Genoveva Ramos Santana is a full-time Professor at the University of Valencia, Faculty of Philosophy and Educational Sciences, specializing in Research and Evaluation Methods in Education. She is affiliated with two research groups: DIVFOREVA (Diversity and Evaluation in Lifelong Learning) and GRIFAIN (Research Group in Families and Childhood). Education: PhD in Education from the University of Valencia (2005), thesis: "Elements for the design of evaluation plans for e-learning programs in companies" Her research spans educational evaluation, inclusive education, gender perspectives, and high intellectual capacities. Recent work explores neuroplasticity in early childhood music education, cardiovascular risk in Latin American geriatric cohorts, and equity frameworks for university talent identification. Key collaborations include Amparo Pérez and Ana Mª Alfaro . Current projects focus on gender integration in curricula, inclusive primary school policies in Trapani, and innovative assessment tools for emotional competences.
Jesús Malo is an Associate Professor at the Department of Optics, School of Physics, University of Valencia, Spain. He holds a PhD in Physics (1999) and MSc in Physics (1995) from the same institution. His research focuses on low-level human vision models, information theory applications, and computational neuroscience, with contributions to image processing and vision science. He has held postdoctoral positions at NASA Ames Research Center (1999–2001) and New York University (2013). Education: PhD in Physics (1999), MSc in Physics (1995), both from University of Valencia. Research interests include Fourier analysis, perceptual signal processing, and applications of machine learning to vision science. He is affiliated with the Image and Signal Processing Group and Visual Statistics Group (VI(S)TA) at his university, and serves as Associate Editor for IEEE Transactions on Image Processing. Awards include the Vistakon European Research Award (1994). Advising: Supervised PhD students Irene Epifanio (2002) and Juan Gutiérrez (2005), and MSc students Gabriel Gómez (2004) and Yolanda Navarro (2005). His work integrates theoretical models with practical applications, such as the COLORLAB software for MATLAB. Teaching focuses on vision science in optometry programs. He is a member of AMIT (Association of Women Researchers and Technologists). Personal interests include modern art, chamber music, and Valencia’s Barrio del Carmen. His lab and personal pages highlight interdisciplinary collaborations and artistic influences.
BEATRIZ LOPEZ BOADA is a Full Professor and Vice-Chancellor for Sustainability, Infrastructure, Strategy, and Digital Transformation at Universidad Carlos III de Madrid (UC3M). Her research focuses on vehicle dynamics, control systems, and intelligent transportation systems (ITS), with emphasis on fault-tolerant control, autonomous vehicles, active suspension systems, and traffic optimization. She has pioneered work on magnetorheological dampers, vehicle platooning, and IoT-based sensor fusion for real-time state estimation. Her teaching innovations include integrating AI tools into transport engineering curricula. Key contributions include H∞ control methodologies for roll stability, energy-efficient traffic management, and robust platoon control under disturbances. Education & Professional Roles: Vice-Chancellor for Sustainability, UC3M (current) Full Professor of Automotive Engineering, UC3M Research Interests: Active suspension systems for rollover prevention Fault-tolerant control architectures for electric vehicles Data-driven traffic prediction using LSTM and graph neural networks Integration of IoT and AI in transportation infrastructure Recent work emphasizes connected vehicle systems, including heterogeneous platoon coordination and adaptive control strategies under communication delays. She also investigates how driver preferences impact energy-efficient traffic signal control. Awards & Grants: While specific awards aren't listed, her sustained research output indicates significant contributions to transportation engineering. Active grants include projects on autonomous vehicle safety and smart traffic management systems. Advising & Labs: Leads UC3M's Vehicle Dynamics and Control Lab, focusing on experimental validation of control algorithms using real-world vehicle platforms. Collaborates with automotive industry partners on suspension system optimization and sensor fusion technologies.
Gema Revuelta de la Poza is an Associate Professor at Pompeu Fabra University's Department of Experimental and Health Sciences and holds a part-time lecturer role at the UPF Barcelona School of Management (UPF-BSM). She serves as Academic Director of the Master in Scientific, Medical, and Environmental Communication and directs the Center for the Study of Science Communication and Society. Her roles include leadership in science communication, public health discourse, and educational initiatives in Barcelona. She earned a Degree in Medicine and Surgery from the University of Barcelona. Previously, she was Deputy Director of the Science Communication Observatory (1996–2015) and Director of Scientific Culture at the Institute of Culture of Barcelona (2003–2008). Her academic experience spans over two decades in science communication research and policy. Research interests focus on science communication strategies, health journalism, citizen engagement in science, and innovation ecosystems. Recent articles highlight pandemic-era scientist visibility, collaborative innovation training, and science communication pedagogy. Her work bridges academia and public outreach, emphasizing ethical science communication and societal impacts of scientific research. She contributes to EU science policy initiatives and open science frameworks.
