6th Machine Learning and AI in Bio(Chemical) Engineering Conference
06-07th July 2023
In person-only event
Poster submission open until
June 16th
Machine Learning and AI in Bio(Chemical) Engineering Conference series is co-organised by research groups of the Universities of Cambridge, Leeds, Glasgow, Southampton, and University College London. This series of conferences grew from two collaborative research projects on robotics and automation in chemical development. The conference will feature keynote and invited lectures from global champions of research in ML/AI in chemistry/(bio)chemical engineering, and regular lectures selected from the community submissions.
Conference Agenda
The event will be held at the Department of Chemical Engineering and Biotechnology,
Philippa Fawcett Dr, Cambridge CB3 0AS
July 06th, day one
10:00 – 11:00 Welcome and refreshments
11:00 – 12:00 Keynote 1
12:00 – 12:25 Speaker 1
12:25 – 12:50 Speaker 2
12:50 – 14:00 Lunch
14:00 – 14:35 Speaker 3
14:35 – 15:00 Speaker 4
15:00 – 15:25 Speaker 5
15:25 – 15:35 Break
15:35 – 16:10 Speaker 6
16:10 – 16:35 Speaker 7
16:35 – 17:00 Speaker 8
17:00 – 17:25 Speaker 9
17:25 – 19:15 Networking and dinner
19:15 Day 1 end
July 07th, day two
09:00 – 09:15 Coffee reception
09:15 – 10:00 Workshop part 1 - Rapid predictive modelling without having to write code
10:00 – 10:15 Break
10:15 – 11:00 Workshop part 2 - Rapid predictive modelling without having to write code
11:00 – 12:00 Poster Session
12:00 – 12:35 Speaker 10
12:35 – 13:00 Speaker 11
13:00 – 14:00 Lunch
14:00 – 15:00 Speaker 12
15:00 – 15:25 Speaker 13
15:25 – 15:45 Break
15:45 – 16:10 Speaker 14
16:10 – 16:35 Speaker 15
16:35 – 17:00 Closing Remarks
17:00 End of day 2
Speakers
We are proud to bring inspirational speakers from across the globe
Confirmed speakers
Prof. Pietro Liò (University of Cambridge)
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Generative and Graph models in chemistry and medicine
Dr. Emma King-Smith (University of Cambridge)
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Practical Machine Learning for Synthetic Chemistry
Andrea Dimitracopoulos (DeepMirror)
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Classical and Deep Learning for Drug Property Prediction and Uncertainty Estimation: A Comparative Study
Egon Heuson (University of Lille)
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Enzyme activity prediction using neural networks, docking and high-throughput screening results
Benoît Baillif (University of Cambridge)
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Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
Tom Savage (Imperial College London)
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Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations (DARTS)
Nishanthi Gangadharan (University of Cambridge)
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Data-driven Dynamic Control Scheme for Antibody Producing CHO Cell Cultures in Fed Batch
Prof. Reinhard J. Maurer (University of Warwick)
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High-throughput property-driven generative design of functional organic molecules
Jairu Bai (University of Cambridge)
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From Platform to Knowledge Graph: Distributed Self-Driving Laboratories
Miruna Cretu (IBM Research Zurich)
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Standardizing chemical compounds using language models
Kobi Felton (University of Cambridge)
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ML-SAFT: A framework for PCP-SAFT parameter prediction
Dr. Antonio Del Rio Chanona (Imperial College of London)
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Title to be confirmed
William Moss (Opvia)
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Title to be confirmed
Dr. Lucy Colwell (University of Cambridge)
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Keynote talk: Title to be confirmed
Workshop
Rapid Predictive Modeling without Having to Write Code
Volker Kraft, JMP Principal Academic Ambassador
Phil Kay, JMP Learning Manager
More speakers for 2023 to be confirmed
Keynote speaker for 2023 edition
Dr. Lucy Colwell (Cambridge)
Workshop
Rapid Predictive Modeling without Having to Write Code
Volker Kraft, PhD, JMP Principal Academic Ambassador
Phil Kay, PhD, JMP Learning Manager
JMP Pro is an industry-standard software for predictive modeling. In fields like chemistry & biotech, pharma, manufacturing or environment & sustainability, modern modeling methods and powerful tools help engineers and scientists to make statistical discoveries from data – without the need to write any line of code.
During this hands-on workshop you will learn how to:
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build better and more useful models with modern predictive modeling techniques, such as regression, neural networks, decision trees, support vector machines, k nearest neighbors, and more;
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fit multiple models automatically and select the best-performing model with model screening;
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apply cross-validation techniques to avoid overfitting;
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use the Prediction Profiler to improve model understanding and make predictions;
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deploy models both inside and outside of JMP (e.g., in Python)
Abstract Submission
Poster deadline: 16th June/2023
The 6th International Conference on Machine Learning and AI in (bio)Chemical Engineering will take place on July 06-07, 2023. We are requesting abstracts for posters and talks related to this theme. Potential topics/submissions include but are not limited to:
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Software implementations of key methods
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Experimental case studies using ML
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Method development, particularly those that simplify user experience
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Benchmarking of ML methods
Abstracts may be up to 400 words and optionally include explanatory figures. We emphasise that abstracts should include sufficient introduction for newcomers.