Luis Javier Miguel González is a Professor in the Department of Systems Engineering and Automation at the University of Valladolid since 1997. He has transitioned from early work on automatic fault diagnosis and predictive maintenance to leading research on energy systems dynamics since 2004. He directs the Energy, Economics, and Systems Dynamics research group and has supervised five doctoral theses. His roles include managing the Department of International Development Cooperation, promoting initiatives like the Interuniversity Master's in International Development Cooperation and the University of Valladolid's Development Cooperation Observatory. Research focuses on energy transition pathways, integrating biophysical and socioeconomic constraints through models like MEDEAS. His work addresses systemic challenges such as the land-energy-food nexus, renewable energy variability, fossil fuel depletion, and decarbonization strategies. He has led projects under EU H2020 and national programs, emphasizing interdisciplinary approaches to sustainability. Publications span energy policy analysis, IAM development, and participatory education tools for sustainability. Notable contributions include the WILIAM System Dynamics model and pymedeas open-source software. His research bridges technical modeling with policy implications, addressing global challenges like SDG acceleration and climate mitigation.
Juan Antonio Corrales Ramón is a Research Fellow at the University of Santiago de Compostela's CiTIUS laboratory under the prestigious Beatriz Galindo program. Previously, he served as an Associate Professor at Sigma Clermont Engineering School (2014-2020) and conducted research at UPMC/ISIR in France. His academic credentials include a PhD in Automatic Control and Robotics (2011) and a Computer Engineering degree (2005) from the University of Alicante. Dr. Corrales' research centers on robotics with specialized interests in: Human-robot physical interaction for industrial applications Deformable object manipulation and control theory Tactile sensing technologies and sensor fusion Robotic grasping and in-hand manipulation Multi-robot coordination systems His recent publications demonstrate strong focus on adaptive control systems for deformable objects, tactile sensor development, and human-robot collaboration frameworks. He has participated in significant EU projects including: H2020 SoftManBot (soft material handling) H2020 Bots2Rec (recycling robotics) FUI Aerostrip (aerospace applications) Sudoe Commandia (service robotics) Honors include competitive fellowships: FPU Program Fellowship (Spanish Ministry of Education) Beatriz Galindo Program Fellowship (research excellence) At CiTIUS laboratory, he contributes to advanced robotics research with applications in industrial automation, healthcare, and agricultural robotics.
Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.
Dr. Cristina Castejón Sisamon is a Full Professor in the Department of Mechanical Engineering at Universidad Carlos III de Madrid (UC3M), affiliated with the MAQLAB (Machines Laboratory) and the Pedro Juan de Lastanosa Institute of Technology Development and Innovation. She also serves as the Assistant Vice-Rector for Entrepreneurship and In-House Research Program. Her research focuses on mechanical systems, condition monitoring of railway components, vibration analysis, robotics, and educational technology. Key areas include railway axle fatigue detection, wavelet transform applications, and predictive maintenance strategies. Her work spans advanced methodologies for fault diagnosis in rotating machinery, railway systems, and agricultural equipment. She has contributed to projects funded by entities like the EU, John Deere, and Alstom, addressing challenges in mechanical engineering and maintenance optimization. She leads or co-leads over 20 research projects, emphasizing innovation in mechanical systems and digital twins. Dr. Castejón is also active in educational initiatives, developing interactive tools for engineering education and robotics design. Her research outputs include 15+ recent articles (2022–2024), with a focus on vibration-based diagnostics, topology optimization, and historical mechanical treatises analysis. She holds patents related to skin biopsy devices and railway axle monitoring. MAQLAB serves as her primary research lab, fostering interdisciplinary collaboration in mechanical engineering and technology development.
