Optimization, Uncertainty and Machine Learning in Wind Energy Conversion Systems

Prof. Kishalay Mitra received his B.E. from the National Institute of Technology (NIT) Durgapur, India, in 1995, M. Tech. from Indian Institute of Technology (IIT) Kanpur, India, in 1997, and Ph.D. from the Indian Institute of Technology (IIT) Bombay, India, in 2009 (all in Chemical Engineering). He is a Professor in the Department of Chemical Engineering and associated faculty in the Departments of Artificial Intelligence and Climate Change at IIT Hyderabad, India. His work interests lie in the interface of data analysis and process optimization such as machine learning, evolutionary optimization, optimization under uncertainty, planning and scheduling of supply chain, and analysis of systems involving biology, climate change, and renewable energy. He worked in several engineering leadership positions at General Electric Global Research, Bangalore, and Tata Research Development & Design Centre, Pune for close to 15 years. His research has been supported by several leading agencies in India like the Department of Science & Technology, Department of Bio-Technology, Ministry of Education, Defence Research & Development Organization, and Tata Steel through several nationally important projects (~ INR 50 million). He has over 200 international journal and conference publications to his credit. Apart from serving on the editorial board of several reputed journals and conferences, he has been visiting Washington University in St. Louis, USA, and the University of Washington, Seattle as a visiting professor on several occasions. He has been named among the World's top 2% scientists according to the latest profile review conducted by a group from Stanford University since 2021.

Prof. Richard Everson is a Professor of Machine Learning and Director of the Institute for Data Science and Artificial Intelligence at the University of Exeter. He is a Fellow of The Alan Turing Institute and the Turing University Lead for Exeter. His research interests focus on statistical machine learning, multi-objective optimization, and the interactions between them. Particularly relevant is his work on measuring and accounting for uncertainty in evolutionary and robust Bayesian optimization. He was the principal investigator for the EPSRC `Data-Driven Surrogate-Assisted Evolutionary Fluid Dynamic Optimisation' which pioneered Bayesian optimization methods for Computational Fluid Dynamics problems.

Prof. Jonathan Fieldsend received a BA in Economics from Durham University in 1998, an MSc in Computational Intelligence from the University of Plymouth in 1999, and a PhD in Computer Science from the University of Exeter in 2003. He is a Professor of Computational Intelligence at the University of Exeter. He primarily works on the interface of optimization heuristics and machine learning. This includes both fundamental advances, as well as solving immediate and near-term problems with partners from industry and the public sector. He has particular interests in multi-objective, expensive, and uncertain design optimization problems, along with search landscape characterization. He has contributed to several professional activities, including acting as Editor-in-Chief for the ACM Genetic and Evolutionary Computation Conference in 2022.