Dr. V.A. Kandappan is an Assistant Professor in the Department of Computer Science at Shiv Nadar University Chennai. He has a well-rounded academic background, having completed his Bachelor’s degree in Electrical and Electronics Engineering, a Master’s degree in Power Systems Engineering from Anna University, and a PhD in Computational Science from IIT Madras. Before his PhD, he worked as a Graduate Engineer at IBM India Pvt. Ltd., specialising in Software Testing Automation. His current research focuses on developing fast matrix algorithms for scientific computing applications, with a particular interest in high-performance computing and machine learning.

Publications

1. Kandappan, V. A., Vaishnavi Gujjula, and Sivaram Ambikasaran. (2023). “Hodlr2d: A new class of hierarchical matrices.” SIAM Journal on Scientific Computing 45, no. 5: A2382-A2408.
2. Khan, Ritesh, Kandappan, V. A., and Sivaram Ambikasaran. (2022) “Numerical rank of singular kernel functions.” arXiv preprint arXiv:2209.05819.
3. Kandappan, V. A., Vaishnavi Gujjula, and Sivaram Ambikasaran. (2023) “HODLR3D: Hierarchical matrices for $ N $-body problems in three dimensions.” arXiv preprint arXiv:2307.16303.
4. Kandappan, V.A., Rekha, A.G. (2021). Machine Learning in Finance: Towards Online Prediction of Loan Defaults Using Sequential Data with LSTMs. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1381. Springer, Singapore.

Conferences

1. Kandappan, V. A., and Sivaram Ambikasaran. (2022) “A New Preconditioner for Covariance Kernels and Green’s Function in 2D” in SIAM Annual Meeting (AN22).
2. Kandappan, V. A., and Sivaram Ambikasaran. (2022) “A Domain Decomposition-based Preconditioner for Discretised Integral Equations in Two Dimensions” in 24th Conference of the International Linear Algebra Society, NUIG, Ireland.
3. Kandappan, V. A., Vaishnavi Gujjula, and Sivaram Ambikasaran. (2022) “HODLR2D – A new class of Hierarchical Matrices with application to Lippmann Schwinger Equation” in $17th Copper Mountain Conference on Iterative Methods
4. Kandappan, V. A., and Sivaram Ambikasaran. (2021) “Accelerating Multi-frontal Method for Finite Element Matrices through Low-Rank Approximation” in SIAM Conference on Applied Linear Algebra (LA21).

Area of Research

• High-Performance Computing
• Soft Computing techniques
• Machine Learning Applications
• Parallel numerical algorithms
• Scientific Machine Learning
• Fast matrix algorithms