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 M眉nster, working in the Mathematical Optimisation group led by . I completed my PhD at the University of Heidelberg under the supervision of and earned my undergraduate and MSc degrees at Escuela Polit茅cnica Nacional del Ecuador, where I was part of the 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
- , Wirth Benedikt
- Proceedings in Applied Mathematics and Mechanics, PAMM Vol 26 (2026)
- , ,
- Proceedings of the 36th European Safety and Reliability Conference European Safety and Reliability Conference, pp. 2426-2433 (2026)
- , Welker Kathrin
- 7th International Conference on Geometric Science of Information (2025)
- Kalise Dante, , Zhong Zhengang, Morris Kirsten A
- IFAC-PapersOnLine Vol 58, pp. 292-297 (2024)
Professional Activities
- Organiser
- 17/9/2026
- Participant
- 1/9/2026
- Organiser
- 20/7/2026
- Organiser
- 30/6/2026
- Organiser
- 2/6/2026
- Peer reviewer
- 6/2026
Projects
- 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
Contact
Dr
Estefania
Loayza Romero
Lecturer
Mathematics and Statistics
Email: estefania.loayza-romero@strath.ac.uk
Tel: 548 3804