2 - 6 September 2024



The Sargent Centre is delighted to announce we are hosting our third edition summer school, on the topic of Data-driven optimisation: Bayesian optimisation.

The school will consist of preliminary tutorials on Gaussian processes and Bayesian optimisation. Attendees will gain a foundation in the underlying principles, and learn probabilistic modelling and sequential decision-making. With hands-on exercises, students will develop their understanding of how Gaussian processes serve as powerful tools for modelling uncertainty and guiding decision-making processes.

As the program develops, participants will explore Bayesian optimisation from different experts, going into diverse applications across high-dimensional spaces, molecular discovery, real-time optimisation, and safety-critical systems in science and engineering.

From navigating the complexities of multi-fidelity Bayesian optimisation to harnessing the potential of batch Bayesian optimisation for high-throughput experimentation, students will discover how Bayesian techniques can be used both in industry and academia.

Don't miss out on a place. Join us and REGISTER TODAY!

Further information on the PSE Research Day on the 6th of September will be coming soon.


Summer School 2024


Registration information

Registration Fee: 

  • Early bird Industrial :                                          £490      (until 30 June 2024, from 1 July 24 - £550)
  • Early bird Academic :                                          £350      (until 30 June 2024, from 1 July 24 - £400)
  • PSE Research Day only (6 September 2024):       £75 

PLEASE NOTE: The PSE Research Day fee is included in the registration fee for the week. There is no need to book the PSE day separately if you sign up for the week.

To register, please follow this link .


Full refunds, less 10% administration fee, will be given for cancellations that are received in writing on or before 31 July 2024 . After this date, until 11 August 2024, participants who cancel will receive refunds of 50% of the registration fee paid. No refunds will be provided for cancellations received after 12 August 2024. 

Substitutions may be made at any time, whilst a valid place is held. The organiser cannot accept liability for costs incurred in the event of a course having to be cancelled as a result of circumstances beyond its reasonable control.  

Speaker Bios

Joel Paulson is the Assistant Professor of Chemical and Biomolecular Engineering at The Ohio State University (OSU) where he is also a core faculty member of the Sustainability Institute (SI) and an affiliate of the Translational Data Analytics Institute (TDAI). He joined OSU in 2019 after completing his Ph.D. at the Massachusetts Institute of Technology (MIT) in Chemical Engineering and a subsequent postdoctoral appointment at the University of California, Berkeley in the area of systems and control theory. He has received several awards including the Best Application Paper Prize from the 2020 IFAC World Congress, the NSF CAREER Award, and the OSU Lumley Research Award. His research interests are in data-driven optimization, physics-informed machine learning, and model predictive control. Methods developed by the Paulson group are being applied to a variety of next-generation biochemical systems including continuous pharmaceutical manufacturing, chemical looping combustion, sustainable battery systems, and non-equilibrium plasma jets.

Dr Lauren Ye Seol Lee, Assistant Professor at University College London and Honorary Research Fellow at Imperial College London. She joined the Molecular Systems Engineering research group in 2017 and received her Ph.D. in 2022. During her PhD, she focused on the development of computer-aided molecular and process design frameworks aiming to provide a reliable path to accelerate the identification of optimal solvents. Awards include a Global Visiting Fellowship (2022) at Seoul National University, a British Federation of Women Graduates (2020) and Roger Sargent scholarship (2018). Before joining the group, she worked as a Research Engineer in Hanwha Ocean Co., Ltd. She led an R&D project for the development of a new concept of offshore oil production process. As a research associate, she worked on a project of the PharmaSEL-Prosperity partnership, which focuses on the data-driven design of reacting solvents for drug production processes. Her research interests include knowledge-based and data-driven molecular/chemical product design, spanning the development of design method, reaction optimisation for small molecule design, property prediction techniques and optimisation algorithms.

