IDSAI Research Fellows
The Institute for Data Science and Artificial Intelligence has appointed four Research Fellows since July 2019. The research fellows have split roles within the Institute; they conduct their own independent research alongside undertaking pilot research projects aligned to the aims of the IDSAI and the Alan Turing Institute.
Dr. Ravi Pandit
Ravi joined the IDSAI in July 2020 having previously been a Research Associate (RA) at the University of Strathclyde, with a PhD in Electronics and Electrical Engineering. His expertise includes data-driven frameworks for offshore wind such as big data analysis, condition monitoring, predictive maintenance, machine learning & deep learning and forecasting & predictions.
Data Science Expertise: Machine Learning (the application AI/Machine learning for solving industrial problems such as big data analysis, condition monitoring, predictive maintenance).
Bertrand joined IDSAI in October 2020. His interests include regression and regularization and more generally, computational statistics and statistical machine learning.
Data Science Expertise: Optimisation, Machine learning, Bayesian inference, Time series analysis, Statistical modelling, Emulation/uncertainty quantification, Frequentist inference, Regularization, Generalized additive models, Quantile regression, R programming.
George De Ath
George joined the IDSAI in July 2021 from his role as a Research Fellow working on digital twin calibration projects at the University of Exeter. George's research interests include the optimisation (calibration) of expensive-to-evaluate problems using Bayesian optimisation, as well as more general single- and multi-objective optimisation problems, machine learning and computer vision.
Data Science Expertise: Optimisation, calibration, machine learning, modelling/emulation, Bayesian inference and uncertainty quantification.
Andy joined the Institute in September 2021 following a fellowship at the University of Exeter, with an industrial partner, researching computer vision and mathematical modelling for oceanographic optical transfer. With a background in pure mathematics and a fascination for the mechanics of machine learning, Andy’s research is themed by using mathematical methods to investigate the inner workings of computer algorithms with a focus on explainability of the physical systems they represent.
Data Science Expertise: Deep neural networks; computer vision; explainable AI; physics-guided algorithms; mathematical modelling of dynamical systems; practical machine learning.
Andy’s personal homepage may be found here where you will find contact details and research.