Carme Torras Genís is a Research Professor at the Spanish National Research Council (CSIC), affiliated with the Institute of Robotics and Industrial Informatics (IRI) in Barcelona and the Technical University of Catalonia (UPC). Her career spans over three decades, focusing on robotics, neurocomputing, and artificial intelligence with applications in healthcare and deformable object manipulation. M.Sc. in Mathematics (University of Barcelona, 1978) M.Sc. in Computer Science (University of Massachusetts, 11981) Ph.D. in Computer Science (UPC, 1984) Research Interests : Robotic manipulation of deformable objects (especially textiles) Neurocomputing and machine learning for robotic control Human-robot interaction and assistive robotics Computational topology for cloth state representation Ethics in social robotics and AI Medical applications of robotics for neuromuscular disease assessment Scientific Leadership : ERC Advanced Grant recipient (2016) IEEE and EurAI Fellow Coordinator of Horizon Europe project SoftEnable and former ERC project CLOTHILDE Editorial leadership in IEEE Transactions on Robotics and multiple journals Active in ethics committees and AI policy advisory boards Advisory Committee of Ethics in AI (Catalan Government) Vice-President of CSIC Ethics Committee Member of Royal Academy of Engineering (Spain)
Gil Serrancoli Masferrer is an Associate Professor in the Department of Mechanical Engineering at the School of Engineering of East Barcelona (EEBE), part of the Polytechnic University of Catalonia (UPC). He is affiliated with the InSup - Research Group in Surface Interaction in Bioengineering and Materials Science and the LAM - Multimedia Applications and ICT Laboratory. His work focuses on biomechanics, computational modeling, and telerehabilitation systems development for clinical applications. Dr. Serrancoli's research spans multisolid dynamics, dynamic optimization, movement simulation, and telerehabilitation systems. His expertise lies in applying computational techniques to solve complex problems in orthopedics, gait analysis, and rehabilitation engineering. His work bridges mechanical engineering with biomedical applications, particularly in musculoskeletal modeling and simulation of orthopedic procedures. He has developed novel computational frameworks for estimating internal musculoskeletal loading and muscle adaptation in various conditions, including hypogravity environments. His recent publications demonstrate a strong focus on in-silico modeling of orthopedic procedures, particularly knee osteotomies (proximal fibular osteotomy versus high tibial osteotomy), with detailed analysis of joint pressure redistribution. He has also pioneered the application of machine learning techniques, particularly recurrent neural networks, to biomechanical problems including cycling biomechanics and running dynamics prediction. His work consistently integrates computational efficiency with clinical relevance. Technical Award - OpenSim+ Advanced Workshop March 2024 Accésit del XLV Congreso de la Sociedad Ibérica de Biomecánica y Biomateriales European Society of Biomechanics Travel Award OpenSim Virtual Workshop - Technical Award OpenSim Visiting Scholar 2017 Enginyers BCN 2018 Dr. Serrancoli leads several competitive R&D projects including 'Muvity: a novel physical telerehabilitation system' for vulnerable populations and 'Simulaciones predictivas in silico para cirugías ortopédicas' (Predictive in-silico simulations for orthopedic surgeries). He collaborates extensively with researchers across Europe, particularly with Jordi Torner, Josep Maria Font Llagunes, and Joan Carles Monllau, and has secured funding from national and regional programs including Plan Estatal de Investigación Científica y Técnica y de Innovación. He is actively involved in the BIOMEC - Biomechanical Engineering Lab and the TecSalut - Research Group in Health Technologies, where he contributes to the development of innovative solutions for healthcare challenges, particularly in the areas of telerehabilitation and computational biomechanics for orthopedic applications.
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.
Alejandro F. Villaverde is a Ramón y Cajal research fellow in the Department of Systems & Control Engineering at the School of Industrial Engineering, University of Vigo, Spain. He also serves as a Research fellow at CITMAga since 2022. Previously, he worked as a postdoctoral researcher at IIM-CSIC from 2016-2020. His research focuses on the modeling of dynamical systems with particular emphasis on biological applications. Villaverde earned his PhD in Systems and Control Engineering from University of Vigo between 2005 and 2009. His academic career has centered at Spanish institutions with a strong interdisciplinary approach bridging engineering, mathematics, and biology. His primary research interests include systems biology, control theory, and mathematical modeling, with specialized expertise in structural identifiability, observability analysis, and computational tools for dynamic modeling of biological systems. Villaverde's work addresses fundamental challenges in building reliable mathematical models of complex biological processes, with applications spanning immunology to microbial communities. His theoretical contributions have practical implications for improving model reliability and predictive power in biological research. Villaverde has published extensively in top journals including PLOS Computational Biology, Bioinformatics, and IEEE/ACM Transactions on Computational Biology. His recent publications (2023-2025) reveal a consistent research trajectory focused on developing theoretical frameworks for biological model analysis, creating practical software tools, and applying these methods to cutting-edge problems. His work shows particular strength in identifying and addressing fundamental limitations in modeling approaches, especially regarding parameter identifiability and model observability constraints. Among the top 2% Scientists Worldwide 2024 (Stanford University list) Recognition as one of the EEI's top valued instructors at University of Vigo's School of Industrial Engineering Villaverde leads multiple significant research projects including DYNAMO-bio (funded by Ministry of Science, Innovation and Universities), SICOMORO (focusing on symmetries in biological communities), and PREDYCTBIO. His group actively develops open-source software tools such as STRIKE-GOLDD for structural identifiability and observability analysis. The laboratory, part of the BICO research group, includes several researchers and students working on various aspects of dynamic modeling in biology, with recent additions including Mahmoud Shams Falavarjani, Adriana González Vázquez, and multiple interns working on specialized projects.
Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework
Elisa Santana Monagas is a Part-Time Substitute Professor at the Department of Psychology, Sociology and Social Work, Universidad de Las Palmas de Gran Canaria. She is affiliated with the Institute of Textual Analysis and Applications (IU IAText) and the ICP2 Project research group. Her research focuses on teacher-student communication styles, motivational messages in educational contexts, and their impacts on student motivation, academic performance, and psychological well-being. She employs sentiment analysis and longitudinal studies to explore reciprocal relations between teaching practices and student outcomes, particularly in secondary education settings. Her key research interests include developmental and educational psychology, with an emphasis on autonomy-supportive communication, relatedness in teacher-student relationships, and the role of emotional engagement in learning. She has contributed to understanding how classroom environments influence covitality, boredom reduction, and adaptive behaviors among adolescents. Notable contributions include analyzing reciprocal dynamics between motivational appeals and academic outcomes, gamification strategies in higher education, and the validation of assessment tools for teacher feedback. Her work bridges theoretical frameworks like self-determination theory with methodological innovations in sentiment analysis and needs-supplies fit processes. Elisa collaborates with interdisciplinary teams in the GIR IATEXT group, focusing on didactics and learning in specific educational contexts. She actively publishes in peer-reviewed journals and presents at conferences on topics such as teacher enthusiasm, empathetic communication, and the design of pedagogical interventions to enhance student well-being.
Nuria Sebastian Galles is a Full Professor at the Universitat Pompeu Fabra (UPF), affiliated with the Department of Engineering and the UPF Center of Studies in Natural and Artificial Intelligence. Her career includes roles as Associate Professor (University of Barcelona, 1988-2002) and Full Professor (since 2002). She leads the Speech Acquisition and Perception (SAP) Research Group and coordinates major projects such as the BRAINGLOT consortium on bilingualism and cognitive neuroscience. Dr. Galles has received notable awards including the James S. McDonnell Foundation Award and ICREA Academia Prize, and served on the European Research Council Scientific Council. Her research focuses on cognitive neuroscience, bilingualism, and infant language development, leveraging EEG, brain imaging, and behavioral studies. Education: PhD in Experimental Psychology (University of Barcelona, 1986), postdoctoral training at Max Planck Institute and LSCP-CNRS (Paris). Visiting scholarships include University of Pennsylvania, University College London, and University of Chicago. Research Interests: Bilingualism's impact on cognition, neurobiological foundations of language acquisition, infant speech perception, and social cognition. Current projects include ERC-funded studies on attention-language links and cognitive effects of walnut consumption in adolescents. Publications: Over 100 peer-reviewed articles in journals like Science , PNAS , and Developmental Science , with a focus on bilingualism, phonetic categorization, and social hierarchy processing in infants. Editor of Developmental Science and Language Learning and Development . Grants & Teams: ERC Advanced Grant (Under Control), Consolider-Ingenio 2010 (BRAINGLOT). Lab collaborations include multi-country studies on traffic pollution’s cognitive impact and walnut-based dietary interventions.
Scott Nelson is an Associate Professor of Finance at the University of Chicago Booth School of Business. His research bridges consumer credit markets, regulatory frameworks, and behavioral economics, with a focus on how information asymmetries and algorithmic decision-making shape market outcomes. He has contributed to understanding the impacts of the 2009 CARD Act, eviction protections in housing markets, and fairness in credit scoring systems. PhD in Economics, Massachusetts Institute of Technology BA (summa cum laude) in Economics and Mathematics, Yale College Nelson's work employs diverse data sources, including credit reports, court filings, and tax records, combined with structural models to analyze consumer and firm behavior. Key themes include regulatory efficiency, validity disparities in predictive models, and the welfare implications of policy interventions. His articles reveal trends in algorithmic regulation (2025), eviction dynamics (2025), credit scoring disparities (2024), and public finance impacts on Chinese real estate (2023). These publications highlight interdisciplinary methodologies integrating economics, law, and data science. Scientific awards include the AQR Top Finance Graduate Award (2018) and National Science Foundation Graduate Research Fellowship. He has held postdoctoral roles at the Consumer Financial Protection Bureau/Princeton University and visiting research positions at the Federal Reserve Bank of Boston.
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).
