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.
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.
Karina Gibert is a Full Professor at Universitat Politècnica de Catalunya (UPC), specifically at the Faculty of Computer Science of Barcelona (FIB) in the Department of Statistics and Operations Research. With a permanent teaching position since 1990, she contributes to research and education with a focus on Data Science, Artificial Intelligence, Explainable AI, and Ethics in AI. Full Professor, UPC (since 1990) PhD in Informatics Engineering Postgraduate in Higher Education Teaching Director and co-founder of IDEAI research center Dean of the Official Professional College on Informatics Engineering of Catalonia Active in bridging the gender gap in STEAM through multiple initiatives Her research focuses on extracting strategic knowledge from data and intelligent systems with ethical and explainable perspectives. She has led various projects including Diet4You for personalized diets, INSESS-COVID19 for social vulnerability analysis, Top Rosies Talent for female AI development, and ciutadanIA for AI culture promotion. Her work spans health, environment, sustainability, tourism, and social technology applications. As an editor of Environmental Modeling and Software journal and active academic, she contributes to international conferences, working groups, and research collaborations. Her service includes membership on various ethics and AI strategy committees and advisory boards. Women Tech Award 2023 National Informatics Engineering Award in Digital Dissemination 2022 Ada Byron Award 2022 Honorific Mention of Creu Casas award 2021 donaTIC 2018 Award Finalist at AMETIC Awards 2021 Finalist of European Social Services Awards 2021
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
Nicholas Polson is the Robert Law, Jr. Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His academic career centers on Bayesian statistics with applications in financial econometrics and machine learning. Polson's research interests span Bayesian statistics, financial econometrics, Markov chain Monte Carlo methods, particle learning, and deep learning applications in finance. His work has significantly contributed to understanding stochastic volatility models and developing new algorithms for Bayesian inference. He has pioneered applications of deep learning in asset pricing, portfolio management, and financial prediction, demonstrating how neural networks can detect complex patterns invisible to traditional financial models. His recent publication trends reveal a strong focus on integrating deep learning with financial econometrics, particularly in developing characteristics-sorted factor models, portfolio optimization techniques, and explaining the performance differences between active and passive investment strategies. His work consistently bridges theoretical statistical methods with practical financial applications, with a particular emphasis on nonlinear modeling and high-dimensional data analysis. His article 'Bayesian Analysis of Stochastic Volatility Models' was named one of the most influential articles in the 20th anniversary issue of the Journal of Business and Economic Statistics Polson teaches courses including 'Bayes, AI and Deep Learning' and 'Business Statistics' at Chicago Booth, with scheduled offerings for both 2024-2025 and 2025-2026 academic years. His work has been featured in Chicago Booth Review, where he has contributed insights on statistical analysis in chess, machine learning applications in money management, and the odds of cheating in competitive settings. His research demonstrates the powerful intersection of Bayesian statistics, financial modeling, and modern machine learning techniques.
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.
Luis Merino Cabañas is a Professor at the Universidad Pablo de Olavide , affiliated with the Deporte e Informática department and leading the SRL Service Robotics Laboratory . His research focuses on robotics, systems engineering, and automation, with a specialization in human-robot interaction and path planning. Education : PhD in Systems Engineering from the Universidad de Sevilla (2007), where his thesis explored cooperative perception techniques for multiple unmanned aerial vehicles in forest fire detection. Research Trends : Recent work (2023–2025) emphasizes 3D path planning, sensor fusion (LiDAR, radar, inertial systems), neural distance fields for safe navigation, and socially aware robotics. His studies integrate AI, genetic programming, and multi-modal perception for applications in construction, healthcare, and GNSS-denied environments. Labs & Teams : He leads the SRL Service Robotics Laboratory , contributing to projects like the Skyeye team and BIM2ROS integration for construction robotics.
