ECML talk series 2019 Title: Experimental optimization with Richard Allmendinger (University of Manchester) Speaker: Richard Allmendinger, University of Manchester When: 4 - 5pm, Wednesday 27 March 2019 Where: 14.08.14 meeting area Abstract: This presentation will have two main parts: First I will provide an overview of the various research activities I am currently involved in and happy to collaborate on. The second part will introduce the area around experimental optimization and some of the rather non-standard challenges in this area. In particular, I will discuss the challenges around (i) changing decision variables during search and (ii) multiobjective problems where the objective functions have non-homogenous evaluation times. Real applications are associated with each challenge and some methods to deal with them are presented. Speaker bio: Richard is Business Engagement Lead of Alliance Manchester Business School and Lecturer in Decision Sciences at the University of Manchester. Prior to Manchester, he worked at the Biochemical Engineering Department, University College London. He studied Business Engineering at the Karlsruhe Institute of Technology and completed a PhD in Computer Science at the University of Manchester. Richard's research interests are in the field of data science and in particular in the development and application of optimization, learning and analytics techniques to real-world problems arising in areas such as healthcare, manufacturing, economics, sports, music, and forensics. Much of his research has been funded by grants from Innovate UK, the Engineering and Physical Sciences Research Council (EPSRC), and industrial partners. Richard is the Co-Founder of the IEEE CIS Task Force on Optimization Methods in Bioinformatics and Bioengineering, a Member of the IEEE CIS Bioinformatics and Bioengineering Technical Committee, the General Chair of the 2017 IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, and a Member of the Editorial Board of the Applied Soft Computing journal (IF 4.004).