جست‌وجوی استاد و پژوهشگر

Antonio Gonzálezمشاهده پروفایل

استاد

• Computer Architecture
• Microarchitecture
• Compilers
• +۷ مورد دیگر

Antonio González is a full Professor at the Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Computer Architecture. He leads the Microarchitecture and Compilers (ARCO) research group, focusing on robust and energy-efficient computing systems, including general-purpose processors, GPUs, and cognitive computing architectures. His research spans computer architecture, microarchitecture, compilers, GPU design, and deep learning acceleration, with a strong emphasis on energy efficiency and memory optimization. His work integrates hardware and compiler techniques to enhance performance in modern computing systems. The recent publications highlight a strong trend in energy-efficient DNN acceleration, GPU microarchitecture, memory systems, and near-data processing. His team explores novel quantization methods, adaptive caching, and architectural extensions for vision and AI workloads, published in top-tier venues such as MICRO, ISCA, and IEEE/ACM journals. ACM Fellow (2020) ICREA Academia Award (2014, 2019, 2024) UPC Duran Farell Award (2008) HiPEAC 2024 Paper Award (twice) González has supervised several PhD students and leads competitive R&D+i projects, including those funded by ICREA and the European Research Council. His research group collaborates extensively within UPC and with international institutions. He is actively involved in advancing computer architecture through innovation in simulation, hardware design, and AI acceleration. He is a member of IEEE and ACM, and his work continues to influence both academic and industrial developments in high-performance and energy-efficient computing.

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +۷ مورد دیگر

Carlos Alvarez Martinez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture at the Barcelona School of Informatics (FIB). He is a key member of the Programming Models (PM) research group and collaborates closely with the Barcelona Supercomputing Center (BSC). His research focuses on high-performance computing, FPGA acceleration, task-based programming models like OmpSs, and hardware-software co-design for heterogeneous systems. His research interests center on advancing parallel computing through innovative programming models and hardware acceleration. He investigates efficient task scheduling, resource management in multicore and FPGA-based systems, and runtime support for dataflow models. His work enables high-performance execution of complex applications in domains such as scientific computing and cyber-physical systems. He actively contributes to European initiatives like TEXTAROSSA and AXIOM, aiming to develop next-generation exascale supercomputing technologies. The trend in his recent publications shows a strong focus on leveraging FPGAs for HPC, optimizing SpMV operations, improving task scheduling with hardware support, and developing frameworks for multi-FPGA clusters. His work consistently bridges theoretical models with practical implementations, emphasizing performance, scalability, and energy efficiency in heterogeneous computing environments. Scientific Awards: Premi UPC al Compromís Social 2019 Premi Disseny per al Reciclatge 2013 Alvarez Martinez has advised or collaborated with several doctoral students, including Jaume Bosch, Xubin Tan, and Fahimeh Yazdanpanah. He has been involved in numerous competitive R&D projects, often related to high-performance computing and parallel programming models. His work includes significant contributions to educational innovation, particularly in active learning methodologies and formative assessment using interactive systems. He leads and participates in research labs and teams focused on programming models and computer architecture, notably the PM group at UPC/BSC. These teams develop runtime systems, compilers, and hardware accelerators to push the boundaries of parallel computing efficiency and programmability.

Jordi Perello Muntanمشاهده پروفایل

دانشیار

• Telecommunications Networks
• Optical Fiber Networks
• Optical Networks
• +۱۳ مورد دیگر

Jordi Perelló Muntan is an Associate Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, where he is also affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona (ETSETB). He is a member of the Broadband Communications Systems and Architectures (CBA) and IDEAI-UPC research groups, focusing on advanced optical and future internet networking technologies. Research Interests: His research spans telecommunications networks, optical fiber and optical networking, resource optimization, network architectures, and the Future Internet. He investigates performance optimization in 5G transport networks, Spatial Division Multiplexing (SDM), Recursive Inter-Network Architecture (RINA), elastic optical networks, and cognitive networking. His work integrates SDN, network virtualization, and green networking principles for scalable and efficient infrastructures. Publication Trends: His recent publications focus on probabilistic constellation shaping in multicore fiber networks, cognitive strategies for optical margin reduction, RINA-based QoS assurance, and migration planning toward spectrally-spatially flexible optical networks. These reflect a strong trend toward intelligent, adaptive, and energy-efficient network design for future communication systems. Scientific Awards: Co-recipient of the 2020 Fabio Neri Best Paper Award Runner-up (Elsevier Journal of Optical Switching and Networking) Co-recipient of the ONDM 2021 Best Paper Award Co-recipient of the 2019 IEEE Communications Society Charles Kao Award Co-recipient of the ONDM 2012 Best Student Paper Award Advising and Grants: He has advised multiple PhD students on topics including RINA, optical network planning, and virtual provisioning. He has led or participated in major European (H2020, FP7) and national (PID, TEC) research projects such as SLICENET, PRISTINE, TRAINER, and ALLIANCE, focusing on 5G, RINA, and sustainable network infrastructures. Labs and Teams: He is an active member of the CBA research group at UPC, contributing to experimental and theoretical advancements in optical and programmable networks. His team collaborates internationally on testbed development and standardization efforts in next-generation networking.

