Francesc Moll Echeto is an Associate Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Departament d'Enginyeria Electrònica and the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona. He leads the HIPICS research group focused on high-performance integrated circuits and systems design. His expertise spans energy harvesting, low-power electronics, process variability management, and secure circuit design. He coordinates the Doctorat en Enginyeria Electrònica program and has coordinated EU-funded projects like the European Processor Initiative (EPI). Education: M.S. in Physics, Universitat de les Illes Balears, 1991 Ph.D. in Electronic Engineering, Universitat Politècnica de Catalunya, 1995 Research Focus: His work addresses energy-efficient computing, including: - Design of circuits tolerant to manufacturing variability - Energy harvesting from mechanical and RF sources - Secure hardware countermeasures against side-channel attacks - RISC-V architecture implementations in advanced technologies - Edge computing and autonomous sensor systems Grants & Collaborations: Coordinator of R&D projects like 'ARQUITECTURA DE COMPUTADORES DE ALTAS PRESTACIONES' (PID2023-146511NB-I00) Part of the Barcelona Zettascale Lab consortium Collaborations with Barcelona Supercomputing Center and industry partners Awards: HiPEAC Paper Award (2023, 2024) for innovations in vector processing and DNN acceleration Labs/Teams: Leads the HIPICS group and the EFRICS subgroup, collaborating on EU-funded initiatives like the European Processor Initiative. Active in open-source silicon projects (e.g., Sargantana RISC-V processor).
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نمایش ۱۶-۳۰ از ۴۶۵ استاد راهنما
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Eduard Massaguer Colomer is an Assistant Professor in the Department of Mechanical and Industrial Construction Engineering at the University of Girona, where he is part of the School of Engineering. He is a Serra Húnter Fellow, reflecting his strong research and academic trajectory. He is affiliated with the Research Group in Fluids, Energy and Environmental Engineering (GREFEMA) and leads research in thermoelectric energy harvesting and waste heat recovery. His research focuses on improving the efficiency of thermoelectric generators for heat recovery in automotive, industrial, and renewable energy systems. Key areas include: Thermoelectric generator design and optimization Waste heat recovery from exhaust gases and industrial processes Integration with solar, biomass, and marine systems Dynamic modeling and experimental validation Technology transfer and spin-off development His recent publications reveal a consistent focus on energy conversion efficiency, transient thermal behavior, and practical applications of thermoelectric systems across transportation, industrial, and domestic sectors. He has contributed significantly to hybrid energy systems and low-cost solutions for rural electrification. His scientific awards include: Extraordinary Doctoral Award 2016 (UdG) Volkswagen Think Blue Award 2015 Seal of Excellence 2019 (European Commission) Multiple awards from Repsol Foundation, Iberdrola Foundation, and La Caixa He has supervised two doctoral theses and led multiple competitive research projects, securing over €320k as Principal Investigator and €500k in total funding. His work has led to four patents and five technology transfer contracts. He co-founded the spin-off Nabla Thermoelectrics SL (2016–2021), demonstrating strong industry collaboration and innovation impact. He is actively involved in research leadership, having served as guest editor for a special issue on Solar Thermoelectric Generators in MDPI Energies. His lab work centers on the Heat and Cold Laboratory at the University of Girona, where experimental validation and prototype development are conducted.
- Data Mining
- Bioinformatics
- Machine Learning
- +۳ مورد دیگر
Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es
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.
Francisco Manuel Bernal Martínez is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid. His research focuses on numerical methods, partial differential equations, and computational mathematics, with applications in industrial engineering and materials science. He leads projects such as 'Financiación adicional 5º año (2022)' and collaborates on initiatives like 'Clustering Automático de Comportamientos de Invertebrados en Libertad mediante Imagen 3D.' His work emphasizes domain decomposition algorithms, radial basis functions, and stochastic control problems. He has advised at least one PhD thesis and holds grants from regional and national funding bodies. Key research interests include meshless methods, probabilistic domain decomposition, and uncertainty quantification in energy systems. Recent publications highlight advancements in hybrid algorithms for large-scale PDEs and volatility modeling. Bernal Martínez actively participates in academic networks and has presented at international conferences on computational methods and industrial mathematics.
