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6th Machine Learning and AI in Bio(Chemical) Engineering Conference

06-07th July 2023
In person-only event

Poster submission open until

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

The Conference


We are proud to bring inspirational speakers from across the globe

Confirmed speakers

Prof. Pietro Liò (University of Cambridge)
  • Generative and Graph models in chemistry and medicine
Dr. Emma King-Smith (University of Cambridge)
  • Practical Machine Learning for Synthetic Chemistry
Andrea Dimitracopoulos (DeepMirror)
  • Classical and Deep Learning for Drug Property Prediction and Uncertainty Estimation: A Comparative Study  
Egon Heuson (University of Lille)
  • Enzyme activity prediction using neural networks, docking and high-throughput screening results
Benoît Baillif (University of Cambridge)
  • Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations 
Tom Savage (Imperial College London)
  • Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations (DARTS)
Nishanthi Gangadharan (University of Cambridge)
  • Data-driven Dynamic Control Scheme for Antibody Producing CHO Cell Cultures in Fed Batch 
Prof. Reinhard J. Maurer (University of Warwick)
  • High-throughput property-driven generative design of functional organic molecules 
Jairu Bai (University of Cambridge)
  • From Platform to Knowledge Graph: Distributed Self-Driving Laboratories 
Miruna Cretu (IBM Research Zurich)
  • Standardizing chemical compounds using language models
Kobi Felton (University of Cambridge)
  • ML-SAFT: A framework for PCP-SAFT parameter prediction
Dr. Antonio Del Rio Chanona (Imperial College of London)
  • Title to be confirmed
William Moss (Opvia)
  • Title to be confirmed
Dr. Lucy Colwell (University of Cambridge)
  • Keynote talk: Title to be confirmed


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)


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:

  • 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;

  • fit multiple models automatically and select the best-performing model with model screening;

  • apply cross-validation techniques to avoid overfitting; 

  • use the Prediction Profiler to improve model understanding and make predictions;

  • deploy models both inside and outside of JMP (e.g., in Python)


06-07th July 2023
Hosted by the University of Cambridge

Standard registration: £75

Fees include access to the event, refreshments for both days and confererence networking dinner in the evening of day 1.

Payments can only be done by credit card.

Abstract Submissions

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:

  • Software implementations of key methods

  • Experimental case studies using ML

  • Method development, particularly those that simplify user experience

  • 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.

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