Julian Fierrez is a Full Professor at the School of Engineering, Universidad Autonoma de Madrid. With an h-index of 74 and over 20,000 citations, his work spans biometrics, signal/image processing, artificial intelligence, and human-computer interaction. Key research areas include: Biometric anti-spoofing and DeepFakes detection Mobile and behavioral biometrics Bias/fairness in AI systems Biometric applications in e-health and education Security in multimodal biometric systems His recent publications show strong focus on deep learning applications for biometric security, with specific subfields including fake detection, keystroke authentication, facial analysis for Parkinson detection, and privacy-preserving AI. He serves as Associate Editor for multiple IEEE and Elsevier journals. Scientific distinctions include: IAPR Young Biometrics Investigator Award (2017) Miguel Catalan Award to Best Researcher under 40 (2017) EURASIP Best PhD Award (2012) EBF European Biometric Industry Award (2006) Prof. Fierrez leads the BiDA Lab and supervises students like Ruben Tolosana and Aythami Morales. Current projects include BBforTAI (Biometrics and Behavior for Unbiased & Trustworthy AI) and PRIMA (Privacy Matters). He also contributes to standardization efforts in biometric evaluation.
Stefano Teso is an Assistant Professor at the University of Trento (UNITN), actively engaged in research related to interpretable and trustworthy machine learning. His work focuses on integrating human explanations into the learning process and improving model transparency. His research interests center around explainable AI (XAI) and interactive machine learning, particularly through frameworks that incorporate explanatory supervision. Projects such as awesome-explanatory-supervision , caipi , and calimocho highlight his focus on building models that provide understandable reasoning, turning local explanations (e.g., LIME) into globally consistent and trustworthy predictors using self-explaining neural networks. The absence of listed publications prevents detailed trend analysis, but the thematic consistency across repositories indicates a strong, focused research agenda on making AI systems more transparent, reliable, and aligned with human reasoning. There are no listed scientific awards or recognitions in the available text. There is no information available about student advising or research grants. Similarly, no specific labs or research teams are mentioned, though his GitHub activity suggests he is part of or collaborates with a research group focused on machine learning and explainability at the University of Trento.
Berta María Guijarro Berdiñas is a Researcher in the Department of Computer Science and Artificial Intelligence at the University of A Coruña , Spain. She is affiliated with the Laboratory for Research and Development in Artificial Intelligence and teaches courses like Machine Learning , Development of Intelligent Systems , and Programming at both undergraduate and postgraduate levels. Research Focus: Her work lies at the intersection of Artificial Intelligence , Machine Learning , and Knowledge-Based Systems . Key contributions include frugal learning (limited data), anomaly explanation , and distributed learning for edge devices. She applies these to areas like health informatics , forest fire management , and human-robot interaction . Recent Publications span explainable AI , anomaly detection , multi-agent systems , and low-power machine learning . Her articles appear in top venues like Expert Systems with Applications and IEEE Transactions on Neural Networks and Learning Systems . Grants & Projects include EU-funded initiatives, Spanish Ministry of Science grants, and regional collaborations. She focuses on AI for healthcare , smart systems , and distributed learning .
Sancho Salcedo Sanz is a Full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department and the GHEODE Research Group. His work focuses on applying machine learning and optimization techniques to energy systems, climate science, and environmental modeling. He holds PhDs from Universidad Complutense de Madrid (2019) and Universidad Carlos III de Madrid (2002). Key research interests include deep learning for energy price prediction, spatio-temporal climate analysis, and hybrid models for renewable energy forecasting. His GHEODE group develops optimization algorithms for network design and distributed systems. Recent publications highlight advancements in extreme weather prediction, smart grid optimization, and explainable AI for environmental monitoring. He has pioneered methodologies like Autoencoder-based flow analogues for heatwave reconstruction and multi-method ensembles for energy demand modeling. Labs/Teams: Leader of the GHEODE Group, specializing in modern heuristics and network design. Collaborates extensively on interdisciplinary projects combining AI with environmental and engineering applications.