- Computer Architecture
- Microarchitecture
- Compilers
- +۷ مورد دیگر
Antonio Maria Gonzalez Colas is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Faculty of Computer Science of Barcelona (FIB). He leads the ARCO research group focused on Microarchitecture and Compilers and is actively engaged in high-impact research in computer architecture, GPUs, and energy-efficient computing. His collaborations extend to the Barcelona Supercomputing Center and various national and European research initiatives. Research Interests: His primary research areas include computer architecture, microarchitecture, compilers, GPUs, and processor design. He focuses on energy-efficient computing, deep neural network (DNN) accelerators, GPU simulation and optimization, memory systems, and architectural support for machine learning and autonomous systems. His work often integrates compiler techniques with hardware design for performance and efficiency. Scientific Production Trends: His recent publications demonstrate a strong focus on energy-efficient hardware for AI workloads, particularly DNN and speech recognition acceleration, GPU architectural innovations, memory optimization, and real-time rendering. He frequently publishes in top-tier venues such as ISCA, MICRO, HPCA, and IEEE/ACM journals. ICREA Academia Award 2024 HiPEAC 2024 Paper Award ACM Senior Member (2020) Advising and Grants: He has advised numerous PhD students whose theses cover topics like energy-efficient architectures for autonomous driving, speech recognition, and neural networks. He leads competitive R&D projects, including an ERC Advanced Grant and projects funded by the Spanish National Program and the ICREA Academia program, focusing on domain-specific architectures and cognitive computing units. Labs and Teams: He is the principal investigator of the ARCO (Microarchitecture and Compilers) research group at UPC, a leading team in computer architecture research in Spain. The group is part of a larger collaborative network within UPC and with international partners.
- Earth Physics
- Climate Science
- Remote Sensing
- +۳ مورد دیگر
Francisco J. Tapiador is a Professor at the Universidad de Castilla-La Mancha , affiliated with the Department of Environmental Sciences. His research spans Earth Physics, Climate Science, and Remote Sensing, with a focus on precipitation modeling, climate classification systems, and data fusion techniques. He leads the Earth Physics research group at UCLM and collaborates extensively with international institutions. Key Research Areas : Earth Physics Climate Modeling Satellite Meteorology Data Fusion Algorithms Environmental Uncertainty Quantification Publication Trends : Dr. Tapiador's recent work emphasizes high-resolution precipitation merging via radar corrections, machine learning for storm detection, and uncertainty quantification in climate models. His articles integrate Remote Sensing , Machine Learning , and Climate Informatics to address extreme weather and hydrometeorological hazards. Peer Review Leadership : Atmospheric Research Chaos, Solitons and Fractals Chemosphere Heliyon Journal of Computational Science Meteorology and Atmospheric Physics Water Resources Research Collaborative Networks : Andrés Navarro Eduardo García-Ortega Gyuwon Lee Kyo-Sun Sunny Lim Ziad S. Haddad Technological Impact : His work bridges fluid dynamics , entropy-based algorithms , and neural networks to advance satellite precipitation estimation and climate model validation. Current projects include multi-satellite data correction and climate-human system couplings.
Pablo Serrano Yáñez-Mingot is an Associate Professor at the Telematics Department of Universidad Carlos III de Madrid (UC3M), where he has been since 2002. He holds a Telecommunication Engineering degree and PhD from UC3M (2002 and 2006, respectively). Currently, he serves as Deputy Director of Infrastructure, Sustainability, and Digitalization at the Higher Polytechnic School (EPS). His research focuses on wireless networks, network protocols, and experimental performance evaluation, with over 100 peer-reviewed publications in top journals/conferences like IEEE/ACM Transactions on Networking and IEEE INFOCOM. He has led major projects such as the Hexa-X 6G flagship and Flex5Gware. He is an IEEE Senior Member and has received awards like the UC3M Excellence Award (2021) and Distinguished Member of the INFOCOM TPC (2022). Education: Telecommunication Engineering (UC3M, 2002), PhD in Telematics (UC3M, 2006). Research Interests: Wireless networks, 5G/6G systems, network protocols, energy efficiency, and experimental evaluation of communication systems. Recent Articles Trends : Focus on 5G/6G architectures, network automation, serverless computing, and energy-efficient solutions. Publications highlight advancements in NFV scaling, network slicing, and AI-driven orchestration. Awards : UC3M Excellence Award 2021 (Young Research Excellence) ACM WiNTECH 2016 Best Paper Award Elevated to IEEE Senior Member (2015) Advising & Grants : Supervised over 30 PhD/MSc students. Principal Investigator of projects like AMAZING-6G and iTrust6G. Actively involved in editorial and organizational roles in IEEE conferences (e.g., INFOCOM, WCNC). Labs & Teams : Leads research groups in network protocols, 5G/6G systems, and experimental evaluation. Collaborates with institutions like the University of Rome Tor Vergata and Trinity College Dublin through visiting positions.
