OPTIML PSE

It's going to be a busy week! The second lineup of speakers at the upcoming #AIChEAnnual conference, featuring Niki, Tom, and Abdullah. If your schedule permits, don't miss the opportunity to join our talks! [2/2] @ImperialChemEng @ChEnected

We are proud to announce talks from Akhil (CAST Student Presentation Award Finalist🏆), Haiting, and Miguel at the upcoming #AIChEAnnual conference. We look forward to seeing some of you there and if you have time, drop into one of our talks! [1/2] @ImperialChemEng @ChEnected

Welcome to our new PhD signing @matthew_marsh11! After completing his MEng at @ImperialChemEng 🎊he is joining the group to work on all things data-driven: modelling, optimisation, and control 🤖

Congratulations to the MSc cohort for their poster presentations, concluding their year-long program. Featured: Maria's research on multi-fidelity Bayesian optimization for reactor design. @ImperialChemEng

Welcome @laurahelleckes! Joining as an @ImperialX_AI postdoctoral fellow, focusing on ML for protein secretion prediction following a PhD in bioprocess lab automation. They're excited to collaborate with PhD students and explore London's cafés! @ImperialChemEng

Congratulations to @EmPajak21 for winning the Sue Gibson Award for best presentation at the MRes Advanced Molecular Synthesis Annual Symposium, sponsored by the @RoySocChem! Emma is working on tracing carbon atoms through chemical value chains with @BASF. @ImperialChemEng

Our August issue is live! ML-assisted flow reactor design, a DNA-based chemical neuron that 'senses' temperature, large-scale graphene-based current collectors, direct imaging of nanocube self-assembly pathways, and more! Read the full issue here: nature.com/natchemeng/vol…

Congrats to group member Buse for winning the best paper award at #ACL2024 in the Scholarly Document Comprehension Workshop! 📊📄

New paper alert 🚨We create a method that allows for humans to easily and effectively interact with Bayesian optimization algorithms, enabling faster optimization and discovery, and in the worst case recovering standard convergence. Thanks to @AntonioE89! doi.org/10.1016/j.comp…

🇨🇦 @KotechaNiki is in Canada for @IFAC_Control's #ADCHEM2024. Tomorrow, she presents 'Leveraging Reinforcement Learning and Evolutionary Strategies for Dynamic Multi Objective Decision Making in Supply Chain Management'.

Thank you to @cebcambridge for organising the 7th Machine Learning and AI in Bio(Chemical) Engineering conference. Group member @MaximilianBloor presented his work on Control-Informed Reinforcement learning and the Python package pc-gym!

Excited to announce @KotechaNiki will be presenting her work on Multi Agent Reinforcement Learning tomorrow at 10:30 am at the 33rd European Conference on Operational Research!

@DrMercangoz @FriedrichH01 @MaximilianBloor After an exciting first day of presentations at ESCAPE, we would like to announce Abdullah Bahamdan (@A_Ba7mdan), who will present his work Surrogate Based Mixed Integer Linear Programming Model for Decarbonization of an Integrated Gas-Oil Separation Network on Thursday Morning.

@DrMercangoz @FriedrichH01 As we travel to Florence, we want to announce our next member @MaximilianBloor who will present his work on Control-Informed Reinforcement Learning. Catch his talk on Tuesday Afternoon!

With the ESCAPE34-PSE24 conference next week in Florence ☀ We would like to announce our first group member presentation, Akhil Ahmed (co-supervised by @DrMercangoz ), who will present his work on Adaptive ARRTOC!

🎉 Our group member Miguel just had their work “The Automated Discovery of Kinetic Rate Models – Methodological Frameworks” published in RCS Digital Discovery. Take a look here: tinyurl.com/ypkt6n5k @ImperialChemEng

📑Our recent group meeting discussed a paper out of @StanfordAILab, offering a captivating alternative to RL with human feedback. Then @Savage_Tom shared insights on plotting best practices! 🎨

Congrats to group member Fred!

🚀Exciting news! Our group member Friedrich Hastedt just shared a paper on @ChemRxiv, discussing the Reliability and Interpretability of ML Frameworks for Chemical Retrosynthesis. Take a moment to check it out here: tinyurl.com/yr39hy67 @EPSRC_CDT_React @ImperialChemEng