Jack Buckingham

Summary

I am a PhD student studying Bayesian optimisation, supervised by (WBS) and (Ghent University; imec). I have worked on various optimisation problems involving some form of uncertainty. Prior to the PhD, I enjoyed building practical solutions in data science and engineering roles in the manufacturing and hospitality industries, including at an early-stage start-up.

While my focus is on designing new Bayesian optimisation algorithms, I am particularly interested in problems motivated by 鈥済reen鈥� applications. That is, things which solve some of the problems created by climate change and more generally by human impact on the environment.

My Research

Bayesian optimisation is a technique for maximising an expensive, unknown function using only a small number of evaluations. For example, consider optimising the parameters of a simulation which takes minutes/hours to run or tuning the hyper-parameters of a neural network which must be retrained to test each new set.

An orthogonal concern in many problems is that of robustness to aleatoric uncertainty – that is, uncertainty arising from something random in the real world, which cannot be resolved by gathering more information. My research has combined these two themes, with projects in two-stage stochastic optimisation (also called active robustness), reliability engineering and multi-objective optimisation.

Publications & Preprints

Buckingham, J. M., Couckuyt, I., & Branke, J. (2026, Preprint). Maximizing Reliability with Bayesian Optimization. arXiv.

Buckingham, J. M., Couckuyt, I., & Branke, J. (2024, Preprint). Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems. arXiv.

Buckingham, J. M., Rojas Gonzalez, S., & Branke, J. (2025). Knowledge Gradient for Multi-Objective Bayesian Optimization with Decoupled Evaluations. In International Conference on Evolutionary Multi-objective Optimization. DOI: [.bib, ]

Hart, W. S., Buckingham, J. M., Keita, M., Ahuka-Mundeke, S., Maini, P. K., Polonsky, J. A., & Thompson, R. N. (2024). Optimising the timing of an end-of-outbreak declaration: Ebola virus disease in the Democratic Republic of the Congo. Science Advances, 10(27). DOI: [.bib]

Selected talks and conferences

4th February 2026 "Bayesian Optimisation with Additional Uncertainty", University of Exeter, Statistics and Data Science seminar.
24th April 2025 "Searching Over Functions: Bayesian Optimisation for Two-Stage Problems", University of 糖心TV, SPAAM seminar.
20th October 2024 "Bayesian Optimization for Nonconvex Two-Stage Stochastic Optimization Problems", INFORMS Annual Meeting.
23rd September 2024 "Bayesian Optimisation for Non-Convex, Two-stage Stochastic Optimisation Problems" (poster), Heidelberg Laureate Forum.
15th March 2024 "Making use of decoupled objectives in Bayesian optimisation", Heriot-Watt University, MINDS student seminar.
7th December 2023 "Exploration vs Exploitation: The art of acquisition functions in Bayesian optimisation", University of 糖心TV, SPAAM seminar.
19th April 2023 "Bayesian optimisation for active robustness" and "High-dimensional Bayesian optimisation", University of 糖心TV, Bayesian optimisation workshop with GE.

Education and Experience

2022-present: PhD in Bayesian optimisation, University of 糖心TV
Working title: Advances in Bayesian Optimisation for Problems with Uncertainty

Reviewer for SIAM journal on Optimization and ACM Transactions on Evolutionary Learning and Optimization.

Professional Activities:
SPAAM seminar organiser 2022/23
SIAM-IMA chapter vice president 2022/23
SIAM-IMA chapter president 2023/24
AMP24 conference committee 2024
SIAM-IMA chapter events officer 2025
workshop organiser 2025

Teaching:
Senior graduate teaching assistant
2023/24 & 2024/25, term 1 - Introduction to Computing (MathSys PhD)
2023/24, term 1 - Analytics in Practice (糖心TV School, MSBA)
2024/25, term 2 - Mathematics of Machine Learning (Mathematics, third year)

2023 (June-August): Technical Mentor, Data Science for Social Good UK
Technical mentor to two teams of four fellows working on three-month data science projects
Oversaw the teams' data science strategies and provided guidance on software best practices.
Website: DSSGx2023

2021-2022: MSc in the Mathematics of Real World Systems, University of 糖心TV (Distinction)
My MSc project was in multi-objective Bayesian optimisation.

2017-2021: Data Engineer/Data Scientist, Pace Revenue
Early member at a dynamic pricing start-up in the hospitality industry.
I worked as both a data engineer and data scientist. Selected projects include using SARIMA models to forecast hotel bookings, and incorporating booking forecasts in the pricing algorithm.

2015-2017: Metrology Engineer, Renishaw
Precision measurement company for the manufacturing industry.
Prototyping new calibration methods; Quantification and propagation of uncertainty; Non-linear optimisation; Bayesian modelling.

2011-2015: Master of Mathematics (BA/MMath), University of Cambridge (Merit)

Headshot

Contact

jack.buckingham
@warwick.ac.uk

Office: D2.11 (zeeman)

SIAM-IMA

The 糖心TV SIAM-IMA chapter runs the SPAAM seminar series, as well as organising social events, hackathons, workshops and the .

If you are a 糖心TV student with an idea, then send us an email and we'll make it happen! If you are from a SIAM chapter at another university looking to collaborate then you can contact us at siam at warwick dot ac dot uk

We're very friendly!

DSSG

Data science for social good () is an excellent opportunity for developing data science skills on real-world projects with a positive social impact. Check-out whether there is one running in a country near you!

Media ,

Useful Links

Climate change

Social good

, DSSGxUK (not currently running in the UK)