Dr. Anna Maria Mandalari is an Assistant Professor at University College London (UCL), affiliated with the Electronic and Electrical Engineering Department. She serves as Director of the SafeNetIoT laboratory and the Internet Engineering/Telecommunications MSc/MRes Programme at UCL. She holds an Honorary Research Fellow position at the Institute for Security Science and Technology at Imperial College London. Her research focuses on IoT security, privacy engineering, and network measurement techniques, particularly analyzing information exposure from consumer devices and developing adaptive network security strategies. Her work has led to significant contributions such as the IMC'19 Community Contribution Award-winning study on multidimensional IoT device exposure and the Telekom Challenge-winning IoTrim prototype for blocking non-essential IoT traffic. She has secured funding through InnovateUK’s CyberASAP program and participated in high-impact projects like NATwatcher and the European roaming analysis initiative. Key Awards: Community Contribution Award (IMC'19), Best Paper Award (C2B(I)D 2015) Grants: InnovateUK CyberASAP Phase 2 funding, Telekom Challenge Development Stream Top 5 prize Labs/Teams: Director of SafeNetIoT Lab, Leading M2M and IoT security teams at UCL Her recent work addresses IoT device misactivation risks (e.g., smart speakers), scalable detection of IoT devices in the wild, and protocol design informed by crowdsourced data. She actively collaborates with industry partners like Telekom and academic institutions on network infrastructure security and performance optimization.
Juan Gamero Salinas is a Collaborating Researcher at the Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI) of Universidad de Navarra . He is affiliated with the research groups StatData (Statistical Design and Data Analysis) and SAVIA (Sustainable Architecture, Environmental Sustainability, Industrialized Housing). His primary focus lies in environmental engineering and climate-adaptive building design. Education: PhD in 2021 with thesis on tropical overheating risk mitigation via semi-outdoor spaces. Academic Contributions: >300 hours of teaching in Master’s and undergraduate programs. Research Themes: His work bridges building design , thermal comfort , and data-driven climate adaptation . Key areas include: Passive cooling strategies in high-density tropical settings Impact of building form on overheating and daylight optimization Statistical modeling for environmental performance Publications & Projects: Juan’s publications (2020-2025) span journals like Energy & Buildings and Building & Environment , focusing on semi-outdoor spaces , thermal resilience , and few-shot AI applications . He served as Principal Investigator in the Trees4HeatResilience project. Labs & Groups: Active member of StatData (statistical modeling) and SAVIA (sustainable architecture) research teams.
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Ramon Canal is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics and the Computer Architecture Department. He has served as Vice Dean of postgraduate studies and leads the VirtuOS (Virtualization and Operating Systems) research group. His academic background includes BSc, MSc, and PhD from UPC, with thesis supervision by Antonio González (UPC) and James E. Smith (University of Wisconsin-Madison). He completed sabbaticals at Harvard University (2006-2007) and University of Cyprus (2019-2020). Education: PhD, MSc, BSc in Computer Engineering (UPC) Research focus: Microarchitecture security, reliability across circuit/system levels, cloud optimization Recent publications address privacy in IoT, secure hardware accelerators, and safety-critical systems. His work contributes to the DRAC project (2019-2022), Red-RISCV network, and Horizon's Vitamin-V project. Awards include HiPEAC Paper Awards, IEEE Senior Member status, Fulbright recognition, and multiple education excellence accolades. Scientific Honors HiPEAC Paper Award (ISCA-44, 2017) IEEE Senior Member (2016) Best Paper Nominee (ICCD-32, 2014) UPC Outstanding PhD Award supervision (2011) He advises current MSc students and has mentored multiple PhD graduates. Professional activities span academic leadership, research collaborations with Barcelona Supercomputing Center (BSC), and technical contributions to reliability analysis frameworks like RECIPE and FRACTAL.
