جست‌وجوی استاد و پژوهشگر

Alexandre Santos Franciscoمشاهده پروفایل

استاد

• porous media
• computational mathematics
• computational fluid mechanics
• +۳ مورد دیگر

Alexandre Santos Francisco is Professor of Fluid Mechanics at Fluminense Federal University (Universidade Federal Fluminense) in Brazil, holding a PhD in Nuclear Engineering. He will soon serve as Visiting Professor at the ERC Advanced Grant project DyCon under Prof. Enrique Zuazua (FAU, University of Deusto, and Universidad Autónoma de Madrid), focusing on computational mathematics and porous media research. His academic credentials include: PhD in Nuclear Engineering (1994–2000), Federal University of Rio de Janeiro Master's Degree in Nuclear Engineering (1990–1993), Federal University of Rio de Janeiro Bachelor's Degree in Mechanical Engineering (1984–1990), Federal University of Rio de Janeiro Internship at Petrobras (1988) Francisco's research centers on porous media, computational mathematics, and computational fluid mechanics, with emphasis on numerical methods for heterogeneous media flows. His work bridges nuclear engineering safety, environmental contaminant transport, and industrial process optimization through advanced simulation techniques. Analysis of his 15 most recent publications reveals dominant trends in multiscale modeling for porous media, parallel computing implementations, and reliability assessment in nuclear systems. Key application areas include waste disposal simulation, steam generator integrity, and multiphase industrial processes, consistently integrating mathematical rigor with engineering challenges. No scientific awards are documented in the provided materials. While student advisement details are absent, his upcoming ERC Advanced Grant project DyCon involvement signifies active grant-funded research. The project will expand his work on mathematical control theory for complex systems under Prof. Zuazua's supervision. He collaborates internationally through the ERC DyCon project, leveraging expertise in numerical methods to address multiscale dynamics in porous media, with future work likely advancing computational techniques for energy and environmental applications.

• Computer Science
• Parallel Computing
• Embedded Systems
• +۳ مورد دیگر

Represa Pérez, César is a faculty member at the University of Burgos , affiliated with the School of Engineering . He has contributed extensively to computer science, parallel computing, and embedded systems through research articles, educational materials, and conference presentations. Education: Doctorate in Parallel & Hybrid Programming (2002). Research Areas: Parallel computing (MPI/CUDA), 3D virtual labs, sensor technology, Android applications, and embedded systems design. Key Collaborations: Co-authored works with José María Cámara Nebreda, Pedro L. Sánchez Ortega, and others on technical education and hardware-software integration. Recent Trends: Focus on smartphone-driven sensors, additive manufacturing monitoring, and educational tools for engineering students.

FRANCISCO MANUEL FERNANDEZ RIVERAمشاهده پروفایل

استاد

• Computer Architecture
• High-Performance Computing
• Parallel Algorithms
• +۳ مورد دیگر

Francisco Manuel Fernández Rivera is a Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing under the Faculty of Physics. His research focuses on high-performance computing, parallel algorithms, and LiDAR data analysis. He leads the ARQCOMP research group and is part of the Center for Research in Intelligent Technologies (CITIUS). His work addresses challenges in NUMA systems optimization, sparse matrix computations, and geospatial data processing. He holds a PhD from the University of Santiago de Compostela (1988), with a thesis on algorithm partitioning in hypercube computers. His research spans parallel computing frameworks, LiDAR-based infrastructure analysis, and performance modeling of distributed systems. Recent projects include developing thread migration strategies for NUMA architectures and creating tools for LiDAR data visualization and simulation. Key contributions include advancements in LiDAR-based pathfinding, energy-efficient NUMA system management, and parallelization techniques for irregular codes. His work bridges theoretical computer architecture with practical applications in urban planning and environmental monitoring.

• Computer Architecture
• GPU Systems
• Accelerators for Machine Learning
• +۴ مورد دیگر