Leocadio Hontoria García serves as Full Professor in the Department of Electronic and Automatic Engineering at the Higher Polytechnic School of the University of Jaén, Spain, where he has maintained an active academic position since 2003. His career bridges advanced research in solar energy systems with innovative educational practices in engineering disciplines. Academic Background: PhD in Physical Sciences, University of Jaén (2002) Hontoria's research program focuses on applying artificial neural networks to solve critical challenges in solar energy utilization, particularly in photovoltaic system design, solar radiation modeling, and renewable energy integration. His work demonstrates exceptional continuity in merging computational intelligence with practical energy solutions, establishing him as a leading specialist in solar resource assessment methodologies. Analysis of his recent publications reveals a strategic evolution toward educational innovation and real-world implementation. While maintaining core expertise in neural network applications for solar radiation prediction, his current work emphasizes urban photovoltaic integration, entrepreneurship education for engineers, and open-access educational resources. This shift reflects growing institutional priorities around sustainable development and practical skill transfer. Research Leadership: Principal Investigator for approximately 50 funded research projects and contracts Director of the Laboratory of Electrical and Electronic Engineering Applied to Solar Energy Extensive collaboration with industry partners in renewable energy sector Hontoria maintains active leadership in the Laboratory of Electrical and Electronic Engineering Applied to Solar Energy, where his team develops practical solutions for solar energy monitoring, system optimization, and educational tool creation. Current initiatives include IoT-based photovoltaic monitoring systems and open-source simulation platforms for engineering education.
José L. Abellán is a Ramón y Cajal Fellow (Tenure-Track Associate Professor) and European R3 researcher at the University of Murcia's Department of Computer Engineering and Technology. He leads the EcoArTech research group and holds a Ph.D. in Computer Science from the University of Murcia (2012). His career includes postdoctoral positions at Boston University and previous faculty roles at Universidad Católica de Murcia. Dr. Abellán's research focuses on architectural enhancements for GPU systems and customized accelerators targeting machine learning and fully homomorphic encryption applications. His work spans hardware/software co-design, microarchitectural extensions for privacy-preserving computation, and efficient parallel processing. His extensive publication record demonstrates consistent contributions to GPU architecture, processing-in-memory, hardware acceleration for cryptography, and graph neural networks. Recent work has appeared in top computer architecture venues including MICRO, ASPLOS, and HPCA. Awards and Honors HiPEAC Paper Awards (2019, 2020, 2023, 2024) Best Paper Award at IPDPS 2011 Top Picks in Hardware and Embedded Security 2024 European R3 Certificate (2024) Dr. Abellán leads the EcoArTech research team and serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and Frontiers in Electronics. He is a Senior Member of IEEE and active in the HiPEAC European network.
Andres Barrado Bautista is a Full Professor in the Department of Electronic Technology at the School of Engineering, Carlos III University of Madrid (UC3M). He leads research within the Electronic Power Systems Group (GSEP), focusing on power electronics, energy conversion, and their applications in aerospace, electric vehicles, and renewable energy systems. His work bridges theoretical modeling and practical implementation, with a strong emphasis on high-efficiency DC-DC converters, fuel cell integration, and smart energy management. PhD in Electrical Engineering (specific date not provided) His research interests center on power electronics for energy systems, including modeling and control of DC-DC converters, fuel cells, batteries, and supercapacitors. He has made significant contributions to the development of advanced converter topologies for photovoltaic systems, electric propulsion in aerospace, and hybrid electric vehicles. His work integrates system-level black-box modeling, small-signal analysis, and real-time digital control, often applied to complex embedded and distributed power architectures. The recent publications (2020–2025) highlight a strong trend in high-performance power converters for space applications (e.g., electrospray thrusters), biomedical devices (e.g., wireless pacemaker charging), and renewable integration. The articles reflect expertise in magnetic component modeling, converter stability, and advanced control techniques, with frequent publication in IEEE Transactions journals. He has supervised numerous theses and leads a wide portfolio of research projects funded by national agencies (AEI, CAM), EU programs, and industry partners such as Airbus, Siemens, CIEMAT, and SENER. Convertidor CC-CC reductor y elevador, método de conversión CC-CC, y planta fotovoltaica que incorpora dicho convertidor (2019) Convertidor y método de conversión bidireccional de corriente continua a corriente continua sin aislamiento galvánico (2019) Active control procedures for the connection of very capacitive loads using SSPCs (2017) Active control procedures for the connection of very capacitive loads using SSPCs (2014) Método y dispositivo de transformación de corriente continua en corriente alterna (2014) Método y sistema de alimentación de una carga constituida por una pluralidad de cargas elementales, en particular de LED (2013) He has secured substantial research funding as principal investigator on projects such as SMARTGREENERGY, ECOSIVE, and several initiatives related to hydrogen-powered drones and space propulsion. He also actively collaborates on projects involving intelligent modular converters, energy storage, and electromagnetic compatibility. His lab, the Electronic Power Systems Group, works on both simulation and experimental validation of power electronics systems, with applications ranging from microsatellites to electric transportation.