Jose Miguel Espi Huerta is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. He is an active researcher in power electronics and control systems, contributing significantly to grid-connected converters, renewable energy integration, and digital control techniques. His research interests include: Power Electronics and Inverter Control Predictive and Robust Control Strategies Renewable Energy Systems (Photovoltaic and Wind) Induction Heating Technologies Remote and Web-Based Educational Labs The analysis of his recent publications reveals a strong focus on improving the efficiency and reliability of grid-connected power converters using advanced control methods such as predictive current control and MPPT strategies. His work spans both industrial applications and academic education, particularly in developing remote laboratory platforms for control systems. Scientific awards and honors: No awards listed in the provided text. He has supervised academic theses and is affiliated with the LEII (Laboratory of Industrial Electronics and Instrumentation) research group. While no formal grants are listed, his extensive publication record indicates sustained research activity. He has contributed to the development of educational tools such as air levitation systems accessible via PLC and web interfaces, promoting innovative teaching methods in engineering education. The LEII research group focuses on industrial electronics, instrumentation, and power systems, providing a collaborative environment for applied research in energy conversion and control technologies.
PASCUAL ALVAREZ GOMEZ is a Professor at the University of Cádiz, affiliated with the Department of Industrial Engineering and Civil Engineering. His research focuses on Ground Engineering and Thermal Engineering, with a particular emphasis on geothermal heat pumps and thermal performance modeling. He is associated with the TEP221 Thermal Engineering research group and contributes to the PAIDI area of Production Technologies. Education: PhD in Industrial Engineering from the University of Cádiz (2014), with a thesis on "Vertical Ground Heat Exchanger Simulation Model for Geothermal Heat Pumps" Research: Specializes in hybrid thermal modeling, CFD analysis for evaporation rates, and machine learning applications in corrosion prediction His publications highlight advancements in geothermal systems, low-concentration photovoltaics, and biogas environment corrosion analysis. He employs both analytical and computational fluid dynamics (CFD) approaches for energy efficiency improvements.
Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.
Luis Miguel Bergasa is a Full Professor at the Department of Electronics, University of Alcalá (UAH), with a career spanning over 25 years. He leads the RobeSafe Lab (since 2010) and serves as Director of Digital Transformation at UAH (since 2022). His academic roles include heading the Department of Electronics (2004–2010) and coordinating multiple educational programs. He teaches Perception Systems (Master in Industrial Engineering) Intelligent Control Systems (Computer Science) Computer Vision (Computer Science) His research focuses on Perception Systems for Intelligent Vehicles , emphasizing driver behavior analysis, scene understanding, and sensor fusion via deep learning. He has authored over 300 papers and holds 9 patents. Notable recognitions include being ranked #81 in Computer Science (Spain) by Research.com (2025) and receiving 30+ awards in Robotics/Automotive fields. Recent publications highlight advancements in Transformer-based driver action recognition (2025) Infrastructure-vehicle cooperative frameworks (2025) 3D semantic segmentation for autonomous perception (2024) Simulation-to-reality gap bridging (2024) Scientific Leadership Senior Editor, IEEE Transactions on ITS (2025) International Program Committee roles in 15+ conferences (2024–2025) Co-founder of Vision Safety Technologies Ltd (2009–2016) He supervises 9 active PhD students and has advised 14 former PhD candidates. His group collaborates with institutions in Germany, USA, China, and Spain, including KIT, UC Berkeley, and Northwestern Polytechnic University.
Alberto Ros is a Full Professor at the University of Murcia , Spain, in the Computer Engineering Department (DITEC) . His work focuses on cache coherence , memory hierarchy designs , memory consistency , and processor microarchitecture , with over 100 peer-reviewed publications. Dr. Ros earned his MS (2004) and PhD (2009) in Computer Science from the University of Murcia. He interned at the School of Informatics, University of Edinburgh , and held postdoctoral positions at the Technical University of Valencia and Uppsala University . He is an IEEE Senior Member . Research interests include optimizing hardware for multicore systems. His work spans cache coherence protocols, transactional memory, speculative execution, and data/instruction prefetching techniques. He led the ERC Consolidator Grant (2018) and ERC Proof of Concept Grant (2023) to improve multicore architecture performance. Recent publications emphasize hardware transactional memory efficiency, speculative execution, and secure cache systems. Notable works include cache locking, memory dependency prediction, and fine-grain coherence protocols. Scientific awards : Inducted into the MICRO Hall of Fame ISCA Hall of Fame 27 HiPEAC paper awards (MICRO, ISCA, HPCA, ASPLOS) Winner, ML-based Data Prefetching Competition Winner, 1st Instruction Prefetching Championship IEEE MICRO TopPicks for ISCA'17, MICRO'21 (honorable), MICRO'16 (honorable) Best paper awards at HiPC'16, FORTE'16 Honorable mention at HPCA'24 Nomination at ISCA'22 Grants as Principal Investigator include ERC Proof of Concept (2023) ERC Consolidator (2018) Europe Excellence (2018) Seneca Foundation, Young Leaders in Research (2014) . Dr. Ros is affiliated with the Computer Architecture and Parallel Systems Group (CAPS) at the University of Murcia and previously with UPMARC at Uppsala University.