Jose Luis Abellan Miguel is a Ramón y Cajal Fellow and Tenure-Track Associate Professor at the University of Murcia's Department of Computer Engineering and Technology. He leads the EcoArTech team and is a European R3 researcher. Previously, he held roles at the University of Ferrara, Boston University, and Universidad Católica de Murcia. His research focuses on GPU architectures, accelerators for machine learning, and privacy-preserving computing, particularly Fully Homomorphic Encryption (FHE). He has authored over 70 peer-reviewed publications and contributed to conferences like ISCA, HPCA, and MICRO. Education: Bachelor's, Master's, and PhD in Computer Science and Engineering (University of Murcia, 2007–2012) Research Interests: Abellan's work emphasizes architectural enhancements for GPU systems, customized accelerators for ML and FHE, and efficient synchronization/communication in many-core architectures. His contributions include tools like MGPU-Sim and STONNE for simulation, and frameworks like FIDESlib for FHE on GPUs. Recognition: HiPEAC Paper Awards (2011, 2019–2024) Top Picks in Hardware and Embedded Security 2024 European R3 Certificate (2024) Editorial roles at ACM TACO and Frontiers in Electronics Grants & Leadership: Recipient of a Ramón y Cajal fellowship and a Consolidación Investigadora grant. He chairs sessions at conferences like ISPASS and serves on TPCs for venues including DATE, HPCA, and MICRO. His lab collaborates with institutions like Georgia Tech, Northeastern University, and Intel. Labs/Teams: He leads the EcoArTech team, focusing on next-gen computing systems via architectural simulators. Collaborators include researchers from MIT, Boston University, and industry partners like NVIDIA and Intel.

Jose Tomas Palma Mendezمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Engineering
• Machine Learning
• +۴ مورد دیگر

Jose Tomas Palma Mendez is an Associate Professor at the University of Murcia's Faculty of Informatics, affiliated with the Department of Information and Communication Engineering. He holds a PhD in Computer Science from the same institution (1999), specializing in knowledge engineering for real-time systems. His research focuses on artificial intelligence, knowledge engineering, machine learning, and their applications in healthcare and environmental monitoring. He leads the AIKE research group (Artificial Intelligence and Knowledge Engineering), previously contributing to the 'Inteligencia Artificial e Ingenieria del Conocimiento' group. Key research themes include explainable AI, feature selection in high-dimensional datasets, time series forecasting (e.g., air quality, water consumption), and computational methods for medical prognosis (e.g., cancer outcomes, long-COVID prediction). His work integrates evolutionary algorithms, deep learning, and spatio-temporal modeling. Notable contributions span AI-driven healthcare systems, ambient intelligence for elderly care, and ontology-based decision support tools. His methodologies emphasize multi-objective optimization, surrogate modeling, and interpretable machine learning frameworks. Education: PhD in Computer Science, University of Murcia (1999) Key Projects: Development of SAVIA (agricultural pest management tool), T-CARE (temporal case retrieval), and InSCo-Gen (model-driven web applications) Labs/Teams: AIKE Research Group (University of Murcia) Publications highlight advancements in imbalanced learning frameworks, multi-criteria environmental forecasting, and medical diagnostic support systems. His work bridges theoretical AI innovations with real-world applications in healthcare, environmental science, and smart infrastructure systems.

• High-Performance Parallel and Distributed Computing
• Grid Computing
• Cloud Computing
• +۳ مورد دیگر

Rubén Santiago Montero is an Associate Professor at the Department of Computer Architecture and Systems Engineering, Universidad Complutense de Madrid (UCM). He leads research in distributed systems, focusing on resource provisioning in Grid, Cloud, and edge computing environments. His work emphasizes virtual machine management, cloud federation, and utility computing models. Montero co-leads the OpenNebula project, a widely adopted cloud management platform, and contributed to the GridWay metascheduler. Research interests include distributed resource management, virtualization, and interoperability between cloud infrastructures. He participates in major EU projects such as RESERVOIR, BEACON, and PANACEA, advancing cloud and grid technologies. His contributions span over 200 peer-reviewed publications in top journals and conferences, addressing topics like workflow scheduling, elastic resource allocation, and edge-cloud architectures. Education: PhD in Computer Science (UCM). Grants & Projects: Principal investigator in EU-funded initiatives (e.g., RESERVOIR, BEACON) totaling €20M+. Labs/Teams: Distributed Systems Architecture Group at UCM, collaborating with NASA, IBM, and European research networks.

Judith Liu-Jimenezمشاهده پروفایل

پژوهشگر

• Biometrics
• Iris Recognition
• Fraud Detection
• +۳ مورد دیگر

Judith Liu-Jimenez is a Researcher at the University Carlos III of Madrid since 2004, where she earned her PhD in Electronics in 2010. Her work focuses on Biometrics (particularly Iris Recognition) and microelectronic design for digital signal processing systems. She holds a Telecommunication Engineering degree from the Polythecnic University of Madrid (2004). She has extensive experience in project management and presales roles, including leading Biometric and border control projects in Latin America while at Morpho Colombia (2012). She returned to Europe in 2013 to continue research within the University Group for Identification Technologies. Education: PhD in Electronics, University Carlos III of Madrid (2010) Telecommunication Engineering Degree, Polythecnic University of Madrid (2004) Research Interests: Develops new biometric algorithms, fraud detection systems, and hardware/software co-design for ID tokens. Specializes in high-performance microelectronic systems for computational tasks. Projects & Contributions: 5 European Projects 15 National/Regional Projects 10 Industry Contracts International Collaborations: Conducted research stays at the University of Kent (2011), University of Cambridge (2005), and Warsaw University of Technology (2004). Publications: Authored 8 journal papers, 3 book chapters, and presented at 20 international conferences.

