Speaker: Assoc. Prof. Richard Allmendinger, The University of Manchester 

Title: Experimental challenges in expensive optimization

Synopsis: Evaluating candidate solutions by conducting an experiment, e.g. a physical, biological or chemical experiment, can be expensive, time-consuming and/or resource intense. Drug development, instrument setup optimization, and robotics are prime examples where optimization relies on experiments. This talk will introduce several non-standard challenges arising due to experiments such as non-homogeneous per-objective evaluation times in a multi-objective problem, dynamic resource constraints, non-static drug libraries, and problems with safety concerns, touch on existing techniques to cope with these challenges, and discuss promising areas of future work.

Bio: Richard Allmendinger is Associate Professor in Decision Science at Alliance Manchester Business School, The University of Manchester (UoM), UK, and Fellow of the Alan Turing Institute, UK’s national institute for data science and artificial intelligence. Richard has a PhD degree in Computer Science from UoM, and a Diploma degree in Business Engineering from Karlsruhe Institute of Technology, Karlsruhe, Germany. His research interests include the development and application of decision support systems, comprising simulation, optimization and machine learning tools, in the areas of business & management, healthcare, and manufacturing. Richard’s work is largely funded by various UK funding bodies (e.g. EPSRC, ESRC, and Innovate UK) and industrial partners. Richard is on the editorial board of several international journals in the area of applied data and decision science.

WED 27 APR 4 PM – 5 PM AEST

ZOOM Meeting ID: 873 1557 5255; Password: 778635