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).
Abolfazl Simorgh is a researcher at Charles III University of Madrid's Department of Aerospace Engineering, specializing in climate-optimized aviation systems. His work bridges mathematical control theory with practical climate impact mitigation, focusing on robust trajectory optimization under environmental and operational uncertainties. He leads development of open-source tools for sustainable flight planning while contributing to major European aviation initiatives. Education: B.Sc. in Control Engineering (2017) M.Sc. in Control Engineering (2020) Ph.D. in Aerospace Engineering from Charles III University of Madrid Dr. Simorgh's research centers on developing mathematical frameworks that reconcile aircraft trajectory optimization with climate impact reduction. His expertise spans robust control systems, optimization under uncertainty, and climate modeling integration, with particular emphasis on non-CO₂ emissions. His methodology addresses both CO₂ and non-CO₂ climate forcing mechanisms through computationally efficient algorithms that account for weather variability and climate metric uncertainties. This work directly supports aviation's decarbonization by providing operational strategies that reduce environmental footprint without prohibitive cost increases. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on operationalizing climate-optimal flight planning. His work consistently integrates climate science with aerospace engineering through robust optimization frameworks, demonstrating particular innovation in handling multiple uncertainty sources (weather, climate models, emissions). The publications cluster around three interconnected themes: 1) Development of open-source computational tools (ROOST, CLIMaCCF), 2) Network-scale implementation of climate-aware air traffic management, and 3) Risk analysis of climate mitigation strategies. This body of work establishes new methodological standards for quantifying and minimizing aviation's total climate impact. Scientific Awards: Luis Azcárraga Aeronautical Innovation Award for collaborative research impact Best Paper Award (2022) from a high-impact aerospace journal Dr. Simorgh secures significant research funding through European Commission projects including FlyATM4E (climate-optimized flight planning), ALARM (aviation emissions reduction), and RefMAP (sustainable aviation pathways). His grant portfolio emphasizes practical implementation of climate mitigation strategies, with strong industry-academia collaboration. He mentors junior researchers through project teams and has developed three major open-source Python libraries (CLIMaCCF, ROOST, ROC) that have become community standards for climate impact assessment in aviation research. His current work focuses on scaling climate-optimized trajectories to continental airspace while addressing operational constraints and economic viability. He leads a research group focused on climate-aware air traffic management, developing the ROOST simulation framework for GPU-accelerated trajectory optimization and the CLIMaCCF library for standardized climate metric calculations. His team collaborates with European air navigation service providers and aircraft manufacturers to transition research into operational practice, with current projects emphasizing real-time implementation and regulatory compliance frameworks.
Carlos Platero Dueñas is a Full Professor at the Department of Electrical, Electronic and Automatic Engineering and Applied Physics at the Universidad Politécnica de Madrid (UPM), where he has served for 31 years. He leads the research group Tecnologías para Ciencias de la Salud since 2015 and contributes to interdisciplinary research at the intersection of biomedical engineering, neuroscience, and artificial intelligence. Department: Electrical, Electronic and Automatic Engineering and Applied Physics Research Group: Tecnologías para Ciencias de la Salud (Health Science Technologies) Teaching: 34 years of academic experience, including 128 final projects supervised His research focuses on applying computational methods to neurodegenerative diseases , particularly Alzheimer's and Parkinson's, through neuroimaging analysis, predictive modeling, and hippocampal segmentation. Recent work includes AT(N) profiles for dementia prediction and machine learning techniques for clinical data modeling. The 15 most recent publications reveal a strong emphasis on Alzheimer's disease progression , hippocampal segmentation , and predictive analytics using neuroimaging and clinical markers. Key methodologies involve graph cuts algorithms, longitudinal modeling, and label fusion techniques applied to MRI and CT scans. Teaching contributions include: 128 final projects supervised (undergraduate and master's) 2 doctoral theses directed Active participation in university governance through the School Council and Researcher Staff Committee
Sebastian Tornil Sin is an Associate Professor in the Department of Automatic Control at Universitat Politècnica de Catalunya (UPC), affiliated with the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of UPC and CSIC. His work focuses on fault detection, diagnosis, and monitoring in industrial and water distribution systems. His research interests lie at the intersection of automatic control , data-driven modeling , and industrial applications . He specializes in leak detection and localization in water networks using sensor data, classifiers, and reasoning frameworks like Dempster-Shafer and Bayesian methods. His work also extends to sensor placement optimization and human-robot task design in academic environments. The recent publications (2017–2022) show a consistent focus on applying machine learning and control theory to real-world infrastructure problems, particularly in water networks. Trends include hybrid data-expert systems, classifier-based fault localization, and incremental sensor deployment strategies. Scientific Awards: No awards mentioned in the provided text. Sebastian Tornil Sin actively contributes to research through collaborations with experts such as V. Puig, J. Blesa, and A. Soldevila. While no formal advising or grant information is available, his extensive publication record in journals like IEEE Transactions on Control Systems Technology and Computers and Chemical Engineering indicates strong research leadership and project involvement. He is part of the research team at the Institut de Robòtica i Informàtica Industrial (IRI) , where he contributes to projects in fault detection, monitoring systems, and robotics applications in industrial and urban infrastructure.
