Ramaseshan Kannan
Associate Director Ai and Deeptech @Arup
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WORK HISTORY
Associate Director Ai and Deeptech @Arup
EDUCATION
The University of Manchester
Doctor of Philosophy (PhD)
Indian Institute of Technology, Madras
Dual Degree
SKILLS
ABOUT RAMASESHAN KANNAN
I am an Associate Director at Arup, and an ex-RAEng Industrial Fellow. I lead deeptech research and development combining machine learning, computational science, engineering modelling, and software engineering. The scope of my work is deep and wide: on some initiatives it involves taking ideas from an academic paper to product, on others it is working with clients and end users to identify greenfield opportunities for new product innovation. Track record includes mathematical algorithms productized as debugging tools for simulations, as accelerators for seismic design, as backend sparse matrix storage for high performance computing [and for optimising Transformer (inference time) mat-vec operations!]. The products I\'m transitioning to 0 to 1 include industry-first uses of Uncertainty Quantification, of fusing physics with data, and use surrogate modelling to accelerate engineering decision-making.I\'m a technical leader and maintain a hands-on involvement in research prototyping on the one hand whilst bringing a deep understanding of the business and the domain of built environment on the other. I have extensive experience in the full software development lifecycle, having previously founded and led software teams developing mission-critical commercial software products for engineering simulation and design.External collaborations include an RAEng visitorship at Department of Engineering at University of Cambridge and an external member of Numerical Linear Algebra group at University of Manchester. I sit on numerous committees at the intersection of AI and engineering such as+ TG 3.10 of International Association For Bridge And Structural Engineering (IABSE) for AI + TC206 of International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE) for Observational Method+ Study Group on AI-Informed Structural Engineering & Design of International Assoc. of Shells and Structures (IASS)+ Program Committee of AI in AEC conference organized by Finnish Academy of Civil Engineers (RIL)+ Editorial panel of ICE Journal of Computational Mechanics
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