Dr Sharana Kumar Shivanand

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sharana

Position

Research Associate

Partner Institution

University of Southampton
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Research areas

Numerical (Algorithms) Probabilistic programming Probability Simulation Modelling (Statistical methods & theory) Monte Carlo methods Uncertainty quantification Stochastic optimisation Dynamical systems & differential equations Numerical analysis Mathematical physics Ensemble (Mathematical modelling) Deterministic (Mathematical modelling) Deep learning

Bio

Sharana (he/him) is a Postdoctoral Research Associate in the Data-Centric Engineering programme at The Alan Turing Institute and a Visiting Academic at the School of Engineering at the University of Southampton, United Kingdom. Before this, he was in the AI for Science and Government programme at the Alan Turing Institute and a Visiting Research Fellow in the Department of Engineering at the University of Cambridge, United Kingdom. He received his Ph.D. in Applied Mathematics and M.Sc. in Computational Sciences in Engineering at the Technical University of Braunschweig, Germany.

Research interests

Sharana’s research is focused on the development of cutting-edge probabilistic and statistical methodologies in the realms of physics and data-driven numerical predictions for scientific and engineering applications. His interdisciplinary research combines uncertainty quantification, machine learning, and computational physics to explore innovative approaches to tackle challenges in diverse areas such as computational material science, structural mechanics, and bioengineering.

His current research primarily focuses on physics-informed machine learning with an emphasis on geometric deep learning. Alongside, he is actively engaged in research activities encompassing stochastic modelling of material tensors and multilevel uncertainty quantification. In his recent project at The Alan Turing Institute, he collaborated with the Department of Engineering at the University of Cambridge to develop advanced Monte Carlo methods aimed at enhancing the computational efficiency of digital twins in infrastructure.

Turing Research