Asier Perallos Ruiz is a Professor in the Faculty of Engineering at the University of Deusto, specializing in the Department of Computing, Electronics and Communication Technologies. His research focuses on RFID technology, wireless sensor networks, and computational intelligence applications with significant contributions to intelligent transport systems and antenna design. Dr. Perallos Ruiz's research interests span multiple domains with a focus on RFID technology , Wireless sensor networks , Internet of Things (IoT) , Computational intelligence , Evolutionary algorithms , and Intelligent transport systems . His work bridges theoretical advancements with practical applications, particularly in transportation systems, healthcare, and industrial automation. His research often involves interdisciplinary collaboration across engineering disciplines. His publication portfolio shows a consistent trend toward improving RFID systems, developing efficient anti-collision protocols, and applying computational intelligence to real-world problems. Recent work has focused on polarization-diversity rotation sensing, customizable RFID platforms, and the integration of RFID with IoT applications. His research demonstrates a progression from foundational RFID technology to more complex system integration and application-specific solutions. Dr. Perallos Ruiz has supervised several graduate students including Muralter Florian (2021), Arjona Aguilera Laura (2018), Cmiljanic Nikola (2018), Lopez Garcia Pedro (2016), and Moreno Emborujo Asier (2016). His research has been supported by various projects focusing on RFID technology, intelligent transportation systems, and wireless communication applications. He leads research teams focused on RFID systems development, wireless sensor networks, and computational intelligence applications. Current work appears to be advancing RFID sensing capabilities, energy-efficient protocols, and system integration for practical applications in transportation and industry.
Guillermo Quintero Perez is a Senior Lecturer at the Department of Mechanical Engineering , Technical University of Catalonia . His work focuses on acoustic pollution , low-cost noise sensors , digital signal processing , and noise mapping . He collaborates with researchers like Jordi Romeu and Andreu Balastegui on urban noise monitoring projects. Research Interests : Developing low-cost noise sensors for scalable urban monitoring Optimizing noise mapping through mobile sampling and data stratification Applying digital signal processing to environmental acoustics Investigating sound insulation properties of metamaterial structures Publications : His recent work (2024) includes studies on broadband noise reduction in acoustic windows and compliance testing for low-power sensors. Earlier contributions (2023-2017) explore statistical models for noise estimation, cluster analysis for temporal stratification, and sensor network deployment.
Josep Maria Bergadà Granyó is an Associate Professor in the Department of Fluid Mechanics at the Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT), Universitat Politècnica de Catalunya (UPC). He is actively involved in research through the UPC MICROTECH LAB and CATMech – Centre Avançat de Tecnologies Mecàniques. With a Doctorate in Industrial Engineering, he has a strong academic and research profile spanning decades. His research interests focus on fluid dynamics, particularly Active Flow Control , Fluid Power (Hydraulics) , Piston Pumps , Gas Dynamics , and Computational Fluid Dynamics . He applies these areas to enhance aerodynamic performance in wind turbines and industrial systems. His work employs advanced numerical methods such as the lattice Boltzmann method and RANS simulations, with a strong emphasis on optimizing flow behavior in complex geometries and energy systems. The recent publications highlight a consistent trend in aerodynamic efficiency optimization , especially in wind turbines using active flow control and synthetic jets. His research spans both theoretical modeling and practical applications, including dimensional modifications in fluidic oscillators and turbulence boundary condition effects. These efforts contribute significantly to renewable energy and sustainable engineering solutions. Among his recognitions is the Premi Iniciativa Digital Politècnica 2019 . He has led and participated in multiple competitive R&D projects, demonstrating strong grant acquisition and collaborative capabilities. He has supervised doctoral students such as K. Karimzadegan, M. Baghaei, and B. An, indicating an active role in academic mentoring. His collaborations extend across various research groups at UPC, particularly with experts in mechanical, aerospace, and textile engineering. He is a key figure in the Fluid Mechanics Department, contributing to both teaching and cutting-edge research in fluid dynamics and its industrial applications.
Ignacio Arganda Carreras is an Associate Professor at the Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU) and an Ikerbasque Research Associate, affiliated with the Donostia International Physics Center (DIPC). His research focuses on biomedical computer vision, with a strong emphasis on deep learning applications in microscopy and medical imaging. Key areas include bioimage analysis pipelines, domain adaptation for cross-modal image segmentation, and AI-driven solutions for healthcare diagnostics. He has contributed extensively to open-source tools like BiaPy, CartoCell, and DL4MicEverywhere, which advance accessibility to deep learning in bioimaging. His work bridges computational methods with biological and medical challenges, addressing issues like 3D object detection, super-resolution imaging, and automated classification in microscopy and clinical settings. Research highlights include developing the MitoEM and Nucmm datasets for mitochondria and neuronal nuclei segmentation, as well as innovative applications in wound healing modeling and aquaculture monitoring. His methodologies emphasize reproducibility, generalization, and mitigation of overfitting in deep learning models.
