Optimization, Uncertainty and Machine Learning in Wind Energy Conversion Systems
暫譯: 風能轉換系統中的優化、不確定性與機器學習

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.

基沙雷·米特拉教授於1995年獲得印度杜爾卡普爾國立技術學院（NIT Durgapur）的工程學士學位，1997年獲得印度坎普爾印度科技學院（IIT Kanpur）的碩士學位，2009年獲得印度孟買印度科技學院（IIT Bombay）的博士學位（均為化學工程）。他是印度海德拉巴印度科技學院化學工程系的教授，並在人工智慧及氣候變遷系擔任相關教職。他的研究興趣在於數據分析與過程優化的交界處，包括機器學習、進化優化、不確定性下的優化、供應鏈的規劃與排程，以及涉及生物學、氣候變遷和可再生能源的系統分析。他在通用電氣全球研究（General Electric Global Research）班加羅爾和塔塔研究發展與設計中心（Tata Research Development & Design Centre）浦那擔任多個工程領導職位，工作近15年。他的研究得到了印度多個主要機構的支持，如科學與技術部、生物技術部、教育部、國防研究與發展組織以及塔塔鋼鐵，通過多個國家重要項目（5000萬印度盧比）。他擁有超過200篇國際期刊和會議的出版物。除了擔任多個知名期刊和會議的編輯委員會成員外，他還曾多次作為訪問教授前往美國聖路易斯華盛頓大學和西雅圖華盛頓大學。根據斯坦福大學一組進行的最新評估，自2021年以來，他被評選為全球前2%的科學家之一。

理查德·埃弗森教授是埃克塞特大學的機器學習教授及數據科學與人工智慧研究所所長。他是艾倫·圖靈研究所的研究員，並擔任埃克塞特的圖靈大學負責人。他的研究興趣集中在統計機器學習、多目標優化及其之間的相互作用。特別相關的是他在進化和穩健貝葉斯優化中測量和考量不確定性的工作。他是EPSRC「數據驅動的代理輔助進化流體動力學優化」的主要研究者，該項目開創了計算流體動力學問題的貝葉斯優化方法。

喬納森·菲爾登教授於1998年獲得達勒姆大學的經濟學學士學位，1999年獲得普利茅斯大學的計算智能碩士學位，2003年獲得埃克塞特大學的計算機科學博士學位。他是埃克塞特大學的計算智能教授。他主要研究優化啟發式方法與機器學習的交界處，包括基本進展以及與產業和公共部門合作解決當前和短期問題。他特別關注多目標、高成本和不確定的設計優化問題，以及搜索景觀特徵化。他參與了多項專業活動，包括在2022年擔任ACM遺傳與進化計算會議的主編。