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 .
Alejandro F. Villaverde is a Ramón y Cajal research fellow at the Department of Systems & Control Engineering, Universidade de Vigo. His research focuses on structural properties of nonlinear systems, particularly structural identifiability and observability analysis. His primary research interests include: Structural identifiability of dynamic models Observability analysis of nonlinear systems Control theory and systems engineering Development of computational tools for model analysis Applications in systems biology and pharmacokinetics Villaverde's work centers on developing mathematical methods and software tools to analyze structural properties of nonlinear models. His most notable contributions include the STRIKE-GOLDD MATLAB toolbox and its Python counterpart StrikePy, which implement the FISPO algorithm for simultaneous assessment of state observability and parameter identifiability. His research has practical applications in systems biology, biochemical network analysis, and pharmacokinetic modeling where model validation and parameter estimation are critical. Among his scientific recognitions, he holds the prestigious Ramón y Cajal research fellowship, a competitive program in Spain supporting experienced researchers. Villaverde actively supervises students, with David Rey Rostro having developed the StrikePy toolbox under his supervision. His work demonstrates strong connections between theoretical control engineering and practical computational implementations, making structural analysis methods more accessible to researchers across various disciplines.
Santiago Badia is a Full Professor of Computational Science and Engineering at Universitat Politècnica de Catalunya (UPC), holding an adjoint researcher position at the International Center for Numerical Methods in Engineering (CIMNE). He leads the Large Scale Scientific Computing (LSSC) group at CIMNE, focusing on finite element methods, numerical analysis, and high-performance computing. His research emphasizes fluid dynamics, multiphysics problems, and scalable solvers for large-scale systems. Previously, he worked at Politecnico di Milano and Sandia National Labs. He developed the FEMPAR software framework, a parallel finite element tool for PDE simulations, achieving landmark scalability (e.g., 60 billion unknowns on 458,672 cores). FEMPAR is recognized in the High-Q Club of European codes. His expertise includes discontinuous Galerkin methods, XFEM, and domain decomposition preconditioners. Research interests span metal additive manufacturing, superconductor devices, and nuclear engineering applications. Awards include FEMPAR's High-Q Club inclusion. He advises PhD and MSc students (e.g., Jesus Bonilla, Eric Neiva, Marc Olm) and has open positions in postdoc/PhD levels. His team includes researchers like Javier Principe and Alberto Martín. Ongoing projects involve advancing parallel algorithms, multiphysics simulations, and software scalability for exascale computing.
JUAN CARLOS TORRES ZAFRAمشاهده پروفایل
استاد مهمان
Juan Carlos Torres Zafra is a Visiting Professor at Universidad Carlos III de Madrid. His research focuses on optoelectronics, liquid crystal technologies, visible light communication (VLC), and sensor systems. His work spans interdisciplinary areas including energy-harvesting IoT nodes, indoor positioning systems, and optical communication interfaces for high-definition media transmission. Torres Zafra has contributed to advancements in semiconductor materials (e.g., perovskites), low-cost sensor networks, and hybrid RF-VLC positioning systems. His research also extends to educational technology, exploring tools like Telegram and Google Workspace for improving student engagement in cybersecurity engineering programs. Notable projects include the GUTI group's work on optical vortices using liquid crystal devices and the development of tunable resonators based on liquid crystal capacitance. His publications from 2020–2025 highlight trends in VLC system optimization, AI-driven disinformation detection (SmartVote-AI initiative), and medical studies on amyloidosis therapy outcomes. Torres Zafra’s work often emphasizes practical applications in robotics, automotive systems, and sustainable energy solutions. While no formal awards are listed, his extensive publication record (over 80 entries from 2004–2025) reflects sustained contributions to photonics, sensor engineering, and liquid crystal device innovation. His research integrates hardware design, algorithm development, and material science to address challenges in modern communication systems and assistive technologies for visually impaired patients.