• Optimization
• Unit Commitment
• Generation Expansion Planning
• +۴ مورد دیگر

Dr. Diego Alejandro Tejada Arango serves as a Visiting Professor in the Energy Systems Modeling area at the Institute for Research in Technology (IIT) of Comillas Pontifical University's School of Engineering. His academic journey includes a BSc in Electrical Engineering from Universidad Nacional (Colombia, 2006), an MSc from Universidad de Antioquia (Colombia, 2013), and ongoing MSc studies in Research in Engineering Systems Modeling at Universidad Pontificia Comillas. BSc Electrical Engineering, Universidad Nacional, Medellin (2006) MSc Electrical Engineering, Universidad de Antioquia (2013) MSc Research in Engineering Systems Modeling, Universidad Pontificia Comillas (in progress) His research focuses on developing advanced modeling tools for energy sector analysis and planning, with particular expertise in optimization techniques for generation capacity expansion. Dr. Tejada's work bridges theoretical advancements with practical applications in power system operations and planning. His research interests span optimization methodologies, unit commitment formulations, generation and transmission expansion planning, and energy storage integration within modern power systems. Analysis of his publication record reveals a strong focus on improving computational efficiency in energy system modeling while maintaining accuracy, with recent work addressing temporal resolution flexibility, hydrogen infrastructure integration, and interpretable machine learning applications for power system operations. His research demonstrates consistent evolution from traditional power system planning toward addressing contemporary challenges of renewable integration and decarbonization. 4th EASE Student Award for PhD thesis on energy storage co-optimization Dr. Tejada has contributed extensively to collaborative research projects with industry partners including Endesa and Enel Iberoamerica, focusing on generation expansion, renewable integration, and strategic network planning. His technical reports for the Spanish Ministry of Economy demonstrate engagement with policy-relevant research. As a reviewer for leading power systems journals including IEEE Transactions on Power Systems and International Journal of Electrical Power & Energy Systems, he contributes to scholarly discourse in the field.

• Intelligent Agents
• Distributed Artificial Intelligence
• Multiagent Systems
• +۸ مورد دیگر

Javier Vazquez Salceda , Senior Lecturer at Universitat Politècnica de Catalunya (UPC), is affiliated with the School of Computer Science and the Department of Computer Science. His research spans intelligent agents, smart cities, and socio-technical systems. Key Projects: HUB D'INNOVACIÓ PEDIÀTRICA, XARXES R+D+I in health technologies, and AI-on-demand platforms. Collaborations: Active in IDEAI-UPC (Intelligent Data Science and AI Research Group) and KEMLG (Knowledge Engineering and Machine Learning Group). Research Focus: Agent-based modeling, normative systems, and distributed AI applications in healthcare and urban planning. He explores how multiagent systems can address socio-technical challenges through projects like wastewater management simulations and organ transplant coordination tools. Academic Engagement: Published extensively in journals like Artificial Intelligence and Law and IEEE Intelligent Systems , with a focus on middleware architectures, electronic institutions, and provenance systems. His work bridges theoretical AI with real-world implementations. Contact: Email jvazquez@lsi.upc.edu , ORCID 0000-0003-1732-9446 , Scopus ID 6602325102 .

