaran, focused on developing a new class of fast matrix algorithms for N-body problems in 2D and 3D, with complexity, rank, and error estimates.

He holds a Master’s degree in Engineering (Power Systems) from the College of Engineering Guindy, Anna University, and a Bachelor’s degree in Engineering (Electrical and Electronics) from MEPCO Schlenk Engineering College. Prior to pursuing his PhD, he worked as a Graduate Engineer at IBM India Private Limited, specializing in Software Testing. His research interests include (not limited to) High-performance computing applications, Fast matrix algorithms, and Applications of Machine learning, among others.

Apart from academics, he enjoys following NBA, F1, and chess. He is also a big fan of Sheldon Cooper. “Bazinga!”**2

news

Nov 24, 2023	Invited talk on Hierarchical low-rank structures on distributed memory systems. Program Website
Oct 12, 2023	Started as Assistant Professor at Shiv Nadar University Chennai.
Jul 21, 2023	Successfully defended PhD Thesis.

latest posts

No posts so far...

selected publications

HODLR2D: A New Class of Hierarchical Matrices

V A Kandappan, Vaishnavi Gujjula, and Sivaram Ambikasaran

SIAM Journal on Scientific Computing, 2023

Abs Bib

Abstract. This article introduces HODLR2D, a new hierarchical low-rank representation for a class of dense matrices arising out of \(N\) -body problems in two dimensions. Using this new hierarchical framework, we propose a new fast matrix-vector product that scales almost linearly. We apply this fast matrix-vector product to accelerate the iterative solution of large dense linear systems arising out of radial basis function interpolation and discretized integral equation. The space and computational complexity of HODLR2D matrix-vector products scale as \(\mathcalO(pN \log (N))\) , where \(p\)is the maximum rank of the compressed matrix subblocks. For the logarithmic kernel function, we prove that \(p ∈\mathcalO \left (\log \left (N\right ) \log \left (\log \left (N\right ) \right ) \right )\) . We also numerically observe a similar scaling for other kernel functions in 2D. We thereby demonstrate that the storage and computational complexity of HODLR2D matrix-vector products remain tractable for large \(N\) . Additionally, we also study the parallel scalability of HODLR2D as part of this article.
@article{hodlr2d, author = {Kandappan, V A and Gujjula, Vaishnavi and Ambikasaran, Sivaram}, title = {HODLR2D: A New Class of Hierarchical Matrices}, journal = {SIAM Journal on Scientific Computing}, volume = {45}, number = {5}, pages = {A2382-A2408}, year = {2023}, doi = {10.1137/22M1491253}, eprint = {{https://doi.org/10.1137/22M1491253}}, }