Danfeng Zhangمشاهده پروفایل

استاد

• Programming Languages
• Security
• Differential Privacy
• +۵ مورد دیگر

Danfeng Zhang is a faculty member at Duke University whose research sits at the intersection of programming languages and security. Active across the premier PL conferences since 2015, Zhang has served on more than two-dozen program committees and currently co-chairs the POPL Student Research Competition. Education & Affiliation: Home page: users.cs.duke.edu/~dz132 Affiliation: Duke University, United States Research Interests: Zhang’s work spans programming-language design, static and dynamic analysis, formal verification, and security. A recurring theme is developing language-based techniques that guarantee strong security and privacy properties—ranging from side-channel resistance and constant-time execution to differential-privacy proofs—while preserving performance and usability. His recent projects combine type systems, program logics, and automated reasoning to build practical verification tools for concurrent, speculative, and approximate software. Publication Trends: Across nine representative papers (2015-2024) Zhang has advanced static detection of cache side channels, automated proofs of differential privacy, and relaxed concurrency models. The trajectory shows deepening integration of security concerns into language infrastructure, with tool-building (CtChecker, SpecSafe, LightDP) that bridge formal guarantees and real-world systems. Service & Leadership: 2024 POPL Student Research Competition Co-Chair 2025 POPL Program Committee member Repeated reviewer/PC member: PLDI, SPLASH/OOPSLA, ISSTA, ECOOP, APLAS, PriSC, PASS Zhang regularly mentors student researchers through SRC sessions and workshop panels, fostering diversity and early-career participation in the programming-languages community.

Luo Maiمشاهده پروفایل

استادیار

• Computer Systems
• Machine Learning Systems
• Data Management
• +۳ مورد دیگر

Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .

• In-memory computing for AI
• Neuromorphic computing
• Emerging resistive memory
• +۳ مورد دیگر

Irem Boybat is a Researcher in the In-Memory Computing Group at IBM Research - Zurich, Switzerland, focusing on advanced AI hardware solutions. She holds a Ph.D. in Electrical Engineering from EPFL (2020) and prior degrees from EPFL and Sabanci University. Ph.D., Electrical Engineering, EPFL (2020) M.Sc., Electrical Engineering, EPFL (2015) B.Sc., Electronics Engineering, Sabanci University (2013) Her research bridges in-memory computing and AI, targeting energy-efficient hardware for deep learning and neuromorphic systems. Recent work explores analog AI accelerators, heterogeneous architectures, and scalable models for edge computing. Publications highlight cross-disciplinary innovation in materials, circuits, and system design. She has received the IBM Pat Goldberg Memorial Best Paper Award and EPFL PhD Thesis Distinction. Her invited talks span prestigious venues including the European Phase-Change Symposium, IEEE CICC, and HiPEAC. Collaborations include EU H2020 projects like MANIC and WiPLASH.

• High-Performance Computing
• Big Data
• Interconnection Networks
• +۵ مورد دیگر

