Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.
Dr. Steven Kemp is a Senior Lecturer in the Department of Public Law at the University of Girona, Spain. His research focuses on the intersection of criminal law, cybercrime, and criminological analysis, with affiliations to institutions like UNICRI, Open University of Catalonia, and the Institute of Public Security of Catalonia. His work spans digital security, fraud victimization, and sentencing disparities. Current roles: Serra Hunter Fellow, University of Girona (2023–present); Collaborating Teacher, UNICRI (2024–present) Previous roles: Associate Professor, University of Girona (2015–2021); Postdoctoral Researcher, Pompeu Fabra University (2021–2023) and University of Manchester (2021) His research interests include: Cybercrime dynamics during global crises (e.g., pandemic-related fraud trends) Victimization patterns in digital societies, particularly among older adults Legal implications of smart technologies and cybersecurity frameworks Comparative criminal justice systems, especially plea bargaining and sentencing disparities Key trends in his recent publications (2025–2019) highlight: Rising cyberfraud incidents and their societal impacts Interdisciplinary approaches to digital security and legal systems Statistical modeling of crime trends post-COVID-19 Behavioral responses to cybercrime risks Scientific recognition includes: Serra Hunter Fellow He actively collaborates with research groups like the Research Group in the Seminar of Criminal and Criminological Sciences and contributes to public policy initiatives in cybersecurity and fraud prevention.
Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.
Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.
Seth Blumsack is a Professor at the Pennsylvania State University in the Department of Energy and Mineral Engineering and serves as Director of the Center for Energy Law and Policy . He holds an Adjunct Research Professor position at the Carnegie Mellon Electricity Industry Center and is affiliated with the Santa Fe Institute as an External Faculty member. His research spans energy economics , power grid reliability , and complex infrastructure networks . Key projects include: Interdependent natural gas and electricity systems analysis Governance of regional transmission organizations Smart grid consumer behavior studies Power grid reliability tools development He has secured funding from the U.S. National Science Foundation , Department of Energy , Environmental Protection Agency , and private industry. His Best paper award at Hawai’i International Conference on System Sciences (2011) and John T. Ryan, Jr. Fellowship (2011-17) highlight his scientific recognition. Publications emphasize electricity market deregulation , energy infrastructure resilience , and consumer response to smart grid technologies . His work has been cited in major media outlets like The New York Times and The Los Angeles Times , and he has consulted for National Renewable Energy Laboratory , U.S. Department of Energy , and other industry stakeholders.
Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.
Yolanda Vidal Segui is an Associate Professor in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola d'Enginyeria de Barcelona Est (EEBE). Her research focuses on wind energy systems, predictive maintenance, and structural health monitoring of wind turbines. She leads projects in the CoDAlab and WinTurCoM research groups, specializing in data-driven models, condition monitoring, and failure prognosis. Her work integrates machine learning, mathematical modeling, and sensor technology to enhance turbine reliability and energy efficiency. Dr. Vidal holds a PhD in Applied Mathematics and has authored over 350 publications. Her contributions include advancements in SCADA data analysis, vibration-based diagnostics, and AI-driven condition monitoring systems. She has received several accolades, including the WindEurope Technology Workshop recognition and the IFIT Distinction in Mechanism and Machine Science. Her research bridges academia and industry, addressing challenges in offshore wind turbine integrity and maintenance strategies. Active in professional service, she serves on conference committees and editorial boards (e.g., Mechanical Systems and Signal Processing, Wind Energy). Her work emphasizes sustainable energy solutions and has been applied in real-world scenarios like the Alpha Ventus wind farm. She also contributes to educational initiatives, developing innovative teaching materials for engineering students.
Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.
Antonio Molina Fernández is a Professor at the Universidad Politécnica de Madrid , leading the Plant Innate Immunity and Resistance to Necrotrophic Fungi group at the Center for Plant Biotechnology and Genomics (CBGP) . His research focuses on understanding plant defense mechanisms against necrotrophic fungal pathogens, particularly using the Arabidopsis-Plectosphaerella cucumerina interaction as a model system. Key areas include molecular recognition of pathogens, cell wall integrity signaling, and fungal pathogenicity mechanisms. His group investigates how plants sense necrotrophic fungi through receptors like ERECTA and BAK1, regulatory pathways involving MAP kinases and G-proteins, and the role of secondary metabolites in immunity. They also study the genomic basis of fungal lifestyles (pathogenic vs. endophytic) and their interactions with host plants. Current projects include enhancing crop disease resistance via cell wall-derived signals, zinc-mediated immunity, and sustainable agricultural solutions through bioengineering. Molina has secured funding from multiple EU programs, including Horizon-CL6 and MCIN/AEI grants. Notable achievements include identifying the YODA kinase pathway for broad-spectrum resistance and demonstrating the role of cell wall DAMPs in immunity. His work bridges fundamental research with translational applications, aiming to develop biocontrol strategies and climate-resilient crops.
