Xiaolu Liu

I am a second-year PhD student at the Mathematics for Real-World Systems Centre for Doctoral Training (MathSys II CDT). I am conducting my research under the supervision of Prof. Juergen Branke. My research focuses on Bayesian optimisation and Bayesian optimal experimental design.

PhD project: Calibration and Optimisation of biopharmaceutical processes

My research topics is generally concern the inverse modelling problem. Our research aims to develop new Bayesian Optimisation algorithms that have improvements over existing methods used in controlling biopharmaceutical manufacturing processes. The experiment is very expensive, and there are many factors that need to be tuned, and we expect our research can help them to explore potential methods to allow them to produce better control strategies and more accurate parameters whilst minimising the costs of the experiments. Currently we are breaking the steps and focus on the optimal experimental design for identifiability problem.

Project

Efficient Data Collection for Establishing Practical Identifiability via Active Learning

Practical identifiability analysis (PIA) plays a crucial role in model development by determining whether available data are sufficient to yield reliable parameter estimates. In bioengineering applications, identifying the minimal experimental design that ensures parameter identifiability is essential in order to reduce cost, time, and resource consumption. In this paper, we introduce E-ALPIPE, a sequential active learning algorithm that recommends new data collection points most likely to establish practical identifiability given the current data, mathematical model and noise assumptions. We empirically evaluate E-ALPIPE against both a benchmark algorithm from the literature and random sampling over three synthetic experiments. Our results show that E-ALPIPE requires up to 50% fewer observations on average to achieve practical identifiability, compared to the strongest competitor, while producing comparable or narrower confidence intervals and more accurate point estimates of system dynamics.

Link: https://www.biorxiv.org/content/10.1101/2025.07.28.667128v1

Education

(2023 - Present) University of Warwick MathSys II CDT : PhD Mathematics of Systems

(2022 - 2023) University of Warwick MathSys II CDT : MSc Mathematics of Systems (Distinction)

(2021 - 2022) London School of Economics : MSc Operations Research & Analytics (Distinction)

(2019 - 2021) University of Leeds : BSc Mathematics (First class)

Research Activities

Talk: Efficient data collection for practical identifiability via active learning, School of Engineering (Alexander Darlington’s group), Coventry (April 2025).
Talk: ALPIPE - Active learning for practical identifiability and parameter estimation, MathSys PhD meeting, Coventry (January 2025)
Poster: Model Identification in Biopharmaceutical Manufacturing, Future Target Healthcare Manufacturing Hub, London (May 2023).
Talk: Active Learning and Bayesian Optimisation for Model Selection, Bayesian Optimisation Reading Group, Coventry (October 2023).

Teaching

Senior Graduate Teaching Assistant for IB3200 Simulations (2024/25 - Term 2)
Senior Graduate Teaching Assistant for CS275 Probability and Statistics (2024/25 - Term 1)

Graduate Teaching Assistant for CS416 Optimisation Methods (2023/24 - Term 2)

Email: xiaoluliu@warwick.ac.uk

Office: D1.13, Zeeman Building