Dr Estefania Loayza Romero

Lecturer

Mathematics and Statistics

Personal statement

I am a Lecturer in Mathematics and Statistics at Strathclyde. Before this, I was a Chapman Fellow in the Mathematics Department at Imperial College London and, prior to that, a postdoctoral researcher at the Cluster of Excellence Mathematics Münster, working in the Mathematical Optimisation group led by Prof. Benedikt Wirth. I completed my PhD at the University of Heidelberg under the supervision of Prof. Roland Herzog and earned my undergraduate and MSc degrees at Escuela Politécnica Nacional del Ecuador, where I was part of the Research Center on Mathematical Modelling (MODEMAT) under the guidance of Prof. Juan Carlos De los Reyes and Prof. Pedro Merino.

My research focuses on computational PDE-constrained optimisation, with particular interest in non-smooth optimisation algorithms and their applications, data assimilation, shape optimisation, and optimisation on manifolds.

Publications

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Publications

Discrete geodesic calculus for complete Riemannian manifolds: Loayza Romero Estefania, Wirth Benedikt; Proceedings in Applied Mathematics and Mechanics, PAMM Vol 26 (2026); https://doi.org/10.1002/pamm.70118
Interval uncertainty propagation on CO₂ emission calculations in road haulage: McIntosh Angus, Loayza Romero Estefania, de Angelis Marco; Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference, pp. 2426-2433 (2026); https://doi.org/10.3850/ESREL2026061419_esrel26-p27770-cd
Numerical techniques for geodesic approximation in Riemannian shape optimization: Loayza Romero Estefania, Welker Kathrin; 7th International Conference on Geometric Science of Information (2025)
Multi-level optimal control with neural surrogate models: Kalise Dante, Loayza Romero Estefania, Zhong Zhengang, Morris Kirsten A; IFAC-PapersOnLine Vol 58, pp. 292-297 (2024); https://doi.org/10.1016/j.ifacol.2024.10.184

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Research

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Professional Activities

LMS Workshop: Mathematical Modelling and Optimisation in Multiscale Problems: Organiser; 17/9/2026
Numerical methods for Hamilton-Jacobi and mean-field PDEs: Participant; 1/9/2026
Computational Methods in Applied Mathematics (CMAM 2026): Organiser; 20/7/2026
Women in Analysis and Optimization on Manifolds: Organiser; 30/6/2026
SIAM Conference on Optimization: Organiser; 2/6/2026
IMA Journal of Mathematical Control and Information (Journal): Peer reviewer; 6/2026

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Projects

Strathclyde Centre for Doctoral Training in data-driven uncertainty-aware multiphysics simulations: De Angelis, Marco (Principal Investigator) Loayza Romero, Karen Estefania (Co-investigator) Kazashi, Yoshihito (Academic) Ruggeri, Michele (Academic) Bi, Sifeng (Academic) Ochnio, Dawid (Post Grad Student) McIntosh, Angus (Post Grad Student); StrathDRUMS aims to train the next generation of interdisciplinary uncertainty quantification specialists who can design and analyse state-of-the-art computational techniques and apply them to real-world challenges. An important aspect of this CDT is the non-deterministic and data-driven modelling approach, which is crucial to overcome the limitations of classical deterministic approaches. A key skill emphasised by StrathDRUMS is the uncertainty quantification, which enables the characterisation, propagation, and quantification of the inevitable uncertainties, providing model predictions over a range of outcomes (distributional, interval, fuzzy, and hybrid) instead of a unique solution with maximum fidelity to a single experiment. Providing data-driven uncertainty-aware model predictions allows for a more comprehensive understanding of the system being studied, improving the accuracy of the predictions and enabling the automatic verification of numerical simulations.; 01-Jan-2023 - 31-Jan-2029

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