Jesus Escudero-Sahuquillo is a Full Professor at the Computing Systems Department (DSI) of the Faculty of Computer Science Engineering at the University of Castilla-La Mancha (UCLM), Spain. His academic journey began at UCLM where he completed his Degree in Computer Science in 2006, followed by a Master of Science in 2008, and a PhD in Advanced Computing Technologies in 2011. His professional experience includes: Full Professor at UCLM (current position) PostDoc at Technical University of Valencia (2015) PhD Senior Engineer at Oracle Norway (2013-2015) 5-year PostDoc position at UCLM funded by UCLM and European Commission (2016) Dr. Escudero-Sahuquillo's research focuses on high-performance computing and Big Data, with particular emphasis on interconnection networks and related optimization strategies. His work spans congestion management, routing algorithms, network topologies, and power saving techniques. He has extensive experience with InfiniBand technology and has contributed significantly to congestion control mechanisms in high-performance interconnects. His publication record demonstrates a strong focus on advancing network performance in high-performance computing environments. Over the past decade, his research has evolved from foundational work on routing algorithms and network topologies to more sophisticated congestion management techniques applicable to modern data centers and exascale computing architectures. His most recent work addresses cutting-edge challenges in packet identification, hybrid congestion control, and adaptive routing for next-generation interconnection networks. Dr. Escudero-Sahuquillo has served as a program committee member and reviewer for numerous prestigious conferences and journals including IEEE Micro, IEEE Transactions on Parallel and Distributed Systems, and Journal of Parallel and Distributed Computing. He was the co-organizer for five editions of the IEEE International Workshop on High-Performance Interconnection Networks in the Exascale and Big-Data Era (HiPINEB). His research has been supported through participation in multiple projects funded by the European Commission and the Spanish Government. He has collaborated extensively with researchers across Europe and has been instrumental in advancing the state of the art in high-performance interconnection networks.

Mateo Valero Cortésمشاهده پروفایل

استاد

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

Mateo Valero Cortés is a renowned Professor of Computer Architecture at the Polytechnic University of Catalonia and Director of the Barcelona Supercomputing Center (BSC). He has held academic and leadership roles since 1974, advancing high-performance computing (HPC) and computer architecture research. His work includes pioneering contributions to vector architectures, multithreading, and instruction-level parallelism. Education includes a Telecommunications Engineering degree from the Polytechnic University of Madrid (1974) and a PhD in Telecommunications Engineering from the Polytechnic University of Catalonia (1980). His research spans over 700 publications, focusing on HPC systems, parallel computing, and supercomputing infrastructure. Key research interests include vector processing, super-scalar processors, and task-based programming models. Recent work emphasizes scalable architectures for exascale computing and energy-efficient hardware-software co-design. Notable achievements include the Eckert-Mauchly Prize (highest in computer architecture), Seymour Cray Award, and Charles Babbage Prize. He has led initiatives like the Spanish Supercomputing Network (RES) and PRACE (European HPC partnership). Academic affiliations include the Royal Academy of Engineering of Spain, ACM Fellow, and IEEE Fellow. He has received 13 honorary doctorates and awards such as Mexico’s Order of the Aztec Eagle. Current projects include the Mont-Blanc HPC prototype and ERC-funded research on multi-core chip design. His BSC oversees over 300 researchers and manages MareNostrum supercomputers.

• Computer Architecture
• Interconnection Networks
• High-Performance Computing
• +۴ مورد دیگر

Francisco J. Quiles is a Full Professor of Computer Architecture and Technology at the University of Castilla-La Mancha (UCLM), where he has been a faculty member since 1987. He currently serves as Director of the Department of Computer Systems and leads the High Performance Networks and Architectures (RAAP) research group, which he founded 33 years ago. His leadership extends to national and international organizations, including his current role as President of the Spanish Scientific Society of Computer Science (SCIE) since 2024. Dr. Quiles earned his Degree and PhD in Physics (Electronics and Computer Science) from the University of Valencia in 1986 and 1993, respectively. His academic journey began at UCLM in 1987, where he has held numerous leadership positions including Dean of the Department of Computer Science (1991-1994), first Dean of the Computer Science Engineering Faculty (1994-2000), and Vice-Rector of Research (2000-2011). Dr. Quiles' research focuses on high-performance interconnection networks for multiprocessor systems, clusters, and datacenters, along with parallel algorithms for video compression and transmission. His work bridges theoretical computer architecture with practical applications in high-performance computing environments. His research group has established significant collaborations with CERN's ATLAS project since 2019, demonstrating the real-world impact of his work in large-scale scientific computing. His publication record shows a consistent focus on congestion control, routing algorithms, and network performance optimization across various topologies including Dragonfly, Fat-Tree, and Slim Fly networks. Recent work (2023-2025) has emphasized practical implementations in InfiniBand hardware and solutions for modern datacenter challenges, reflecting the evolving nature of high-performance computing infrastructure. Dr. Quiles has received recognition through his leadership roles in professional organizations including SARTECO (Spanish Society of Computer Architecture and Technology) and IEEE Computer Society, where he is a Senior Member. His contributions to the field have shaped research directions in computer architecture both nationally and internationally. With over 270 peer-reviewed publications, supervision of 9 doctoral theses, and participation in more than 128 research projects supported by various national and international funding bodies, Dr. Quiles has made substantial contributions to both academic research and industry collaboration. His work with companies like Huawei, Bull, and Mellanox demonstrates the practical applicability of his research. The RAAP research group under Dr. Quiles' leadership has established itself as a significant player in computer architecture research, with particular expertise in interconnection networks. Their association with CERN's ATLAS project since 2019 represents a major achievement in applying academic research to fundamental scientific discovery.