• Algebra
• Number Theory
• Cryptography
• +۴ مورد دیگر

Anna Rio Doval is an Associate Professor in the Department of Mathematics at the Faculty of Informatics of Barcelona (FIB) at the Universitat Politècnica de Catalunya (UPC). She is a core member of the STNB (Seminari de Teoria de Nombres de Barcelona) research group, which serves as a hub for number theory research connecting UPC with Universitat de Barcelona and Universitat Autònoma de Barcelona. She earned her Mathematics Degree and Mathematics Doctorate from UPC, establishing a long academic career focused on theoretical mathematics with practical cryptographic applications. Her educational background laid the foundation for her expertise in algebraic structures and number-theoretic methods. Rio Doval's research spans Algebra, Number Theory, and Cryptography , with particular emphasis on Hopf-Galois theory, elliptic curves, and algebraic structures. Her work bridges abstract mathematical concepts with concrete applications in secure communications. Over her career, she has published more than 100 scholarly outputs across diverse formats including journal articles, conference presentations, books, and research projects, demonstrating remarkable productivity and sustained scholarly contribution. Analysis of her recent publications reveals a sophisticated evolution in her research focus, moving from foundational work on Galois representations and elliptic curves to more specialized investigations of Hopf-Galois structures and brace theory. Her current work explores the intricate relationships between algebraic structures of specific sizes (np, 8p) and their implications for field extensions, with applications to both theoretical mathematics and cryptographic implementations. While specific individual awards are not prominently documented in the available information, her sustained research productivity, leadership in the STNB research group, and consistent funding from competitive national and European research programs (including HORIZON 2020) indicate significant recognition within the mathematical community. Professor Rio Doval has maintained extensive collaborative networks throughout her career, with particularly strong partnerships with Teresa Crespo, Montserrat Vela, and Daniel Gil. She has contributed to numerous doctoral theses (including Daniel Gil's thesis on Hopf-Galois structures), though specific student names are not comprehensively listed in the available documentation. Her research funding includes multiple competitive grants from Spain's National Research Plans and Catalan research initiatives. As a key member of the STNB research group, she participates in a vibrant intellectual community that organizes regular seminars, workshops, and collaborative projects. The group's activities foster interdisciplinary connections between pure mathematics and applications in computer science, particularly in cryptographic implementations. Her involvement extends to educational innovation projects aimed at enhancing student engagement and integrating cutting-edge mathematical research into undergraduate and graduate curricula.

• Computer Architecture
• Parallel Processing
• Networking Systems
• +۳ مورد دیگر

Javier Verdu Mula is a Professor at the Departament d'Arquitectura de Computadors (Universitat Politècnica de Catalunya - UPC) and a key researcher at the CRAAX - Centre de Recerca d'Arquitectures Avançades de Xarxes . His work focuses on computer architecture, parallel processing, and networking systems. Fields of Research include RISC-V virtualization, multithreaded processor optimization, and performance analysis of stateful networking applications. Scientific Awards include the BDigital Global Congress (2015) and Wayra Barcelona (2012) recognitions. Collaborations span institutions like Barcelona Supercomputing Center and researchers such as Manuel Alejandro Pajuelo, Mateo Valero Cortes, and Mario Nemirovsky. His recent publications address RISC-V hypervisor extensions, deep packet processing in parallel architectures, and statistical thread assignment models. He also holds patents in hardware virtualization and resource control systems.

• Computer Architecture
• Parallel Processing
• Memory Systems
• +۷ مورد دیگر

Llaberia Griño, Jose M. is a faculty member in the Department of Computer Architecture at the Faculty of Computer Science of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He has been actively involved in research and academic activities for several decades, contributing significantly to the field of computer architecture. His research interests lie primarily in computer architecture, with a focus on high-performance computing, memory systems, cache management, interconnection networks, and parallel processing. He has made notable contributions to the design and optimization of cache hierarchies, particularly in multicore and non-volatile memory systems, and has explored innovative techniques such as reuse detection, data compression, and systolic array implementations. The trends in his recent publications indicate a sustained focus on improving the performance, efficiency, and reliability of memory systems, especially last-level caches using emerging non-volatile technologies like STT-RAM. His work often combines architectural innovation with practical modeling and forecasting techniques to evaluate system behavior. Among his scientific recognitions is participation in The 2nd Cache Replacement Championship (CRC-2) , highlighting his expertise in cache algorithms. Llaberia has advised doctoral students such as Ana Bosque Arbiol and Rubén Gran Tejero. He has been a key participant in numerous competitive R&D projects, including 'Computación de Altas Prestaciones' and 'HIPEAC 3 - European Network of Excellence', demonstrating sustained funding and collaborative research efforts. He is a member of the CAP - Grup de Computació d'Altes Prestacions (High-Performance Computing Group) at UPC, a leading research group in computer architecture and high-performance systems.