Frank NIELSEN is a Professor at École Polytechnique with expertise in information geometry, data science, and machine learning. He holds a PhD (1996) and HDR (2006) in computer science and has established himself as a leading researcher in geometric approaches to information science. His educational background includes a PhD in computer science (1996) followed by a Habilitation à Diriger des Recherches (HDR) in 2006, the highest academic qualification in France that qualifies one to supervise doctoral candidates. Dr. NIELSEN's research focuses on the Geometric Science of Information , where he develops theoretical frameworks for understanding data through geometric and information-theoretic lenses. His work bridges Computational information geometry Statistical manifold theory Bregman divergences and their applications Machine learning with geometric foundations High-dimensional data analysis He aims to address the challenge of inappropriate data representation in current Data Science by building a theory of Computational Information Geometry to enable Intrinsic Data Science with principled distances. His extensive publication record shows a clear trend toward developing geometric frameworks for understanding statistical divergences, with recent work focusing on Bregman geometry, Fisher-Rao metrics, and their applications in machine learning. His research spans theoretical developments in information geometry to practical implementations like the pyBregMan Python library, demonstrating both theoretical depth and practical relevance. Dr. NIELSEN has made significant contributions through his teaching and publications. He has taught courses at École Polytechnique including INF442, INF517, and INF591. His authored textbooks include Introduction to HPC with MPI for Data Science (2016), A Concise and Practical Introduction to Programming Algorithms in Java (2009), and Visual Computing: Geometry, Graphics, and Vision (2005). He has also edited influential volumes such as Computational Information Geometry for Image and Signal Processing (2016) and Geometric Theory of Information (2014). He actively organizes and participates in academic events, serving on program committees for major conferences including GSI (Geometric Science of Information), CVPR, and ICCV. His work has established him as a key figure in the growing field of geometric approaches to information science.
Dr. Francisco Pérez Fernández is a Professor at Camilo José Cela University (UCJC) in both Psychology and Criminology & Security departments, and holds a PhD in Philosophy & Education Sciences from Complutense University of Madrid (1997). He serves as coordinator of Criminology Degrees (2018-present), former Criminology Department Coordinator (2008-2013), and has been Academic Mentor Accreditation holder since 2008. His international collaborations include Evidentia University (Florida, USA) since 2022. PhD in Philosophy & Education Sciences (UCM, 1997) Postgraduate in Cognitive Sciences, Emotion & Stress (UCM) Accredited Academic Mentor (Spanish Mentoring Network) Civil & Commercial Mediator (UCJC) His research focuses on intersections between History of Psychology , Criminal Psychology , and Cultural Criminology , particularly examining: Crime-cultural system interactions Penitentiary system evolution Popular culture's role in criminal narratives Forensic science historical development Collective psychological phenomena Scientific contributions include establishing the Psychological Autopsy as investigative technology and developing the Method VERA behavioral analysis framework. His publications span: 15+ peer-reviewed articles (2019-2024) 6 book chapters on cultural anthropology 2 edited volumes on violence phenomena 1 book on vampire psychology 1 book on Batman's cultural origins Scientific recognition includes: 2015 In Memoriam Prof. Orozco Acquaviva Award 2016 Real Colegio de Médicos de Sevilla Award 2023 SECCIF Gold Insignia As academic leader, he has: Directed 60+ academic committees Mentored numerous doctoral candidates Coordinated postgraduate programs (2015-2016) Co-edited EduPsykhé journal Reviewed for 10+ international publications Current affiliations: Secretary of Spanish Society for the History of Psychology (SEHP, 2016-present) Member of American Psychological Association (APA) Full member of Spanish Society of Criminology and Forensic Sciences (SECCIF) Editor-in-Chief of EduPsykhé. Journal of Psychology and Education
Cristobal Pagan Canovas is a Permanent Professor (tenure-track) at the Department of English Philology, University of Murcia, where he co-directs the Daedalus Lab and the Murcia Center for Cognition, Communication, and Creativity. He is also a member of the international consortium Red Hen Lab, focusing on multimodal communication research. Education includes: PhD in Ancient and Modern Greek Literature from University of Murcia BA+MA in Classics and BA+MA in English from University of Murcia MA in Classics from University College London His research explores human cognition and communication through interdisciplinary approaches combining humanities and sciences. Primary interests include: Conceptual integration networks in emotional expression Multimodal communication patterns across language, gesture, and prosody Temporal representation in creative artifacts Cognitive foundations of poetic metaphor and verbal art Cultural evolution of integrative patterns in social interactions Recent publications demonstrate consistent focus on temporal cognition, multimodal communication, and creativity across domains including poetry, music, and gesture. Research employs corpus analysis, big data approaches, and cognitive modeling to examine how humans integrate perceptions into meaningful wholes. Scientific awards and fellowships: Ramón y Cajal Grant (elite national scheme) Alexander von Humboldt Fellowship in Quantitative Linguistics EURIAS Fellowship at Netherlands Institute for Advanced Studies FBBVA Leonardo Fellowship Marie Curie Fellowship ENSAYA'10 Award for scientific essay He leads multiple research grants including ERASMUS PLUS KA220-HED (MULTIDATA) and national grants MULTIFLOW and CREATIME. Supervised trainees include postdoctoral researchers (Marie Curie, Juan de la Cierva), MA students, undergraduates, and data scientists. The Daedalus Lab develops interdisciplinary methods to study cognition and communication, while Red Hen Lab enables large-scale multimodal dataset analysis through international collaboration.
