Development of machine learning methods for data characterized by functions:
• Near-perfect classification and clustering of stochastic processes.
• Identification of causal relations for functional data.
• Causal modelling of the response to external interventions in medicine, the environment, and the economy.
• Connections between thermodynamics, causality, and machine learning.
Alberto Suárez received the degree of Licenciado (BSc) in Chemistry, specialization in Quantum Chemistry, from the Universidad Autónoma de Madrid, Spain, in 1988, and the PhD in Physical Chemistry from the Massachusetts Institute of Technology (MIT), Cambridge, MA, in 1993. After holding postdoctoral positions at Stanford University (USA), at Université Libre de Bruxelles (Belgium), as a research fellow financed by the European Commission within the Marie Curie “Training and Mobility of Researchers” program, and at the Katholieke Universiteit Leuven (Belgium), he is currently Professor of Computer Science and Artificial Intelligence in the Computer Science Department at the Universidad Autónoma de Madrid (UAM), where he co-directs the Machine Learning Group - Grupo de Aprendizaje Automático (MLG-GAA) [www.eps.uam.es/~gaa]. He has also held appointments as Visiting Scientist at the International Computer Science Institute (Berkeley, CA) at MIT (Cambridge, MA), at the Sorbonne Center for Artificial Intelligence (Sorbonne Université, Paris), and at Université Paris Cité (Paris). He has worked on relaxation theory in condensed media, stochastic and thermodynamic theories of nonequilibrium systems, lattice-gas automata, and automatic induction from data. His current research interests include artificial intelligence, in particular, machine learning, computational statistics, stochastic processes, functional data analysis, causality, and generative AI. He is a member of IEEE, of the European Laboratory for Learning and Intelligent Systems (ELLIS) and a founding member of ELLIS Unit Madrid.