Ramaseshan Kannan
About Ramaseshan Kannan
Ramaseshan Kannan is an Associate at Arup with extensive experience in engineering and computational linear algebra, specializing in algorithms and numerical analysis.
Title and Current Position
Ramaseshan Kannan is currently an Associate at Arup, where he has been employed since 2019. He is integral to the Digital Technology group, leading research and development efforts in Algorithms and Numerical Analysis.
Professional Experience at Arup
Ramaseshan Kannan has an extensive history with Arup, starting as an Engineer from 2005 to 2010. He then transitioned to a Consultant Researcher role until 2014, followed by a position as Senior Engineer until 2019. His current role as an Associate involves managing products such as solvers and numerical libraries, and liaising with end-users to develop and prioritize new software features.
Educational Background
Ramaseshan Kannan achieved a Doctor of Philosophy (PhD) in Numerical Linear Algebra from The University of Manchester. Additionally, he holds a Dual Degree in Civil and Structural Engineering from the Indian Institute of Technology, Madras.
Research and Development Expertise
Within his role at Arup, Ramaseshan Kannan focuses on deriving and designing novel algorithms to address numerical and engineering challenges. He develops numerical software utilizing C++ and related technologies, with substantial experience in multi-core and many-core programming. His specialties include graph and clustering algorithms, software performance optimization, and finite element solvers.
Technology and Tools Proficiency
Ramaseshan Kannan is proficient in a wide range of programming and computational technologies, including C++ 11/14, Intel MKL, STL, Boost, Eigen, TileDB, GPGPU, SIMD intrinsics, .NET, MATLAB, OpenMP, JIRA, Git, and Python. He integrates new numerical and computational research into software and conducts peer reviews for academic journals.
Collaborative Research and Academic Involvement
Ramaseshan Kannan co-supervises research collaborations with various university partners, contributing to advancements in his fields of interest. These encompass numerical analysis, matrix algorithms, finite element structural analysis, natural language processing, machine learning, HPC software development, computational mechanics, sparse linear algebra, shared memory parallelism, and heterogeneous architectures.