Automatic experimentation

Date: Wednesday 26 January 2022, 2.00PM - 5.00PM
Location: Online
Online - joining instructions will be sent to those registered
Section Group Meeting
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Automatic experimentation refers to a sequential data-collection process where data are analysed and the next experiment to run is chosen and performed with little to no human involvement. The presentations in this session will showcase recent developments in this area motivated by real applications in science and engineering
 
Programme:

14:00 Welcome

14:05 Statistical methods for automatic optimisation of flow chemistry reactions - Tim Waite (University of Manchester, UK)

14:55 Optimal Bayesian Design of Finitely Sequential Experiments with Deep Deterministic Policy Gradient - Xun Huan (University of Michigan, USA)

15:45 Break

15:55 Entropy-based adaptive design for contour finding and estimating reliability - Robert Gramacy (Virginia Tech, USA)

1645 Questions and comments

1700 End

 

Speakers:

Tim Waite (University of Manchester, UK)

Xun Huan (University of Michigan, USA)

Robert Gramacy (Virginia Tech, USA)

Chair:

Antony Overstall (University of Southampton, UK)

 
Antony Overstall for RSS Computational Statistics & Machine Learning Section
 
Free to RSS Fellows
£10 for non-Fellows
 
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