Keynote Speakers

Bio: María Dolores Romero Morales is a Professor in Operations Research at Copenhagen Business School. Her areas of expertise include Data Science, Supply Chain Optimization and Revenue Management. In Data Science she investigates explainability/interpretability, fairness and visualization matters. In Supply Chain Optimization she works on environmental issues and robustness. In Revenue Management she works on large-scale network models. Her work has appeared in a variety of leading scholarly journals, including European Journal of Operational Research, Management Science, Mathematical Programming and Operations Research, and has received various distinctions. Currently, she is Editor-in-Chief to TOP, the Operations Research journal of the Spanish Society of Statistics and Operations Research, member of the Editorial Board of the International Journal of Production Research, and an Associate Editor of Journal of the Operational Research Society and the INFORMS Journal on Data Science.

Dolores has received funding from the EU as well as national research councils to conduct her research. She has worked with and advised various companies on these topics, including IBM, SAS, KLM and Radisson Edwardian Hotels, as a result of which these companies managed to improve some of their practices. SAS named her an Honorary SAS Fellow and member of the SAS Academic Advisory Board. 

Dolores joined Copenhagen Business School in 2014. Prior to coming to Copenhagen Business School, she was a Full Professor at University of Oxford (2003-2014) and an Assistant Professor at Maastricht University (2000-2003). She has a BSc and an MSc in Mathematics from Universidad de Sevilla and a PhD in Operations Research from Erasmus University Rotterdam.

Abstract: An Operations Research Perspective Dolores Romero Morales Copenhagen Business School Denmark State-of-the-art Artificial Intelligence (AI) and Machine Learning (ML) algorithms have become ubiquitous across industries due to their high predictive performance. However, despite their widespread deployment, these models are often criticized for their lack of transparency and accountability. Their “black-box” nature obscures the reasoning behind decisions, limiting trust and hindering their integration in critical, data-driven decision-making processes. Moreover, algorithmic decisions can perpetuate or even amplify societal biases, leading to unfair and discriminatory outcomes. This concern is especially pressing in high-stakes domains such as healthcare, criminal justice, and credit scoring, where unfair model behavior can significantly impact individuals' lives. In this presentation, we will navigate through a collection of Multi-Objective Optimization models that train ML models with enhanced explainability and fairness.

Bio: Fernando Pérez Cruz received a Ph.D. in Electrical Engineering in 2000 from the Technical University of Madrid. He is a Sr Advisor on Innovation at the Bank for International Settlements and an adjunct Professor at the Computer Science department at ETH Zurich. He was the Chief Data Scientist at the Swiss Data Science Center. He has been a member of the technical staff at Bell Labs and an Associate Professor at the University Carlos III in Madrid. He has work at Princeton University under a Marie Curie Fellowship and a Research Scientist at Amazon. He has also held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), BioWulf Technologies (New York). His current research interest lies in machine learning and its application to economics, sciences, and engineering. He has an h-index of 42.