Ruben Tolosana is a researcher at the Biometrics and Data Pattern Analytics Lab (BiDA Lab) in the School of Engineering at Universidad Autónoma de Madrid. His work centers on biometrics, with emphasis on behavioral and mobile authentication, face recognition, privacy-enhancing technologies, and deep learning applications in human-computer interaction. He actively contributes to major research initiatives such as the FRCSyn-onGoing challenge and the ChildCI framework. PhD in Computer Science or related field (inferred from publication volume and role) Advanced training in machine learning, computer vision, and biometrics His research interests include behavioral biometrics, mobile device security, synthetic data for AI training, privacy in biometric systems, and the application of Transformer models to user authentication. Tolosana's work often involves the development of novel datasets and benchmarking platforms to advance the field. He has co-authored influential surveys on privacy vulnerabilities in mobile sensors and privacy-preserving techniques in biometric recognition. The most recent articles highlight trends in using synthetic data for face recognition, applying Transformers to behavioral biometrics (keystroke, touchscreen, gait), and developing frameworks for child age detection via interaction patterns. His work consistently addresses real-world challenges such as privacy, bias, and system generalization. Publications in journals like Information Fusion , Pattern Recognition , and ACM Computing Surveys reflect the high impact and interdisciplinary nature of his research. Second FRCSyn-onGoing: Winning solutions and post-challenge analysis to improve face recognition with synthetic data (2025) ChildCI framework: Analysis of motor and cognitive development in children-computer interaction for age detection (2024) FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems (2024) SwipeFormer: Transformers for mobile touchscreen biometrics (2024) An Overview of Privacy-Enhancing Technologies in Biometric Recognition (2024) Ruben Tolosana collaborates extensively with researchers in the BiDA Lab, including Rubén Vera-Rodríguez, Aythami Morales, Julian Fierrez, and others. He has contributed to the development of public databases such as mEBAL and ChildCIdb, supporting open science. His work is supported by ongoing research grants (inferred from project scope and publications), and he plays a key role in organizing workshops such as WAMWB to advance the mobile and wearable biometrics community. He is a core member of the Biometrics and Data Pattern Analytics Lab (BiDA Lab), a leading research group in biometric technologies, where he contributes to multiple projects involving mobile authentication, face recognition, and privacy-preserving AI systems.
Luis Merino is an Associate Professor at the School of Engineering, Universidad Pablo de Olavide (UPO), Seville, Spain. He founded and leads the Service Robotics Laboratory and contributed to establishing the Systems Engineering and Automation division at UPO. He served as Vice-Dean for five years and currently coordinates the Computer Science degree program. Education: Ph.D. in Robotics from the University of Seville (2007), supervised by Anibal Ollero. Research: Focuses on cooperative robotic systems, human-robot collaboration, localization/navigation techniques, and machine learning in social robotics. His work includes leading 2 H2020, 4 FP7, 3 National R&D, and 3 Andalusian regional projects. Notable projects: MBZIRC 2020 (PI), collaboration with Honda Research Institute Japan, and EU-funded initiatives. He advocates for open-source code/datasets and industry technology transfer. Scientific Awards: ABB Award to the Best Doctoral Dissertation on Robotics (2007) Best Paper Award at ROBOT2019 Professional Roles: Associate Editor for Image and Vision Computing and IEEE Robotics and Automation Letters . Serves on ICRA/IROS conference program committees. Grant reviewer for FONDECYT (Chile), SBIR (USA), and ERC.
