Siva Rajamanickam is a research scientist at Sandia National Laboratories where he focuses on High Performance Computing. One common theme among all of his work is to design architecture-aware algorithms for next-generation Exascale supercomputers. Specifically, he works on linear solvers and performance-portable kernels that are foundational tools for many scientific simulations of national interest. His primary interest in linear solvers area is to develop algorithms for linear solvers that is targeted towards future supercomputers. He is interested both in the node-level and system-level solvers. At the node-level, his interests lie in the task-parallel and/or data-parallel factorization-like preconditioners, smoothers, and direct solvers. At the system-level, he focuses on hybrid Schur complement methods. His primary interest in performance-portable kernels is to develop foundational sparse, dense, and graph kernels for different architectures.