Manuel V. Hermenegildo is a Distinguished Professor at the IMDEA Software Institute and a Full Professor at the Department of Computer Science, Universidad Politécnica de Madrid. He holds a PhD in Electrical and Computer Engineering from the University of Texas at Austin (1986). His academic career spans over four decades, contributing to global program analysis, verification, and parallel computing. Education: PhD, Electrical and Computer Engineering, University of Texas at Austin (1986) M.S., Electrical and Computer Engineering, University of Texas at Austin (1984) M.S., Electrical Engineering, Technical University of Madrid (1981) Research Interests: His work focuses on global program analysis , verification , and optimization for functional/non-functional properties. He pioneered techniques in abstract interpretation , parallelism , and constraint/logic programming . His contributions include the Ciao programming system and the CiaoPP preprocessor, emphasizing static analysis and program debugging . Key Awards: ACM Fellow Julio Rey Pastor Prize (2006) Elected member of Academia Europaea (2010) Test of Time Award at ICLP 2017 His work has been cited over 10,000 times, with an h-index of 60. Leadership & Contributions: Founded and directed the IMDEA Software Institute Directed Spain's National Research Directorate (1999–2002) Chair of major conferences (POPL 2010, ICLP 2006) Labs & Teams: Leader of the CLIP Lab (Computational Logic, Implementation, and Parallelism) Principal investigator in EU-funded projects on resource-aware computing
David Lopez Alvarez is a Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the School of Computer Science and Department of Computer Architecture . He leads the EduSTEAM - STEAM University Learning Research Group , focusing on STEM Education , Sustainability and Informatics , and Society and Informatics . Research Trends : His recent work emphasizes Integrating Sustainable Development Goals in engineering education Designing transformative learning experiences for STEAM fields Enhancing student engagement through collaborative methodologies Scientific Recognition : Distinció Jaume Vicens Vives for teaching quality Ramon Llull Prize for academic excellence Premi UPC a la Qualitat in teaching trajectory Premio al mejor trabajo at Jenui 2024 Grants & Projects : He coordinates competitive R&D+i projects like Aprendizaje transformador para la sostenibilidad and educational innovation initiatives for student engagement.
Pascual Julián-Iranzo is an Associate Professor in the Information Technologies and Systems Department at the Higher School of Computer Science, University of Castilla-La Mancha (UCLM), where he has served since April 2003. He is a founding member and current co-director of the DEC-tau research group focused on Declarative Programming and Automatic Program Transformation. His academic career includes over 27 years of university teaching experience across various positions, with significant contributions to curriculum development including the Degree Curriculum in Computer Engineering (2008/09 and 2009/10). Education Background: Doctor in Computer Science (1994-2000) from Universitat Politècnica de València, Department of Computer Systems and Computation His research centers on declarative programming languages with emphasis on fuzzy logic programming, functional-logic paradigm integration, and automatic program transformation. He pioneered work in proximity-based reasoning systems like Bousi-Prolog and FASILL, developing novel approaches for similarity-based unification, thresholded tabulation, and fuzzy deductive databases. His publications demonstrate consistent innovation in fuzzy logic semantics, particularly in handling truth degrees and semantic similarity measures for knowledge representation. Analysis of his 15 most recent publications reveals a strong trajectory in formalizing fuzzy logic programming frameworks, with increasing focus on practical implementations (2018-2023) after establishing theoretical foundations (2015-2017). Key thematic clusters include proximity-based unification algorithms (2018-2021), semantic frameworks for truth degree management (2016-2017), and real-world applications in natural language processing (2021) and security systems (2017). Research Recognition: Positive evaluation by CENAI for three consecutive research periods: [1997-2002], [2003-2008], and [2009-2014] Consistent publication record with 24 scientific journal articles (16 JCR-indexed) As an academic leader, Julián-Iranzo has supervised 2 doctoral theses and chaired the PROLE 2021 Conference Program Committee. His grant portfolio includes significant European funding, notably the MERINET project (2016-2019) supported by ERDF and MINECO. He has served on 12 conference program committees and reviewed for top journals including Journal of Theory and Practice of Logic Programming. His DEC-tau research group maintains active international collaborations, evidenced by coordination of the UCLM node in the ALFA Lernet Project (2005-2009) and partnership with Argentina's National University of San Luis (2009-2013).