Marc Sánchez Artigasمشاهده پروفایل

دانشیار

• Distributed Computing
• Cloud Computing
• Big Data
• +۶ مورد دیگر

Marc Sánchez Artigas is an Associate Professor at Rovira i Virgili University, Department of Computer Engineering and Mathematics. He holds a PhD from Pompeu Fabra University (2009) and conducted postdoctoral research at EPFL (Switzerland). His research focuses on distributed computing, cloud storage systems, and serverless architectures. He leads the CloudLab research group and coordinates major EU projects like Horizon Europe's CloudSkin and H2020's IOStack. Education: PhD in Computer Science (2009), Pompeu Fabra University MSc in Computer Engineering (2004), Universitat Rovira i Virgili BSc in Computer Engineering (2002), Universitat Rovira i Virgili Research Interests: Distributed systems, cloud computing, software-defined storage, serverless computing, and privacy-preserving storage solutions. His work emphasizes scalable architectures, data management in heterogeneous environments, and optimizing cloud storage efficiency through novel algorithms and frameworks. Awards: Best Paper (IEEE LCN 2007), Best Dataset (ACM IMC 2015), Serra-Hunter Excellence Professorship, and multiple grants from EU and Spanish funding bodies. Grants & Projects: Coordinated over €5 million in projects including H2020 CloudButton (serverless analytics), FP7 CloudSpaces (personal clouds), and national initiatives like Software-Defined Edge Clouds. Active in coordinating IPCEI-CIS for cloud infrastructure. Teaching: Courses on distributed systems, parallel architectures, and cloud computing. Taught at Universitat Rovira i Virgili and Universitat Oberta de Catalunya.

• Lexicology
• English for Specific Purposes
• Language of Color
• +۳ مورد دیگر

Isabel Espinosa Zaragoza is an Assistant Professor in the Department of English Philology at the University of Alicante, Faculty of Philosophy and Letters I. She holds a PhD in Linguistic Studies (cum laude, 2022) and is renowned for her work in lexicology and English for Specific Purposes, particularly the language of color. PhD in Linguistic Studies (cum laude, University of Alicante, 2022) Master’s in English and Spanish for Specific Purposes (University of Alicante, 2016, extraordinary award) B.A. in English Philology (University of Alicante, 2014, extraordinary award) Her research focuses on lexical creativity, color terminology, and natural language processing applications for accessibility. She contributes to projects like CLEARTEXT (CENID) and leads in text simplification for cognitive disabilities. Published in journals: Procesamiento del lenguaje natural , Feminismo/s , Color Culture and Science Journal Book chapters with Cambridge Scholars Publishing and Universidad de Alicante She participated in 15 conferences (PICS 2022, BICLCE 2024) and contributed to 8 public research projects (2019-2025), including EU-funded initiatives. Teaching spans 10 years across 12 courses in English for International Relations, Cultural Management, and Computational Linguistics.

Pedro Galeanoمشاهده پروفایل

دانشیار

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +۳ مورد دیگر

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

Juan Manuel Cebrian Gonzalezمشاهده پروفایل

استادیار

• Computer Architecture
• Parallel Computing
• Power Management
• +۴ مورد دیگر

Juan Manuel Cebrian Gonzalez is an Assistant Professor at the Department of Computer Engineering and Technology, Faculty of Informatics, University of Murcia. His work focuses on computer architecture, parallel systems, and energy-efficient computing. Doctorate: University of Murcia (2011), thesis on fine-grain power and thermal management in multicore processors. Research interests: Designing architectural mechanisms for optimizing power consumption and thermal management in multicore systems, cache coherence in parallel architectures, and vectorization techniques for high-performance computing. His work also explores heterogeneous architectures, fault tolerance, and efficient memory systems. Recent article trends: Focus on cache management, speculative execution, lock-free constructs, and performance-energy trade-offs in edge and heterogeneous computing. Key methodologies include gem5 simulation, Arm SVE, and AVX-512 vectorization. Collaboration: Supervised by Dr. Juan Luis Aragón Alcaraz and Dr. Stefanos Kaxiras. Active in the Computer Architecture and Parallel Systems research group.

• Artificial Intelligence
• Robotics
• Data Science
• +۴ مورد دیگر

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

• 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