Professor Ruth Misener is the BASF / Royal Academy of Engineering (RAEng) Research Chair in Data-Driven Optimisation (2022-27) at the Imperial Department of Computing. Foundations of her research are in numerical optimisation and computational software. Her applications focus on optimisation challenges arising in industry, e.g. scheduling in manufacturing or experimental design in chemicals research. Ruth also works at the interface of operations research and machine learning. Ruth received the 2017 Sir George Macfarlane Medal as the overall winner of the RAEng Engineers Trust Young Engineer of the Year Award. She received the 2023 Roger Needham Award from the British Computing Society for her research contributions. She has received 5 best paper awards (2013 Journal of Global Optimization Best Paper, 2014 David Smith Award from the American Institute of Chemical Engineers, 2020 International Conference on Autonomous Agents & Multi-Agent Systems Best Demo, 2021 Conference on the Integration of Constraint Programming, Artificial Intelligence, & Operations Research Best Paper, 2021 Rosenbrock Prize for the best paper in Optimization & Engineering). Her Optimisation and Machine Learning Toolkit (OMLT) won the 2022 COIN-OR Cup as the "best contribution to open-source operations research software development".

Dr Austin Mroz is an Eric and Wendy Schmidt AI in Science Postdoctoral Fellow at Imperial College London. Prior to this, Austin received her PhD under the advisement of Prof. Christopher Hendon in 2021. Here, she focused on developing open source postprocessing software to enhance data analysis of solid-state and molecular calculations and modelling defects in MOFs. Austin then joined the Jelfs Group at Imperial College London as a postdoctoral researcher, where she increased her focus on porous materials design and discovery – specifically developing methods and algorithms to realize novel porous liquids. During this time, Austin also led (under the supervision of Prof. Kim Jelfs) an industry collaboration, where the team demonstrated a fully closed-loop materials discovery workflow driven by Bayesian optimization. Inspired by this industry collaboration, and as an AI in Science Postdoctoral Fellow, Austin is now focusing on developing the framework and algorithms that facilitate and underpin closed-loop, autonomous materials discovery initiatives. Her research interests include, materials discovery methods and algorithms, generative AI, symbolic ML, and data-driven optimisation.

Dr Henry Moss is a Departmental Early Career Advanced Fellow (DECAF) in the Institute of Computing for Climate Science. HIs primary focus is on developing scalable Bayesian ML models that help scientists understand the world around us and he has worked on applications in engineering, biology, chemistry, and physics.

Dr Calvin Tsay is a Lecturer (Assistant Professor) in the Department of Computing at Imperial College London. Prior to his appointment as lecturer, he was an EPSRC David Clarke Fellow and Imperial College Research Fellow. His research focuses on machine learning and computational optimisation, with applications in energy and process systems engineering. Calvin received his PhD degree in Chemical Engineering from the University of Texas at Austin and his BS and BA from Rice University (Houston, TX). He is the recipient of the 2021 CPAIOR Conference Distinguished Paper Award, the 2022 COIN-OR Cup, and the 2022 W. David Smith, Jr. Graduate Publication Award from the CAST Division of the American Institute of Chemical Engineers.

Dr Antonio Del Rio Chanona is head of the Optimisation and Machine Learning for Process Systems Engineering group at the Department of Chemical Engineering, and the Centre for Process Systems Engineering, Imperial College London. His research focuses on developing and applying computer algorithms from the area of optimization, machine learning and reinforcement learning to engineering systems. The applied branch of his research looks at bioprocess control, optimization and scale-up. He holds a PhD from the Department of Chemical Engineering and Biotechnology at the University of Cambridge, where he received the Danckwerts-Pergamon award for the best PhD dissertation of 2017 and received my undergraduate degree from the National Autonomous University of Mexico (UNAM).


Practical Information

Venue:    Imperial College London
               Roderic Hill Building
               South Kensington Campus
               London SW7 2BB

For the campus website, use this link.

Closest Underground Stations are South Kensington or Gloucester Road.

Affordable summer accommodation, walking distance from the Roderic Hill building, can be enquired about via this link. The accommodation to enquire about is Prince's Garden or Beit Hall.

Accommodation can also be booked via the below websites. 
Please note that Imperial College has no affiliations with these websites and other websites are available.