Francisco Charte Ojeda is a Titular de Universidad (Associate Professor) at the University of Jaén, Spain, working in the Department of Computer Science within the Faculty of Experimental Sciences. He is affiliated with the Andalusian Inter-University Institute in Data Science and Computational Intelligence and leads research in the Intelligent Systems and Data Mining group. His academic journey includes earning a doctorate from the University of Granada with a thesis on hybrid flexible computing methods for multilabel classification. His research interests span machine learning with particular emphasis on multilabel classification, autoencoders, feature learning, time series forecasting, and dimensionality reduction. Professor Charte Ojeda has developed several R packages including mldr, mldr.datasets, ruta, and predtoolsTS that have become valuable tools in the machine learning community. His work often focuses on solving practical challenges in data science through innovative algorithmic approaches and software implementations. Analysis of his recent publications reveals a strong focus on autoencoder architectures, multilabel learning techniques, and time series forecasting methods. His research demonstrates a consistent pattern of developing practical software tools alongside theoretical contributions, bridging the gap between academic research and real-world applications. The trend shows increasing emphasis on explainable AI, ensemble methods, and efficient implementations for big data environments. Professor Charte Ojeda has been involved in multiple research projects funded by Spanish national grants (PID2019-107793GB-I00/AEI, TIN2015-68854-R). His collaborative network includes prominent researchers in the field such as Antonio J. Rivera, Francisco Herrera, and María J. del Jesús. He maintains an active presence in academic communities through his personal website (fcharte.com), GitHub repositories, and contributions to open-source software. His educational contributions include textbooks on programming languages, operating systems, and computational tools, reflecting his commitment to both research and teaching excellence.
Catalina Rus Casas is a University Professor in the Department of Electronic and Automatic Engineering at the University of Jaén, where she has held a professorship since 2019. She leads the ENIAS (Applied Engineering and Solar Energy) research team focused on engineering of photovoltaic systems, both grid-connected and stand-alone. Her educational background includes: Electronic Engineering degree from the University of Granada (2004) PhD from the University of Jaén (2011) with thesis on energy estimation methods in grid-connected photovoltaic systems Dr. Rus Casas specializes in photovoltaic system performance analysis, monitoring through IoT, and energy generation through self-consumption photovoltaic systems. Her research integrates technical aspects of solar energy with innovative educational approaches, particularly in engineering education and entrepreneurship development. She has published over 70 indexed publications and maintains an H13 index in JCR journals. Her recent publications reveal a strong trajectory in both technical photovoltaic research and educational innovation. The technical work focuses on optimizing photovoltaic self-consumption systems, particularly for industrial and residential applications under Mediterranean climate conditions, while her educational research emphasizes project-based learning, entrepreneurship integration, and digital tools for engineering education. This dual focus demonstrates her commitment to advancing both solar energy technology and engineering pedagogy. Her notable scientific achievements include: Extraordinary Doctorate Award in Engineering and Architecture 2nd Award for the Promotion of Entrepreneurial Culture from the University of Jaén First and Second Prizes in Innovation and Entrepreneurship Awards As lead researcher of the TEP-988 research group, she has participated in over 20 research projects through national and international competitive programs. She has served as principal investigator for four competitive innovation projects and two Industrial Doctorate training grants. Her mentorship activities include guiding end-of-degree projects as research initiation and mentoring engineering students in sustainable entrepreneurship through university-technological center collaborations. Dr. Rus Casas actively contributes to the TAEE Association (Technologies, Learning and Teaching of Electronics), where she served as secretary from 2015-2022. Through this international association of higher education professors, she collaborates with over 20 universities to improve electronics teaching and integrate research advances into university education.
