We are thrilled to welcome Dr Christian Blum, Professor Marc Schoenauer and Professor Maria Amparo Alonso Betanzos as our keynote speakers.
Rethinking Efficiency in Machine Learning
Abstract
The success of Artificial Intelligence (AI) has so far relied on developing increasingly precise models. However, this has come at the cost of greater complexity, requiring a higher number of parameters to estimate. As a result, model transparency and explainability have diminished, while the energy demands for training and deployment have skyrocketed. It is estimated that by 2030, AI could account for more than 30% of the planet’s total energy consumption.
In this context, green and responsible AI has emerged as a promising alternative, characterized by lower carbon footprints, reduced model sizes, decreased computational complexity, and improved transparency. Various strategies can help achieve these goals, such as improving data quality, developing more energy-efficient execution models, and optimizing energy efficiency in model training and inference. These innovation approaches highlight the potential of green AI to challenge the prevailing paradigm of ever-growing models.
Speaker Bio
Amparo Alonso Betanzos is a Full Professor in the area of Computer Science and Artificial Intelligence at CITIC-University of A Coruña (UDC), where she coordinates the LIDIA group (Artificial Intelligence R&D Laboratory). She is also a Professor II at the Department of Psychology, NTNU Tröndheim. Her research lines are the development of Scalable Machine Learning models, Reliable and Explainable Artificial Intelligence, and Green AI, among others.
She was formerly President of the Spanish Association of Artificial Intelligence (2013-21). She is a Senior Member of IEEE and ACM and Royal Spanish Academy of Exact, Physical, and Natural Sciences. She has participated as a member of the Working Group on AI of the Spanish Ministry of Science, Innovation, and Universities, collaborating in drafting the Spanish R&D&I Strategy in Artificial Intelligence in 2018. She is currently a member of CAIA, the Advisory Council on Artificial Intelligence of the Ministry of Digital Transformation and Public Function of the Government of Spain, since 2020, as well as a Member of the Spanish Research Ethics Committee of the Ministry of Science, Innovation and Universities of the Government of Spain, since 2023.
Large Language Models as Assistants for Improving Metaheuristics
Abstract
Large Language Models (LLMs) are advanced AI systems that are pre-trained on vast amounts of text data to understand and generate text, whether in natural language or code. Based on transformer architectures, a deep learning framework, they analyze input prompts and produce contextually relevant responses. LLMs excel in a wide range of tasks, including answering questions, summarizing information, writing code, generating creative content, and even demonstrating progress in solving mathematical problems. Notable examples include OpenAI’s GPT models, Anthropic’s Claude, Google’s Gemini, and open-weight models such as Meta Llama and DeepSeek R1. Recently, the use of LLMs has been explored across a diverse range of applications. Naturally, researchers in optimization, particularly in metaheuristic algorithms, have considered how LLMs can be utilized to enhance their methods. In this talk, I will discuss our efforts over the past year in utilizing LLMs as assistants to improve metaheuristics.
Speaker Bio
Dr. Christian Blum is a Senior Research Scientist at the Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council (CSIC) in Bellaterra, Spain. Previously, from 2012 to 2016, he served as an Ikerbasque Research Professor at the University of the Basque Country in San Sebastian, Spain. He earned a PhD in Applied Sciences from the Free University of Brussels in 2004 and a Diploma (equivalent to a Master’s degree) in Mathematics from the University of Kaiserslautern, Germany, in 1998. His research primarily focuses on swarm intelligence techniques for optimization and control, as well as the hybridization of metaheuristics with other approaches to tackle large-scale optimization problems in fields such as bioinformatics and transportation. Over the past 25 years, Dr. Blum has (co-)authored more than 250 publications in international journals, books, and peer-reviewed conference proceedings. His work has received approximately 21,000 citations, with a current H-index of 45 (Google Scholar). In addition to his research, Dr. Blum serves as an editor for Computers & Operations Research, overseeing heuristics and metaheuristics, and as an associate editor for journals such as the Artificial Intelligence Journal and Engineering Applications of Artificial Intelligence. Throughout his career, he has received various research and supervision awards, including the IEEE Transactions on Evolutionary Computation (IEEE TEC) Outstanding Paper Award and the 2021 SEIO-BBVA award for the best methodological contribution in Operations Research, a prestigious Spanish national award.
Marc Schoenauer
Institut national de recherche en sciences et technologies du numérique (INRIA)
https://www.lri.fr/~marc/
Title of the talk yet to be announced
Speaker Bio
Marc Schoenauer is Principal Senior Researcher (Directeur de Recherche de Classe Exceptionnelle) with INRIA, Emeritus since May 2024. He graduated at Ecole Normale Supérieure (1975), then got a PhD in Applied Maths at Paris 6 U. (1980). He has been Junior Researcher (Chargé de Recherche) with CNRS (1980-2001), at CMAP (the Applied Maths Lab.) at Ecole Polytechnique. He then joined INRIA, and in 2003 founded the TAO team (Machine Learning and Optimization) at INRIA Saclay together with Michèle Sebag. He has been Head of Research of the Saclay Inria branch (2010-2016) and Deputy Research Director in charge of AI at INRIA (2020-2024).
Marc Schoenauer has been working since early 90s at the interface between Evolutionary Computation (EC) and Machine Learning (ML). He is author of more than 200 papers in journals and major conferences. He is or has been (co-)advisor of 42 PhD students. He has been Chair of SIGEVO (2015-2019); Founding President (2015-2021) of SPECIES, the Society for the Promotion of Evolutionary Computation In Europe and Surroundings that runs the EvoStar series of confeences; Founding president (1995-2002) of Evolution Artificielle, the French Society for Evolutionary Computation; And president of AFIA (2002-2004), the French Association for Artificial Intelligence.
He has been Editor in Chief of Evolutionary Computation Journal (2002-2009, now on the Advisory Board), is or has been in the Editorial Board of other prestigious journals in EC: IEEE Trans. on EC (1996-2004), TCS-C (2001-2006), GPEM (1999-2017), ASOC (2000-2014), and the recent (2019) ACM-TELO. He is Action Editor of Journal of Machine Learning Research (JMLR) since 2013.
Last but not least, he seconded Cédric Villani in writing his report on the French Strategy for AI delivered to Pdt Macron in March 2018.