Jorge Lobo is an ICREA Research Professor at the Department of Information and Communication Technologies at Universitat Pompeu Fabra (UPF) and holds a Visiting Professor appointment at Imperial College London's Department of Computing. He holds a Ph.D. in Computer Science from the University of Maryland (1990), an M.S., and B.E. from Simon Bolivar University. His career includes roles at IBM Research, Bell Labs, and academic positions at the University of Illinois at Chicago. His research focuses on AI, Network & Distributed Systems Management, Security/Privacy, and policy-driven systems. He pioneered policy-based network management, developing the PDL language and contributing to policy enforcement frameworks in telecommunication networks. His work spans policy languages (XACML, PMAC), role mining algorithms, and security policy analysis. He holds 7 patents in policy technologies and has authored two books with over 100 refereed publications. Key contributions: Policy-based systems, neuro-symbolic learning, privacy-preserving frameworks, and declarative distributed computing Notable achievements: ACM Distinguished Scientist, IBM Identity Management Product implementations Recent publications (2020-2024) emphasize neuro-symbolic learning, federated learning frameworks, differential privacy, and AI-driven security solutions. His work bridges formal logic with machine learning to create interpretable systems for complex challenges in network management and policy enforcement. Labs/Teams: Active in UPF's Artificial Intelligence and Machine Learning group. Collaborates internationally on policy-aware systems and declarative networking architectures.
Sergio Jiménez Celorrio is a Full Professor at the Department of Computer Systems and Computation of the Polytechnic University of Valencia. He has held previous positions including Ramón y Cajal fellow at the University of Melbourne, Juan de la Cierva fellow at Universitat Pompeu Fabra, and teaching assistant at Universidad Carlos III de Madrid, where he earned a Distinguished Thesis Award in 2011. His research focuses on automated planning, Bayesian inference, and machine learning synergies. He has co-organized the 7th International Planning Competition and contributed to top AI conferences. Education: PhD in Artificial Intelligence from Universidad Carlos III de Madrid (2011, Distinguished Thesis Award). Research interests emphasize automated planning frameworks, heuristic search, and integrating machine learning with planning systems. Awards include the IJCAI 2016 Distinguished Paper Award and Sister Conferences Best Paper Award at IJCAI 2022. His work spans over 50 publications in venues like AI Journal, JAIR, and IJCAI. Advises PhD student Diego Aineto García and collaborates on grants and competitions. Active in organizing conferences and workshops.
Mireia Marimon Tarter is a Postdoctoral Researcher at the Center for Brain and Cognition, Pompeu Fabra University (UPF), Spain, where she leads an EU-funded Marie Curie project on statistical learning in bilingual and at-risk infants. She is part of the Speech Acquisition and Perception (SAP) group led by Prof. Núria Sebastián-Gallés. Previously, she was a postdoctoral researcher at the University of Potsdam and held visiting positions at UCLA and Université Paris Descartes. Ph.D. in Linguistics (Early Language Development), University of Potsdam, Germany (2019) M.Sc. in Cognitive Science and Language, University of Barcelona, Spain (2015) Her research focuses on early speech perception, word segmentation, and statistical learning in infants. She investigates how infants extract words from fluent speech, the role of prosody and phonological encoding, and links to later language development. A significant part of her work addresses methodological challenges in infant research, including reliability of behavioral methods, individual differences, and web-based data collection . Her recent publications span topics such as pupillometry, artificial grammar learning, social robots in language acquisition, and innovative infant testing tools. The studies reveal trends in neurocognitive methods in developmental science , with increasing use of physiological measures (pupillometry), digital tools (web-based games), and cross-linguistic designs . She has received multiple scientific awards and competitive grants: Marie Skłodowska-Curie Postdoctoral Fellowship (2023–2025) Postdoc Prize from Brandenburg in Human and Social Sciences (2022) Volkswagen Foundation "Open Up" Research Grant (2026–2027, Co-PI) Labex EFL Mobility Grant (2019) KoUP Cooperation Funding (2021) Paper of the Month, Research Focus Cognitive Sciences (March 2022) Mireia Marimon Tarter has supervised master's theses at Universitat Oberta de Catalunya and taught courses in psychobiology of language, cognitive neuroscience, and language acquisition at UPF, UOC, and the University of Potsdam. She has contributed to major collaborative projects such as ManyBabies (MB3N) and served on academic committees including the Study Commission at the University of Potsdam. She is also involved in science communication as coordinator of the Kinder Schaffen Wissen Social Media Team. Her current research is conducted within the Speech Acquisition and Perception (SAP) Group at UPF, which focuses on the cognitive and neural bases of language acquisition. She previously contributed to the "Crossing The Borders" project (DFG-funded) and the "Toytest" project at the University of Potsdam, aimed at early detection of language disorders.