Maria Garcia de la Banda is a distinguished Professor at Monash University's Faculty of Information Technology, where she serves in the Department of Data Science and Artificial Intelligence (DSAI). With over 25 years of academic experience, she has held significant leadership roles including Deputy Dean (Research) until July 2022, overall Deputy Dean of the Faculty (2013-2016), and Head of the Caulfield School of Information Technology (2009-2011). She is currently a member of the ARC College of Experts and Co-Chair of the Monash-Woodside FutureLab. Her educational background includes a Doctor of Philosophy in Computer Science from the Universidad Politecnica de Madrid (awarded July 7, 1994) and an Ingeniero Informatico degree from the same institution (awarded March 1, 1992). Her PhD received the university's Best PhD Award. Garcia de la Banda's research spans multiple disciplines with a strong focus on constraint programming, combinatorial optimization, program analysis, and bioinformatics. She leads the Optimization research group within DSAI and has made significant contributions to declarative programming languages, parallelism, and automatic parallelization. Her interdisciplinary work bridges computer science with biological applications, particularly in protein structure analysis and computational drug design. Her publication record shows consistent contributions across constraint programming, optimization, and bioinformatics. Recent work demonstrates increasing interdisciplinary collaboration, with a notable expansion into bioinformatics applications alongside her core constraint programming research. She has maintained a strong presence at major conferences like CP (International Conference on Principles and Practice of Constraint Programming) while also building impactful industry collaborations. Her scientific recognition includes: Logan Fellowship (1997) - the first and only prestigious award of its kind in the Faculty of IT International Constraint Modelling Challenge winner (2005, with Peter Stuckey) Universidad Politecnica de Madrid's Best PhD Award (1994) Induction into the Monash Honour Roll (2021) Vice-Chancellor's Diversity and Inclusion Award (2020) As a research leader, Garcia de la Banda has secured over $20M in industry funding and $14M in nationally competitive funding, including $8M as Chief Investigator in 11 ARC grants (5 as lead). She has served as Area Editor of the Journal of Theory and Practice of Logic Programming since 2010 and on the Editorial Board of the Constraints journal since 2019. Her leadership extends to professional organizations, having served on the Executive Committees of both the Association of Logic Programming (2005-2008) and the Association of Constraint Programming (2017-2020), where she was President (2019-2020). She leads the Optimization research group within DSAI and collaborates extensively across Monash University and with industry partners. Her current major projects include HARNESS (Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems), the ARC Training Centre in Optimisation Technologies, and the Building 4.0 CRC project focused on better buildings through technology. These initiatives demonstrate her commitment to translating theoretical research into practical applications with real-world impact.
Marta Ruiz Costa-Jussà is a Professor at the Faculty of Informatics of Barcelona (FIB), part of the Universitat Politècnica de Catalunya · BarcelonaTech (UPC). She is affiliated with the Department of Computer Science and holds additional teaching roles at the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). She is also the academic co-director of the postgraduate program Artificial Intelligence with Deep Learning at UPC School. Her research is conducted through key UPC research groups: Intelligent Data Science and Artificial Intelligence (IDEAI), the Center for Technologies and Applications of Language and Speech (TALP), and the Speech Processing Group (VEU). Her research focuses on advancing machine translation through deep learning, particularly in multilingual and low-resource settings. She investigates neural machine translation (NMT) systems that use intermediate mathematical representations (interlingua) to improve efficiency and inclusivity across majority and minority languages. A significant emphasis of her work is on ethical AI, aiming to detect and mitigate biases in translation systems. She is also pioneering research in automatic speech translation, a domain not yet fully mastered by major tech companies. Her recent publications reflect a strong trajectory in neural machine translation, multilingual representation learning, speech-to-speech translation, and ethical considerations in AI. The research trends show a consistent focus on inclusivity, efficiency, and robustness in language technologies, with applications spanning text, voice, and cross-lingual understanding. Scientific Awards: ERC Starting Grant (€1.5 million) for the LUNAR project Google Faculty Research Award (2019) Google Faculty Research Award (2020) She has secured significant research funding, notably the ERC Starting Grant, which supports her vision for lifelong universal language representation. Her advising likely includes graduate students in AI and NLP, though specific names are not listed. She has collaborated internationally with institutions such as LIMSI-CNRS (France), University of São Paulo (Brazil), Infocomm Research Institute (Singapore), National Polytechnic Institute of Mexico, and the University of Edinburgh (UK). She is a core member of several research teams: the IDEAI research center, TALP, and the VEU group. These labs focus on cutting-edge AI, data science, language technologies, and speech processing, providing a rich interdisciplinary environment for her work on inclusive and ethical machine translation systems.
Georg Zetzsche is a tenure-track faculty member at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany, since November 2018. He leads the Models of Computation group , focusing on theoretical foundations of formal verification and synthesis for infinite-state systems. His work bridges decidability, computational complexity, and automata theory , with applications to program analysis and concurrent systems .
