Organizers:
Sociedad de Estadística, Investigación Operativa y Ciencia de Datos
Facultad de Ciencias de la Universidad de Extremadura
Image credit: María Escalera Rota
Ana Justel
Universidad Autónoma de Madrid (UAM)
Ana Justel is a Full Professor of Statistics and Operations Research at the Universidad Autónoma de Madrid (UAM). Her research focuses on interdisciplinary data science, with particular emphasis on the analysis of complex and high-dimensional data. Throughout her career, she has developed methodological contributions in areas such as functional data analysis, clustering, and time series analysis, increasingly oriented towards applications in environmental and life sciences.
Her work is characterized by a strong multidisciplinary perspective, combining rigorous statistical methodology with real-world scientific challenges, particularly in environmental and polar studies. Ana Justel has participated in 11 scientific campaigns in Antarctica, contributing to the analysis of complex environmental data in extreme conditions. She has also carried out research stays at prestigious international institutions, including Duke University, the University of Florida, Université Paris-Dauphine, and Université Catholique de Louvain, further strengthening her international profile and collaborative research network. Currently, she serves as co-director of the scientific project MICROAIRPOLAR, where she coordinates research on new statistical methods for studying the dispersion of airborne microorganisms in Antarctica.
Her scientific trajectory has been recognized with the I Margarita Salas Award (2019) for excellence in scientific career, highlighting both the impact and originality of her contributions. In addition to her research achievements, she has played an active role in the academic community through participation in research projects and collaboration networks at both national and international levels.
Mercedes Landete
Miguel Hernández University of Elche (Spain)
Mercedes Landete is a Full Professor of Statistics and Operations Research at Miguel Hernández University of Elche (UMH). Her research focuses on the development of mathematical models and algorithms for combinatorial optimization and integer programming. Throughout her career, she has specialized in a wide range of discrete optimization problems, with particular emphasis on facility location, set packing, routing, rank aggregation, and classification. Her work is characterized by the design of efficient solution approaches to complex logistics and distribution problems, effectively bridging advanced theoretical frameworks with practical applications.
A dedicated leader within the scientific community, Mercedes Landete led the “Resource Management and Optimization” research group at UMH for 12 years. Her influence also extends to the European level, where she served for four years on the executive board of the EURO Working Group on Locational Analysis (EWGLA). At the national level, she has played a key role in the Spanish Statistics and Operations Research Society (SEIO), serving as Vice-President for the Operations Research area for three years. In addition, she is currently Editor-in-Chief of the journal TOP: Transactions in Operations Research, published by SEIO.
Beyond her publications in leading journals such as European Journal of Operational Research and Computers & Operations Research, her scientific contributions are distinguished by the originality of her mathematical formulations for decision-making. In addition, she has contributed to the dissemination of knowledge through publications in outreach science journals. She is strongly committed to advancing the field through the supervision of doctoral theses and active participation in international conference committees.
Stanislav Nagy
Charles University (Czech Republic)
Stanislav Nagy is an Associate Professor of Probability and Mathematical Statistics at Charles University in Prague, Czech Republic. His research focuses on the development of nonparametric and robust statistical methods for the analysis of multivariate and functional data. His work lies at the intersection of statistics, probability theory, geometry, functional analysis, and machine learning, reflecting a strongly interdisciplinary approach.
He is particularly well known for his contributions to the theory and applications of statistical data depth, where he has played a significant role in advancing the field. His research has been disseminated through peer-reviewed articles, book chapters, and edited volumes, contributing to the development of modern statistical methodology and its applications.
Beyond his research contributions, Stanislav Nagy is actively engaged in the international scientific community. He has served as a referee for most major journals in statistics, organized invited sessions at prominent international conferences, and contributed as a member of scientific committees. He has presented his research at numerous scientific conferences worldwide, reflecting both the breadth and impact of his work. He has been awarded an ERC CZ research grant in 2024. He has also served as principal investigator on several projects funded by the Czech Science Foundation. In addition, he has contributed to academic training and mentorship by serving on PhD defense committees.
David Pisinger
Technical University of Denmark
David Pisinger is a Full Professor of Operations Research at the Technical University of Denmark (DTU). His work centers on combinatorial optimization, with a particular emphasis on making transportation and maritime logistics more sustainable and resilient. Throughout his career, he has bridged the gap between theoretical algorithmic innovation and practical decision-making in sectors ranging from railway scheduling and vehicle routing to city-bike sharing and drone applications.
He is widely recognized for developing algorithms that tackle large-scale, computationally demanding problems, such as the knapsack problem and container ship stowage, combining methodological innovation with practical applications. His research has been pivotal in reducing the carbon footprint of global supply chains. Currently, his research explores the intersection of machine learning and decision-making under uncertainty, a focus driven by his 2023 ERC Advanced Grant project, DECIDE.
An active figure in the international OR community, David’s career is marked by numerous distinctions. Recent highlights include being twice selected as a finalist for the EURO Excellence in Practice Award (2018, 2021), one of the leading recognitions in the field.This impact extends to his dedicated academic mentorship, having supervised numerous PhD students, and his leadership of large-scale industrial collaborations that translate mathematical models into real-world environmental impact and operational efficiency.