Francisco M. Delicado is an Associate Professor in the Department of Sistemas Informáticos at Universidad de Castilla-La Mancha, Spain, where he has been employed since 2007. He earned his PhD in Computer System from the same institution, with his doctoral work beginning in 1999. His research spans several key areas in computer science and engineering, particularly focusing on Internet of Things (IoT) , Wireless Networks , Quality of Service (QoS) , and Environmental Monitoring . He has developed low-cost IoT ecosystems for detecting glyphosate in water and monitoring agrochemical spray drifts. Additionally, his work extends to medical IoT (IoMT), blockchain applications in e-government, and machine learning for vector monitoring. His recent publications show a strong trend toward applying IoT and machine learning to environmental and public health challenges. Earlier works focus on QoS mechanisms in wireless and mobile networks, including WiMAX (IEEE 802.16), TDMA/TDD, and HIPERLAN/2, with contributions to bandwidth allocation, contention resolution, and video transmission resilience. He has no listed scientific awards in the provided data. Francisco M. Delicado has collaborated extensively with researchers such as Teresa Olivares, Javier Aira, and Luis Orozco-Barbosa. While no formal advising roles or grants are mentioned, his long-standing publication record suggests active research leadership. There is no mention of specific labs or research teams, but his work implies involvement in networking and IoT-focused research groups at his university.
Juan Alcalde Martín is a Permanent Researcher at the Institute of Earth Sciences Jaume Almera (GEO3BCN-CSIC), a leading research institution under the Spanish National Research Council. He holds a PhD in Earth Sciences from the University of Barcelona and has held research positions at prestigious institutions including the University of Aberdeen and the University of Edinburgh. His work focuses on geophysical characterization of the subsurface for energy and environmental applications. PhD in Earth Sciences, University of Barcelona (2014) Master’s in Geophysics, University of Barcelona (2011) Bachelor’s in Geology, University of Salamanca (2008) Dr. Alcalde Martín's research spans geophysics, carbon capture and storage, geothermal energy, seismic imaging, and reservoir characterization . He investigates subsurface structures using seismic reflection data, with a focus on uncertainty, fracture networks, and the application of geoscience to climate change mitigation. His recent work emphasizes negative emissions technologies and the repurposing of geological formations for hydrogen and CO2 storage. His 15 most recent publications (2024–2025) reveal a strong trend toward climate-relevant geoscience , particularly in carbon storage, geothermal potential in the Iberian Peninsula, and subsurface energy storage. He integrates AI and advanced seismic methods to enhance subsurface analysis, contributing to energy transition strategies and sustainable resource management. His scientific achievements have been recognized with several honors: PhD awarded Cum Laude with International Mention 2017 Best Recent Paper Award, AAPG Petroleum Structure and Geomechanics Division Finalist for Image of the Year, British Geophysical Association Dr. Alcalde has been involved in numerous research projects funded by international and national agencies, collaborating with institutions across Europe. He has mentored students and early-career researchers through fellowships and collaborative projects, though specific advisees are not listed. He is actively engaged in knowledge dissemination through conferences and high-impact publications. He is a key contributor to major research initiatives such as the UnriDDLE Project and maintains leadership in developing databases like the Iberian Evaporite Structure Database (IESDB), supporting interdisciplinary teams focused on energy, carbon, and nuclear waste storage solutions.
