United Kingdom
I am a Senior Lecturer (Associate Professor) in Engineering AI in the School of Engineering and Materials Science at Queen Mary University of London. Previously, I worked as a Postdoctoral Researcher at the Solid Mechanics and Materials Engineering group in the Department of Engineering Science at the University of Oxford. I'm also the leader of the team MultiFun, which won the Airbus-UNESCO Fly Your Ideas Global Challenge 2015 for a project on multifunctional materials for energy harvesting and storage and the awardee of the NLF Dutch Aerospace Award and the ITMA Future Materials Awards 2015 (WTiN, UK). I am an elected Fellow of the Royal Aeronautical Society (FRAeS) and the UK Higher Education Academy (FHEA). My current research lies in the broad area of aerospace structures and multifunctional composites while exploiting Artificial Intelligence (AI) Techniques for computational modelling and design. Some of the ongoing projects include data-driven constitutive models for faster finite element simulations, deep learning-enabled damage detection in composite materials, high-temperature self-healing materials, active vibration control using smart materials, and digital twins. I have also worked as the DSG Principal Investigator at the Alan Turing Institute (UK's National Institute for Data Science and AI) as an academic lead for an applied AI problem for mechanics. I was also the awardee of the UK-Italy Trustworthy AI Exchange Award from the Alan Turing Institute, London, UK. For more information about our group, please visit, www.saponnusami.com
Conducting research and teaching in aerospace structures and composite materials with a focus on developing predictive computational models and tools for analysis and design
My research is focused on multiphysics mechanics of materials while exploiting Artificial Intelligence (AI) techniques materials engineering and design.
Academic Lead for a project on data-driven and mechanistic detection and tracking of ocean eddy currents using machine learning techniques. In collaboration with National Oceanography Centre, UK.
Predictive numerical modelling of materials failure under impact in aerospace gas turbine engine fan blades. Other projects dealt with developing utilising data-driven machine learning tools for solid mechanics problems such as composite property prediction.