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Statistics and Machine Learning in (Bio) Chemical Engineering - An Open Workshop

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If you have a question about this talk, please contact Alexei Lapkin.

9:30 Introduction to the Challenge-led Talks/Discussion (A. Lapkin)

Talks and discussion on the topics of surrogate functions, optimisation, classification, data mining, automation and more in-depth topics, such as specific methods of tackling various uncertainties in model development.

“Interpretable ML for Chemistry: Designing algorithms with statistical physics and extracting chemical knowledge from results”, Dr Alpha Lee, Cavendish Laboratory.

13:00 Lunch break

14:00 Seminar talk: “Closed-loop automatic experimentation for optimisation” Dave Woods, Professor of Statistics in the Southampton Statistical Sciences Research Institute

Abstract: Automated experimental systems, involving minimal human intervention, are becoming more popular and common, providing economical and fast data collection. We discuss some statistical issues around the design of experiments and data modelling for such systems. Our application is to “closed-loop” optimisation of chemical processes, where automation of reaction synthesis, chemical analysis and statistical design and modelling increases lab efficiency and allows 24/7 use of equipment. Our approach uses nonparametric regression modelling, specifically Gaussian process regression, to allow flexible and robust modelling of potentially complex relationships between reaction conditions and measured responses. A Bayesian approach is adopted to uncertainty quantification, facilitated through computationally efficient Sequential Monte Carlo algorithms for the approximation of the posterior predictive distribution. We propose a new criterion, Expected Gain in Utility (EGU), for optimisation of a noisy response via fully-sequential design of experiments, and we compare the performance of EGU to extensions of the Expected Improvement criterion, which is popular for optimisation of deterministic functions. We also show how the modelling and design can be adapted to identify, and then down-weight, potentially outlying observations to obtain a more robust analysis.

15:00 Introduction to the EPSRC project “Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products”, a collaboration between Universities of Cambridge, Glasgow and Southampton 15:30 Coffee/Networking 16:00 Afternoon workshop session 17:00 Close of workshop

Please register at https://www.eventbrite.co.uk/e/statistics-and-machine-learning-in-bio-chemical-engineering-tickets-46075627442

This talk is part of the Chemical Engineering and Biotechnology occasional seminars series.

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