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SUMMARY:AI-Driven Chemical Processes: From Human-in-the-Loop to Self-Optim
 ising Labs - Dr Antonio del Rio Chanona - Imperial College London
DTSTART:20251029T133000Z
DTEND:20251029T143000Z
UID:TALK237085@talks.cam.ac.uk
CONTACT:120673
DESCRIPTION:Artificial intelligence offers a new path to chemical processe
 s: enabling chemical systems that not only learn from data but also improv
 e themselves over time.  In this talk\, I will outline how AI can close cr
 itical gaps in process development and operation\, paving the way towards 
 autonomous\, self-optimising laboratories and process plants. I will focus
  on three complementary algorithmic approaches. The first\, Bayesian optim
 isation\, provides a framework for experimental design and optimisation. I
  will particularly discuss how we can use multi-fidelity and human-in-the-
 loop strategies for a more informed process optimisation.  Second\, I will
  discuss how large language models (LLMs) can and are being leveraged to c
 apture human knowledge\, interact with algorithms\, and enable autonomous 
 processes via symbolic reasoning and algorithmic search.  Finally\, I will
  talk about how reinforcement learning\, famous for making computers “le
 arn” and beat the best humans in the world at various tasks\, unlocks ne
 w opportunities for control and real-time optimisation of complex\, dynami
 c processes\, with particular promise in bioprocess applications. Together
 \, these advances point to a unifying vision: AI-driven chemical processes
  that are faster to develop\, safer to operate\, and inherently more susta
 inable.  I will conclude by reflecting on the emerging paradigm of autonom
 ous process innovation and the opportunities it creates for the chemical s
 ciences and industry.
LOCATION:Lecture Theatre 1\, Department of Chemical Engineering and Biotec
 hnology\, West Cambridge Site
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