Marina Iturrate-Bobes

Marina Iturrate-Bobes is a second-year PhD student in Statistics and Operations Research at the University of Oviedo (Spain), supervised by Ignacio Montes and Raúl Pérez-Fernández. She holds a BsC in Mathematics (2022) and an MsC in Teacher Training (2023), both from the University of Oviedo, as well as an MsC in Educational Technology and Digital Competence from the International University of La Rioja (2024). Additionally, she has undertaken postgraduate studies in Data Analysis for Business Intelligence at the University of Oviedo (2025). Her academic background is complemented by certifications in digital teaching skills and bilingual education, along with advanced English proficiency certified by Cambridge University.

Since 2023, she has been working as a full-time substitute lecturer in the Department of Statistics, Operations Research and Mathematics Education at the University of Oviedo, with experience teaching Statistics in both Spanish and English across several undergraduate programs, including Chemistry, Biology, and various Engineering degrees.

Regarding her research background, she has participated in 5 international conferences (Belgian-Polish Workshop 2024, IPMU 2024, ISIPTA 2025, Workshop on Non Parametric Statistics 2025 and EUSFLAT 2025) and 1 national conference (Congreso de Jóvenes Investigadores de la RSME 2023). Additionally, she is the co-author of a JCR-indexed scientific article published in Information Sciences (Q1), as well as of a chapter of a book published in the Springer series Lecture Notes in Computer Science. Moreover, she has completed two international research stays (2024 and 2025) at Ghent University under the supervision of Bernard De Beats, with a total duration of 1 month and 1 week.

Her research focuses on statistical testing. In particular, the core aim of her thesis is to develop new statistical tools that are robust to sampling imprecision, which frequently arises in real-world data due to rounding, measurement errors, censored values, or missing data. More specifically, the objectives of her research include:

1. To study the impact of imprecision on existing goodness-of-fit and symmetry tests.
2. To axiomatically adapt the notion of skewness coefficients to imprecise data.
3. To propose new statistical tests that are resistant to measurement imprecision.
4. To adapt classical visualization for use in settings where data is imprecise.

This research builds upon my previous work on symmetry testing, which was initiated during my undergraduate thesis and later published in Information Sciences, and contributes to a growing need for statistical methods that account for uncertainty in modern data analysis.