Dr. Lluis Batet Miracle is a Professor at the Universitat Politècnica de Catalunya (UPC) with the Department of Physics . He leads the Advanced Nuclear Technologies Research Group (ANT) and has contributed extensively to nuclear fusion technology, thermal hydraulics, and liquid metal systems. His work spans reactor safety analysis, tritium processing, and magnetohydrodynamic modeling. Expertise : Nuclear Engineering, Plasma Physics, Computational Fluid Dynamics, Fusion Reactor Design, Tritium Management Notable Projects : CONSOLIDER TECNO-FUS (2009-2013), EURATOM collaborations, HCLL Breeding Blanket Systems for ITER Research Trends : Recent publications focus on helium solubility in liquid metals, bubble dynamics in fusion blankets, and MHD simulations under nuclear conditions. His work combines atomistic modeling, high-fidelity CFD, and experimental validation for tritium and hydrogen systems in fusion reactors. Collaborations : Regularly works with Luis Sedano, Eduardo Ríos, Jordi Martí, Francesc Reventos, and Elisabet Mas de les Valls Grants : Involved in Horizon Europe, EURATOM, and Spanish National Research programs
Bernardino Casas Fernández is an Adjunct Professor at the Departament de Llenguatges i Sistemes Informàtics (LSI) of Universitat Politècnica de Catalunya (UPC). He holds offices at both the Vilanova i la Geltrú campus (EPSEVG-VG1, Room 120) and the Barcelona campus (Campus Nord-Edifici Omega, Room 224). His research focuses on quantitative linguistics, natural language processing, polysemy analysis, and computational linguistics. He has contributed to open-source tools like FreeLing and projects such as CARPANTA for email summarization. Recent work explores semanticity in Catalan using large language models and ethical implications of generative AI in education. He maintains active involvement in computational linguistics and child language development studies. Education details are not explicitly listed, but his academic career spans from 2003 (earliest article) to 2024. He collaborates with initiatives like LAPPS (Language Acquisition and Processing Systems) and has participated in projects involving churn prediction systems and entropy estimation techniques.
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.
Josep Casanovas is a Full Professor at the Statistics and Operations Research Department of the Technical University of Catalonia (UPC), affiliated with the Barcelona School of Informatics. He previously served as head of inLab FIB (2012-2020) and as dean (1998-2004) and vice-rector (2006-2011) of UPC, leading strategic initiatives in university governance and ICT policies. His research focuses on Modelling and Simulation , Internet and Information Systems , and Urban Mobility . He has led projects for the European Union, including C-ROADS Spain, REMEDiAL, and ECHORD++, addressing intelligent transport, software automation, and robotic innovation. Recent publications highlight his work on agent-based simulation for urban health, deep learning applications in traffic and energy savings, and wildfire management tools . He co-directs LogiSim and coordinates the Severo Ochoa Research Excellence Program at the Barcelona Supercomputing Center (BSC-CNS).
Adrián García Gutiérrez is a Professor in the Department of Aerospace Engineering at the University of León's College of Engineering. His research focuses on aerospace systems, uncertainty quantification in CFD, atmospheric boundary layer modeling, and airship technology. Recent publications highlight his work in parallel orbital propagation algorithms, stochastic optimization of high-altitude platforms, and neural network applications for wind profiling. Key trends include aerodynamic modeling under uncertainty and interdisciplinary approaches combining turbulence analysis with LiDAR measurements. He contributes to educational innovation in aerospace engineering through simulation-based learning tools and leads the GITA Tecnología Aeroespacial research group.
Maria Paz Linares Herreros is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC) in the Department of Statistics and Operations Research within the Faculty of Mathematics and Statistics (FME). She is affiliated with the IMP - Information Modeling and Processing research group and inLab FIB. Her educational background includes a Licenciada en Matemáticas, a Doctorate from UPC, and a Master's in Logistics, Transportation, and Mobility. Licenciada en Matemáticas Doctorat from Universitat Politècnica de Catalunya Máster en Logística, Transporte y Movilidad Her research focuses on urban mobility , traffic simulation , and smart city technologies . She develops data-driven models for transportation systems and parking management, integrating deep learning techniques for real-time predictions. Her work addresses environmental impact assessment of traffic policies and inclusive mobility solutions . Recent publications analyze urban mobility trends through macroscopic and microscopic traffic models , deep learning applications for parking predictions, and dynamic ride-sharing systems . She explores IoT interoperability and data integration in transportation. Scientific awards include the IV International Award on Transport Infrastructure Management Research (2014). She participates in competitive R+D+I projects like ALGORAE and Virtual Mobility Lab, focusing on transportation innovation and smart city policy evaluation . She contributes to multimodal transport simulation frameworks such as CitScale and Barcelona Virtual Mobility Lab, which evaluate emerging mobility concepts and city policies using integrated modeling approaches .