Dr. Antonio Andriella is a Postdoctoral Researcher at the Institut de Robòtica i Informàtica Industrial (IRII), a joint research center of the Spanish National Research Council (CSIC) and Technical University of Catalonia (UPC). He is an incoming Tenured Scientist at IRII expected to start by the beginning of 2026. Previously, he was a Research Scientist at Pal Robotics awarded with a Marie Skłodowska-Curie cofund fellowship in the H2020 project PRO-CARED (no.801342), and a Postdoctoral Researcher at the Artificial Intelligence Research Institute (IIIA) working on the Value-Aware Artificial Intelligence (VALAWAI) project. His educational background includes: PhD in Control, Robotics and Vision (2022) from Institut de Robotica i Informatica Industrial, CSIC-UPC MEng in Artificial Intelligence (2009) from Sapienza, University of Rome BSc in Computer Engineering (2006) from Sapienza, University of Rome Dr. Andriella's research focuses on creating proactive, personalized robots that can tailor their behavior to individual needs and preferences. His work spans Human-Centered Robotics , Socially Assistive Robotics , Robot Personalisation , Proactive Behaviour , and Explainable Robotics . He investigates how robots can explain their decisions to help users build clearer mental models of these systems, ultimately making robots more transparent and understandable. His research has significant applications in healthcare, particularly for elderly care and cognitive training. His recent publications demonstrate a strong focus on applying robotics to healthcare challenges, particularly in cognitive training for elderly patients with cognitive impairments. His work bridges theoretical AI approaches with practical applications in real-world environments, emphasizing user experience, personalization, and explainability across various contexts including frailty assessment, customer service interactions, and therapeutic applications. His scientific achievements have been recognized with prestigious awards: Georges Giralt award for the best European PhD thesis from euRobotics AIHUB.CSIC prize for the best AI PhD Thesis from the AI Hub of the Spanish National Research Council (CSIC) Dr. Andriella has been actively involved in multiple research projects including SWEET (Social aWareness for sErvicE roboTs), ROSPIA, FRAILWATCH, and DEMETER 5.0. He has organized workshops on trust, AI, ethics and personalization at major conferences including HRI, RO-MAN and ICSR, and has served as guest editor for journals such as International Journal of Social Robotics, Frontiers in Robotics and AI, and Paladyn Journal of Behaviour and Interaction Studies. His research group focuses on developing the CARESSER framework (aCtive leARning agEnt aSsiStive bEhaviouR) for in situ learning of robot social assistance, with applications in cognitive training therapy and elderly care. Current projects are exploring robot-assisted frailty assessment systems, dataset reliability for HRI research, and multilingual intent recognition on social robots, demonstrating his commitment to both theoretical advancements and practical implementations in the field.
Bogdan Kulynych is a research scientist at Lausanne University Hospital in Switzerland, working within the Clinical Data Science group. He holds a Ph.D. in Computer Science from EPFL (Switzerland), where he was advised by Carmela Troncoso, and a B.Sc. in Applied Mathematics from Kyiv Mohyla Academy in Ukraine. His academic journey also includes a visiting fellowship at Harvard University with Flavio du Pin Calmon, and internships at Google and CERN. His research spans three interconnected domains: Algorithmic Accountability, Verification, and Reliability; Privacy-Preserving Learning and Statistics; and Algorithmic Systems in Healthcare. Kulynych develops methods for obtaining practical guarantees on model stability, robustness, and reliability, while also auditing these properties. His privacy work focuses on systems ensuring practical privacy guarantees with legally legible and interpretable operational risk analyses. In healthcare, he critically studies algorithmic system deployment in clinical practice through collaboration with clinicians and medical informatics practitioners. Kulynych's publication record demonstrates significant impact in top venues including NeurIPS, ICML, ICLR, FAccT, and PETS. His recent work addresses fundamental questions in differential privacy, operational privacy metrics, and healthcare AI applications. His research trend shows increasing focus on translating theoretical privacy guarantees into practical healthcare settings while addressing the social implications of algorithmic systems. Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy (NeurIPS 2025) (ε,δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees (2025) Attack-Aware Noise Calibration for Differential Privacy (NeurIPS 2024) As an active member of the academic community, Kulynych regularly presents at major conferences and seminars, including recent talks at Harvard Privacy Tools Seminar, NeurIPS, and Imperial College London. His work has received media attention in The Guardian, Wired, The Verge, and CNET regarding algorithmic bias challenges. Kulynych maintains an active presence on Bluesky (@bogdankulynych) where he engages in critical discussions about AI ethics, privacy, and the societal implications of technology.
