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Dr Tinkle Chugh is a Lecturer in Computer Science at the University of Exeter. He is Associate Editor of the Complex and Intelligent Systems journal. Between Feb 2018 and June 2020, he worked as a Postdoctoral Research Fellow in the BIG data methods for improving windstorm FOOTprint prediction (project funded by Natural Environment Research Council UK. He obtained his PhD degree in Mathematical Information Technology in 2017 from the University of Jyväskylä, Finland. His thesis was a part of the Decision Support for Complex Multiobjective Optimization Problems project, where he collaborated with Finland Distinguished Professor (FiDiPro) Yaochu Jin from the University of Surrey, UK. His research interests are machine learning, data-driven optimization, evolutionary computation, and decision making.

Webpage: http://emps.exeter.ac.uk/computer-science/staff/tc489


Dr Richard Allmendinger is a Professor of Applied AI at the Alliance Manchester Business School, The University of Manchester, and Fellow of The Alan Turing Institute, the UK’s national institute for data science and artificial intelligence. Richard has a background in Business Engineering (Diplom, Karlsruhe Institute of Technology, Germany + Royal Melbourne Institute of Technology, Australia), Computer Science (PhD, The University of Manchester, UK), and Biochemical Engineering (Research Associate, University College London, UK). Richard’s research interests are in the development and application of optimization, learning and analytics techniques to real-world problems arising in areas such as management, engineering, healthcare, sports, music, and forensics. Richard is known for his work on non-standard expensive optimization problems comprising, for example, heterogeneous objectives, ephemeral resource constraints, changing variables, and lethal optimization environments. Much of his research has been funded by UK funding bodies (e.g. Innovate UK, EPSRC, ESRC, ISCF) and industrial partners. Richard is a Member of the Editorial Board of several international journals, Vice-Chair of the IEEE CIS Bioinformatics and Bioengineering Technical Committee, Co-Founder of the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering, and contributes regularly to conference organisation and special issues as guest editors.

Webpage: http://personalpages.manchester.ac.uk/staff/Richard.Allmendinger/default.htm


Dr Jussi Hakanen is a Senior AI Scientist at Silo AI. He received MSc degree in mathematics and PhD degree in mathematical information technology, both from the University of Jyväskylä, Finland. His research is focused on multiobjective optimization and decision making with an emphasis on interactive multiobjective optimization methods, data-driven decision making, computationally expensive problems, explainable/interpretable machine learning, and visualization aspects related to many-objective problems. He has participated in several industrial projects involving different applications of multiobjective optimization, e.g. in chemical engineering. He has been a visiting researcher in Cornell University, Carnegie Mellon, University of Surrey, University of Wuppertal, University of Malaga and the VTT Technical Research Center of Finland. He has a title of Docent (similar to Adjunct Professor in the US) in Industrial Optimization at the University of Jyväskylä.

Webpage: https://scholar.google.com/citations?user=s7ij6T0AAAAJ&hl=fi


Professor Julia Handl obtained a Bsc (Hons) in Computer Science from Monash University in 2001, an MSc degree in Computer Science from the University of Erlangen-Nuremberg in 2003, and a PhD in Bioinformatics from the University of Manchester in 2006. From 2007 to 2011, she held an MRC Special Training Fellowship at the University of Manchester, and she is now a Professor in Decision Sciences at Alliance Manchester Business School. A core strand of her work explores the use of multiobjective optimization in unsupervised and semi-supervised classification. She has developed multiobjective algorithms for clustering and feature selection tasks in these settings, and her work has highlighted some of the theoretical and empirical advantages of this approach.

Webpage: https://personalpages.manchester.ac.uk/staff/julia.handl/

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