• Computer Architecture
• Vector Processors
• GPU Microarchitecture
• +۹ مورد دیگر

Roger Espasa Sans is a senior academic and researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the Department of Computer Architecture. He has made significant contributions to computer architecture, particularly in vector processing, GPU design, and performance optimization. Universitat Politècnica de Catalunya (UPC) Faculty of Informatics of Barcelona (FIB) Department of Computer Architecture His research focuses on advanced computer architecture, including vector and superscalar processors, memory systems, instruction-level and data-level parallelism, GPU microarchitecture, and performance modeling. His work bridges theoretical design and practical implementation, especially in high-performance and embedded systems. The recent articles highlight a strong trend in GPU and shader optimization, memory redundancy elimination, and performance modeling. His publications span prestigious venues such as IEEE Microarchitecture Symposium, IEEE HPCA, and The Visual Computer, indicating sustained impact in the field of computer systems and architecture. Premiada (Awarded activity) Roger Espasa Sans has advised several doctoral students, including Manel Fernandez Gomez and Francisca Quintana Domínguez, contributing to the next generation of computer architects. His collaborative network is extensive, involving key figures like Mateo Valero and international institutions. While specific grants are not listed, his prolific output suggests sustained funding. He has contributed to major research groups such as CAP (High Performance Computing Group). He is a core member of the CAP - Grup de Computació d'Altes Prestacions (High Performance Computing Group), and has also been associated with PM - Programming Models and ARCO - Microarchitecture and Compilers. His work often involves simulation frameworks like JINKS and Asim, indicating a strong focus on performance evaluation and modeling.

• Materials Science
• Metallurgical Engineering
• Thermal Analysis
• +۱۰ مورد دیگر

Ramón Pedro Artiaga Díaz is a Professor in the Department of Naval and Industrial Engineering at the University of A Coruña, Spain. He belongs to the Knowledge Area of Materials Science and Metallurgical Engineering and leads research in thermal and rheological properties of materials. He teaches across diverse programs including Industrial Technology Engineering, Nanoscience, Podiatry, and Fashion Industry Management, with a strong emphasis on materials, polymers, composites, and sustainable technologies. University: University of A Coruña School: College of Engineering Department: Department of Naval and Industrial Engineering Research Group: Propiedades térmicas y reológicas de materiales His research focuses on thermal analysis, kinetics, polymer science, composite materials, and rheology, with applications in sustainable fashion, biomedical engineering, and industrial materials. He uses advanced characterization techniques to study curing processes, degradation, and mechanical behavior of complex materials. His recent publications span high-impact journals such as Journal of Thermal Analysis and Calorimetry , Advanced Materials , and Nature Communications , showing consistent output in thermal characterization, nanocomposites, bioactive scaffolds, and sustainable materials. The research trends highlight interdisciplinary work combining materials science with environmental and medical applications. Thermal and rheological characterization of polymers and composites Development of sustainable and recyclable materials Applications in biomedical engineering and orthopedics Innovation in textile and fashion technologies Kinetic modeling of curing and degradation processes Industrial collaboration with companies like INDITEX Ramón Artiaga Díaz has supervised numerous final-year projects and master’s theses, demonstrating active engagement in student mentorship. He has secured funding from national (Ministerio de Ciencia, Innovación y Universidades) and international (CNRS) agencies, as well as industry contracts (INDITEX, S.A.), supporting extensive R&D activities. He is involved in multiple research projects focusing on complex materials, thermal analysis, and sustainable technologies. His work bridges fundamental science with industrial applications, particularly in textiles and advanced manufacturing.

• Computational Engineering
• Numerical Methods for PDEs
• High-order Methods
• +۶ مورد دیگر

Matteo Giacomini is an Associate Professor of Computational Engineering at Universitat Politècnica de Catalunya (UPC), affiliated with the Laboratori de Càlcul Numèric (LaCàN). He is also an affiliated researcher at CIMNE (Severo Ochoa Excellence Centre) and affiliated faculty at IMTech (Institute of Mathematics of UPC-BarcelonaTech). His research focuses on numerical methods for PDEs, including high-order and low-order methods, error estimation, and reduced-order modeling. Applications span computational fluid dynamics, solid mechanics, image segmentation, and industrial sustainability. Education : PhD in Applied Mathematics, École Polytechnique (2016) MSc & BSc in Mathematical Engineering, Politecnico di Milano (2013 & 2010) Research Interests : High-order methods: finite element, discontinuous Galerkin Error & adaptivity: a posteriori estimates, mesh adaptation Dimensionality reduction: reduced order models, scientific ML PDE-constrained optimization: topology/shape optimization Software development: open-source CSE tools Recent Work Trends : Recent articles emphasize multi-fidelity surrogate modeling, domain decomposition for parametric PDEs, and robust finite volume methods for incompressible/compressible flows. He also contributes to HDG method implementations (e.g., HDGlab) and industrial applications of CFD. Labs & Affiliations : LaCàN - UPC CIMNE - Innovative Algorithms & Credible Data-Driven Models groups IMTech - Mathematical Modelling research line