Juan Manuel Bartolomé Bartolomé is a Professor in the Department of History at the Faculty of Philosophy and Letters, University of León, Spain. His academic work focuses on Modern History, particularly the social and economic structures of León province during the 18th and 19th centuries. He is affiliated with the research group INDETEHI/HISMECON TEMAS HISTÓRICOS/HISTORIA Y MEMORIA CONTEMPORÁNEA, which specializes in contemporary historical topics and memory studies. Education: Doctorate from University of León (1995) Thesis: "Señores cosecheros, hidalgos y campesinos en el Bierzo leonés análisis estructural y dinámica socioeconómica en el siglo XVIII" Supervised by Dr. Laureano M. Rubio Pérez Bartolomé Bartolomé's research centers on social and economic history of early modern Spain, particularly León province. His work examines living conditions across social classes, consumption patterns, material culture, property ownership, and inheritance practices. He analyzes how wealth levels influenced daily life, domestic spaces, clothing, and social mobility during the transition from the Ancien Régime to modern economic structures. His methodological approach involves correlating material culture with levels of patrimonial wealth, revealing how economic standing shaped social expressions. His publication record demonstrates consistent focus on material culture and social stratification in 18th-19th century León. Bartolomé Bartolomé analyzes how different social groups - peasants, bourgeoisie, nobility, and clergy - expressed their status through consumption and domestic arrangements. His research shows the gradual incorporation of new goods and practices into daily life, with strong connections to wealth levels and social aspirations. The temporal scope of his work (1700-1850) captures significant economic and social transformations in the region. While no specific awards are documented in the available information, his extensive publication record indicates significant scholarly contributions to the field of Spanish social history. His work appears in numerous academic publications and conference proceedings, demonstrating recognition within the historical research community. As a Professor at the University of León, Bartolomé Bartolomé has likely supervised numerous graduate students and participated in academic mentorship, though specific students aren't listed in the available information. His research has been supported by academic funding that enabled extensive archival work with notarial records, particularly property inventories, which form the empirical foundation of his scholarship. He is actively affiliated with the research group INDETEHI/HISMECON TEMAS HISTÓRICOS/HISTORIA Y MEMORIA CONTEMPORÁNEA, which facilitates collaborative historical research at the University of León. This group provides institutional support for his investigations into historical topics and contemporary memory, allowing for interdisciplinary approaches to historical analysis.
Sonia Vanier is a Professor in the Department of Computer Science at École Polytechnique, where she holds multiple leadership positions: Head of the 'Trusted and Responsible AI' Chair (X/Crédit Agricole), Head of the 'Optimization and AI for Mobility' Chair (X/SNCF), Head of 3A, and Scientific Manager of Industrial Relations for both the Department and the Computer Science Laboratory (LIX). She coordinates the GdT OR (Network Optimization) working group and leads the REST (Energy, Services and Transport Networks) research axis of the CNRS GDROD, while serving on its scientific council. Her research develops decision support tools for complex industrial problems through hybrid approaches combining Artificial Intelligence and Operations Research , with focus areas including Network Optimization, ethical AI systems, sustainable computing, and trustworthy AI frameworks. Her work bridges theoretical foundations with applications in telecommunications, transportation, and cybersecurity. Publications demonstrate strong emphasis on optimization techniques (branch-and-price, cutting planes) applied to wireless networks, AI safety, and security challenges. Recent works explore LLM memorization, signomial programming, and multi-commodity flow problems, showing consistent integration of OR with machine learning for industrial-scale problems. Awards: Research Award and Innovation Award, Telecom Valley Association ALOES Orange Innovation Project She leads major industrial-academic partnerships through the Crédit Agricole and SNCF chairs, managing research grants focused on responsible AI deployment and mobility optimization. As Scientific Manager of Industrial Relations, she oversees industry collaborations for LIX laboratory. Affiliated with the Computer Science Laboratory (LIX), she directs the 3A research group and contributes to national initiatives through CNRS GDROD, coordinating research in network optimization and sustainable systems.
Silvia Jiménez Fernández is an Associate Professor in the Department of Signal Theory and Communications at Universidad Autónoma de Madrid. Her research focuses on optimization algorithms, smart grids, renewable energy systems, telemedicine, and machine learning applications. She holds a Ph.D. from Universidad Politécnica de Madrid (2009), supervised by Dr. Francisco del Pozo Guerrero and Dr. Paula de Toledo Heras. Her work integrates interdisciplinary approaches, such as combining evolutionary algorithms with engineering challenges in energy systems and healthcare. Key contributions include advancements in coral reefs optimization algorithms for energy management, machine learning for battery health estimation, and telemedicine systems for chronic disease monitoring. Recent research trends emphasize hybrid learning models in education, multi-objective optimization in renewable energy systems, and risk analysis in smart grids with electric vehicles. She is affiliated with the GHEODE Research Group (Modern Heuristics and Network Design).