Jose Barrera is a biostatistician and Assistant Professor in the Department of Mathematics at Universitat Autònoma de Barcelona (UAB), where he teaches applied statistics in health sciences and supervises undergraduate and master’s theses. He is also a senior statistician at ISGlobal (Barcelona Institute for Global Health), where he conducts statistical analyses for environmental epidemiology research. His work spans air pollution, noise, green space, climate impacts, and child health, with a strong focus on methodological innovation in data analysis. B.Sc. in Physics, University of Barcelona (1994) B.Sc. in Statistics, Universitat Autònoma de Barcelona (2007) M.Sc. in Statistics, Polytechnic University of Catalonia (2012) His research interests include biostatistics, environmental epidemiology, statistical methodology, exposome analysis, longitudinal study design, and R programming. He develops open-source tools such as R packages miclust , tlm , and optimalAllocation to improve statistical modeling and interpretation in public health research. His work emphasizes practical implementation and software development for complex models. The 15 most recent publications highlight his contributions to air pollution and health, noise exposure, green space benefits, nutritional interventions, and statistical methods. Key themes include health impact assessment across European cities, cognitive effects of pollution in adolescents, and methodological advances in regression and time-series modeling. His work often involves large multi-center collaborations and high-impact journals like The Lancet Planetary Health and Epidemiology . Jose Barrera has co-authored over 40 articles, with 93% published in Q1 journals. While no specific awards are listed, his consistent publication record and software development reflect significant scholarly impact. He provides statistical supervision to students and researchers and teaches advanced statistical methods in public health programs. He is actively involved in research teams at ISGlobal and collaborates with UAB’s Statistics Service. His work integrates data science, public health, and environmental policy, contributing to evidence-based urban planning and health protection. He continues to publish and develop tools as of 2025.
ABDERRAHIM FICHOUCHE, MOHAMED is an Associate Professor in the Department of Systems Engineering and Automation at the Carlos III University of Madrid (UC3M), where he also serves as Deputy Director of the Doctorate. He is affiliated with the Robotics Lab research group and the Pedro Juan de Lastanosa Institute of Technology Development and Innovation. His academic and research profile spans robotics, control systems, biomedical engineering, and power systems. His research interests include robotics , automation , control systems , biomedical signal processing , computer vision , and renewable energy integration . His work focuses on intelligent robotic systems, rehabilitation robotics, EEG-based brain-computer interfaces, fault detection in power systems, and advanced control strategies for industrial and autonomous systems. The recent publications reflect a strong trend in interdisciplinary research, combining machine learning with signal processing for applications in medical diagnostics , robotic control , and smart grid monitoring . His work increasingly integrates deep learning , wavelet analysis , and adaptive control across domains. He has led major research projects such as: HANDLE (EU and national funding): Focused on dexterous in-hand manipulation. SARAH: Enhancing robot autonomy with artificial hands. PAPREC: Automatic grasping of disordered parts. PROSAVE: Eco-efficient aircraft systems. GRAND-PA: Assistive technologies for the elderly. He currently participates in ongoing projects including: SEGVAUTO5G-CM (2025–2028): Future mobility innovation. RoboCity2030-DIH-CM (2019–2023): Madrid Robotics Digital Innovation Hub. HYPER: Neuroprosthetic and neurorobotic devices for rehabilitation. He has supervised several theses on topics such as transmission line fault detection , autonomous decision-making in robots , and 3D perception . His research is conducted primarily within the Robotics Lab at UC3M, a multidisciplinary team focusing on advanced robotic systems for industrial, medical, and service applications.
Dr. Grigorios Asimakopoulos is an Associate Professor at University Carlos III of Madrid , with research spanning business strategy, technology adoption, and telecommunications. His work examines institutional transitions, innovation dynamics, and user behavior in digital ecosystems. Research Focus: Institutional adaptation and strategic performance Wearable technology engagement Telecom market evolution and regulation Entrepreneurship education frameworks Methodological Expertise: Combines qualitative analysis with quantitative techniques like DEA benchmarking and longitudinal studies.
