In today’s episode, I welcome Ramaseshan Kannan, the Head of Computational Science at Arup and a Royal Academy of Engineering Industrial Fellow. With a PhD in Numerical Linear Algebra from the University of Manchester and a career that spans both industry and academia, Ramaseshan is at the forefront of research in our industry as it relates to computational science, machine learning, and numerical analysis.
Connect with Ramaseshan Kannan | LinkedIn
At Arup, Ramaseshan drives the development of advanced algorithms and finite element solvers, bringing innovation to the built environment. His work is a powerful blend of theory and practice, pushing the boundaries in high-performance computing, uncertainty quantification, and optimization.
This area of research is one of deep personal interest to me. While Ramaseshan delves into the underlying theory and mechanics, myself and many others are on the other side of the fence, trying to implement the types tools and ideas that he creates into practical applications for our clients and projects.
It’s an exciting time to be in engineering, as more tools are becoming available that are increasingly approachable, though each comes with its own learning and opportunity costs.
The decisions we make about which tools to invest our time in are crucial—and often, it’s only in hindsight that we understand their true value. But such is the art of progress, it’s only after the fact we realize we were banging our head against the wall.
My conversation with Ramaseshan was both inspiring and thought-provoking.
I had a fantastic discussion with him, and I look forward to our next conversation. He’s a remarkable thinker, and I’m eager to see where his ideas and his work will lead.
Thank you for being part of the Flocode community.
It’s been a lot of fun so far. I will have more announcements soon.
See you in the next one.
James 🌊
Notes:
What Could Possibly Go Wrong? Meta Tools for Digital Tools and Workflows 🛠️ - By Ramaseshan Kannan
Ramaseshan spoke briefly about the potential of LLM’s in the geometry or parametrization space and since our conversation, I found the following:
World Labs, a newly formed company, said Friday (Sept. 13) that it has raised $230 million to build large world models (LWMs) that “perceive, generate and interact with the 3D world.”
“We aim to lift AI models from the 2D plane of pixels to full 3D worlds — both virtual and real — endowing them with spatial intelligence as rich as our own,” the company said in a Friday post on LinkedIn.
Source: World Labs Raises $230 Million to Build Spatially Intelligent AI
Pretty interesting!
#045 - Flocode Podcast 🌊 | Ramaseshan Kannan - Computational Science in Engineering