Machine Learning and AI in (Bio)chemical engineering
Event Information
Description
Application of machine learning in (bio)chemical engineering is a rapidly developing field with a very fast pace of discovery and innovation. We invite you to an open conference/workshop, comprising of talks, poster presentations and discussions on the topics of machine learning-based optimisation, classification and regression ML algorithms for (bio)chemical engineering applications, data mining and automation in (bio)chemical engineering, and more in-depth topics, such as specific methods of tackling various uncertainties in model development. If you would like to present your work, please send a 1A4 abstract to Prof. Alexei Lapkin, who is coordinating the organising committee of the conference, by May 17th, indicating your preference of oral/poster presentation.
The event is hosted by two EPSRC projects: “Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products”, a collaboration between Universities of Cambridge, Glasgow and Southampton, and “Cognitive Chemical Manufacturing”, a collaboration between Universities of Leeds, Nottingham and UCL. As well as presentations from the two projects, we are inviting abstract submissions for oral and poster presentations at the conference.
This meeting is free to attend. There will be a dinner on the evening of 8th July, and lunch will be provided on both days.
Agenda
A full programme will be published shortly. Presentations will include:
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Keynote talk - “Machine learning computer aided chemical synthesis”, Prof. Klavs Jensen, MIT
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"Kernel-based learning approach to diesel engine emission modelling", Changmin Yu, Cambridge Centre for Advanced Research and Education, Singapore
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"Error-controlled exploration of chemical reaction networks with Gaussian Processes", Gregor N. Simm, University of Cambridge
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"Noisy, sparse and nonlinear: Approaching the Bermuda Triangle of physicochemical inference with deep filtering", C. Poelking, University of Cambridge
1. Who should attend?
Anyone interested in the application of ML/AI in (bio)chemical engineering and in predictive scalability of reactions. We welcome delegates from academia, industry and government. We are looking to bring in people with a wealth of experience in the many different subject areas that are needed so that we can form interdisciplinary partnerships and work together to further the field.
2. What will I get out of it?
You will be able to network with likeminded people who have research interests that complement yours. There will be several keynotes around the major workshop topics to spark discussion and ideas. There will be an opportunity to present your own research in oral and/or poster sessions, and there will be plenty of opportunity to have general discussions and some specific topic based discussions in smaller groups.
3. What are the aims of this workshop?
This workshop is aiming to to drive progress in the area and facilitate collaboration by introducing people to make new interdisciplinary teams, and to produce new grant applications. To achieve this we may commission literature reviews, papers, or small scale investigations to test out new ideas. We welcome ideas and suggestions about how to go forward in this area and how best to achieve our aims.
4. What are the main themes of these workshops?
The main themes these workshops seek to address are: ML/AI in (Bio)Chemical Engineering, chemical robotics, machine learning methods for process development and optimisation, model development and related topics
5. Could I present at ‘ML/AI in (bio)chemical engineering’?
There will be opportunities to present both talks and posters. Please send a 1A4 abstract to Prof. Alexei Lapkin, who is coordinating the organising committee of the conference, by May 17th, indicating your preference of oral/poster presentation.
6. Where do I find accommodation closest to the venue?
The event will be held in the Department of Chemical Engineering and Biotechnology, University of Cambridge, located on West Cambridge Site (Philippa Fawcett Drive, CB3 0AS). The site is easily reached from the centre of Cambridge on the University bus (U). It is about 10-15 min cycling distance from the city centre, or a 30 min walk (from Kings College’ gate). We recommend to explore available hotels using your favourite hotel booking services, but also looking specifically at listings of B&B offers by Colleges, such as B&B at Churchill College, the closest college to the event venue: https://www.chu.cam.ac.uk/conferences/services/bandb/
Organiser Prof. Alexei Lapkin
Organiser of Machine Learning and AI in (Bio)chemical engineering