Nikita Zhivotovskiy is a tenure-track Assistant Professor in the Department of Statistics at the University of California, Berkeley. He previously held postdoctoral positions at ETH Zürich and Google Research, Zürich, and was affiliated with the Technion I.I.T. His academic background includes a PhD from the Moscow Institute of Physics and Technology, with affiliations during his studies at the Institute for Information Transmission Problems, Higher School of Economics, and Skoltech. His research lies at the intersection of mathematical statistics, probability theory, and learning theory . Key areas include robust estimation, online learning, statistical learning theory, algorithmic stability, and high-dimensional statistics. His work emphasizes theoretical foundations of machine learning, with a focus on generalization, risk bounds, and learning under non-standard assumptions. The recent publications reflect a strong trend in theoretical machine learning , particularly in understanding the limits and optimality of learning algorithms. Topics span PAC learning, online classification, private estimation, and clustering, often achieving dimension-free or high-probability guarantees. His work frequently appears in top venues such as NeurIPS, COLT, and FOCS, indicating significant impact in the field. Scientific Awards: Best Paper Award at Conference on Learning Theory (COLT), 2020 Nikita Zhivotovskiy has served as a reviewer for leading journals including Annals of Statistics , Probability Theory and Related Fields , and IEEE Transactions on Information Theory , and as a senior program committee member for COLT and ALT. He has co-taught courses at ETH Zürich and has advised or collaborated with numerous researchers, though formal students are not listed. His research has been supported through academic and industrial collaborations, including at Google Research. His work is embedded within the theoretical machine learning community, with active participation in workshops such as those at BIRS, and a growing body of work that bridges statistical theory and algorithmic design. While no formal lab is mentioned, his research group at UC Berkeley likely focuses on foundational aspects of learning and inference.
Javier Garcia-Heras Carretero is an Associate Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid (UC3M), where he conducts research and teaches in air traffic management, flight planning, and AI applications in aviation. He is a member of the Aerospace Engineering Research Group. Research Interests: His work focuses on enhancing air traffic flow management through machine learning and optimization, particularly in mitigating weather-related disruptions. Key areas include robust 4D flight planning under uncertainty, convective weather prediction, continuous climb operations, and environmental impact reduction in aviation. He integrates AI models such as LSTM and CNNs to forecast thunderstorms and optimize traffic flow. Recent Publication Trends: His most recent articles (2023–2024) emphasize deep learning for pre-tactical convection prediction, fast stochastic flight path simulation under uncertainty, and AI-driven hotspot detection in air traffic due to weather. These works reflect a strong trend toward data-driven, real-time decision support systems in ATM using neural networks and parallel computing. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: He supervises BSc and MSc theses, including one on data science techniques for weather mitigation in ATFM. He has led multiple research projects as Principal Investigator, including Collaborative Learning for Intelligent Meteorological Aviation (2024–2027) and Donut proyect (2019–2022). He has participated in numerous EU-funded projects such as KAIROS, REFMAP, ECONTRAIL, and ISOBAR, as well as industry collaborations with Airbus, Boeing, and SENASA. Labs and Teams: He is part of the Aerospace Engineering Research Group at UC3M, focusing on ATM systems, trajectory optimization, and digital aviation technologies. His team collaborates internationally on projects involving AI, weather forecasting, and sustainable aviation.
Roberto Gil Pita is a Professor at the University of Alcalá (Spain), affiliated with the Department of Signal Theory and Communications. He leads the AES3 research group focusing on acoustic and electromagnetic smart sensor networks and signal processing applications. His doctoral work (2006) centered on radar target classification using statistical and AI methods under the supervision of Dr. Manuel Rosa Zurera. His research spans signal processing, machine learning, and their applications in aerospace, biomedical systems, and smart cities. Key areas include UAV detection, emotion recognition from speech, and acoustic localization using microphone arrays. His academic background includes a doctorate from the University of Alcalá and extensive contributions to wireless acoustic sensor networks, hearing aid signal processing, and bioimpedance spectroscopy. He has developed energy-efficient algorithms for real-time audio analysis, acoustic violence detection systems, and robust methods for speech enhancement in noisy environments. His work bridges theoretical signal processing with practical engineering solutions for defense, healthcare, and urban monitoring. Research interests extend to aeroelastic flutter analysis in aviation, wearable biomedical sensors for stress assessment, and data-driven approaches for sound environment classification. He has pioneered the use of deep learning in flutter testing and acoustic event classification, contributing to datasets like REALISED for benchmarking machine learning models. Notable projects include acoustic localization of drones using microphone arrays, real-time emotion detection systems, and collaborative research in smart healthcare technologies. His work emphasizes computational efficiency and energy conservation, particularly for embedded systems and battery-operated devices.
