I live in London and work at the University of Cambridge and The Alan Turing Institute. I studied computational engineering at Cambridge and aerospace engineering previously at Maryland. During my doctorate I worked at Rolls-Royce plc in Derby and spent time at Stanford. My research focuses on uncertainty quantification, optimisation
and machine learning applied to address challenges in aeronautics. I am the founder of Effective Quadratures
and the organizer for the Cambridge Linear Algebra Seminar Series (CLASS).
My current research projects are:
- Bayesian models for improved aerothermal flow-physics in engines
- Subspace-based dimension reduction: algorithms and 3D blade design
- Machine learning with polynomials: from quadratures to neural networks
- Leveraging virtual reality for digital twins in aeronautics
For all my publications, please check out Google Scholar
For open-source codes, visit my Github
profile. If you are really interested in my research, check out this
high-level research talk.
For my CV, click here
Chun Yui Wong (PhD student): Embedded ridge approximations for improved computational aerodynamic inference.
James Gross (PhD student): Techniques in polynomial subspace projections for design optimisation.
Joe Zhou (Undergraduate student): High-dimensional quadrature rules.
Upcoming conference presentations.
- AIAA SciTech 2020: Optimisation with intrinsic dimension reduction: A ridge informed trust-region method.
- IEEE Aerospace 2020: Digital twin assessments in virtual reality: An exploration study with aeroengines.
Upcoming invited talks.
IN2-17 Inglis Building,
Department of Engineering,
University of Cambridge.
p.seshadri @ eng.cam.ac.uk