Find Academic Supervisors

• High-Performance Computing
• Reconfigurable Computing
• FPGA Acceleration
• +7 more

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.

Danfeng ZhangView profile

Professor

• Programming Languages
• Security
• Differential Privacy
• +5 more

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 MaiView profile

Assistant Professor

• Computer Systems
• Machine Learning Systems
• Data Management
• +3 more

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 .

• High-Performance Computing
• Big Data
• Interconnection Networks
• +5 more

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 CortésView profile

Professor

• Computer Architecture
• High-Performance Computing
• Parallel Computing
• +3 more

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.

Marc Sánchez ArtigasView profile

Associate Professor

• Distributed Computing
• Cloud Computing
• Big Data
• +6 more

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.

Juan Manuel Cebrian GonzalezView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Power Management
• +4 more

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.

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

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

Ramon CanalView profile

Professor

• Secure microarchitectures
• System reliability
• Virtualization and operating systems
• +5 more

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.

• Artificial Intelligence
• Robotics
• Data Science
• +4 more

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).

• Computer Architecture
• Programming Models
• Parallel Computing
• +5 more

Marc Gonzalez Tallada is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). He is a member of the MGT Research Group, focusing on Programming Models and high-performance computing systems. His research interests lie in the domain of computer architecture and programming models, with a strong emphasis on parallel and distributed computing, performance optimization, and system-level software. His work contributes to the design and implementation of efficient programming models that exploit modern parallel architectures. The recent publications (2010–2024) reflect a consistent focus on programming models, particularly in the context of high-performance computing. Key themes include task-based programming, runtime systems, compiler optimization, memory management, and performance analysis. The research spans computer architecture, system software, and compiler design, demonstrating a multidisciplinary approach to solving challenges in parallel computing. Scientific Awards and Recognitions: Premi o reconeixement Advising and Grants: While specific student names are not listed, his involvement in competitive R&D+i projects indicates active participation in funded research. These projects suggest leadership or collaboration in grant-supported initiatives related to programming models and computer systems. His role in innovation and research projects highlights contributions to both academic and applied domains. Labs and Research Teams: He is a core member of the MGT (Programming Models) research group at UPC, which focuses on advancing the state-of-the-art in programming models for parallel and distributed systems.

• Computer Architecture
• High-Performance Computing
• Distributed Systems
• +12 more

Yolanda Becerra Fontal is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). She is actively involved in research projects and collaborations, notably with the Barcelona Supercomputing Center, and is a member of prominent research groups such as the High Performance Computing Group (CAP) and CROMAI (Computing Resources Orchestration and Management for AI). Research Interests: Her research spans a broad spectrum of computer systems, with a consistent focus on performance, efficiency, and scalability. Key areas include Computer Architecture , High-Performance Computing (HPC) , Distributed and Cloud Systems , Resource and Energy Management in virtualized environments, and Data-Intensive Computing . More recently, her work has centered on innovative time-series database systems and data management for edge and cloud analytics. Publication Trends: Her recent scholarly output (2020-2022) shows a strong emphasis on time-series data management, proposing novel database architectures like NagareDB and strategies for polyglot persistence. Earlier work (2009-2013) was pivotal in MapReduce workload management, energy accounting for virtualized systems, and optical data center networks, demonstrating a long-standing contribution to foundational distributed computing challenges. Scientific Contributions: Her work has been published in top-tier journals and conferences such as Future Generation Computer Systems , IEEE Transactions , and Nucleic Acids Research . She has also contributed to significant competitive R&D projects and holds patents related to data flow management and distributed indexing. Advising and Grants: Dr. Becerra Fontal has served as a thesis advisor for doctoral students. Her research has been funded through competitive grants from national and regional programs, including Spanish State Research Plans (Plan Estatal de Investigación) and Catalonia's RIS3CAT strategy, supporting projects on high-performance computing and data management. Research Groups and Labs: She is a core member of the CAP - High Performance Computing Group and the CROMAI - Computing Resources Orchestration and Management for AI group at UPC. Her work is closely associated with the Barcelona Supercomputing Center (BSC) , one of Europe's leading supercomputing facilities, indicating access to advanced computational infrastructure.

Caleb StanfordView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Distributed Systems
• +4 more

Caleb Stanford is an Assistant Professor in the Department of Computer Science at the University of California, Davis. He specializes in programming languages and formal methods with applications to distributed systems, data processing, and computer security. His research focuses on developing practical tools and theoretical foundations for secure and reliable systems. Research interests span programming languages theory, formal verification techniques for distributed systems, Rust ecosystem security, stream processing verification, and automata theory extensions. Specific projects include securing Rust crates through automated auditing tools, extending derivative-based abstractions for regular expressions, and developing verification frameworks for dataflow programming. His publications demonstrate consistent focus on formal methods applications, with recent emphasis on Rust program safety (2025), stream processing verification (2020-2024), and incremental graph algorithms (2023). Article keywords frequently include formal verification, programming languages, distributed systems, and automata theory. Dr. Stanford actively recruits students for research positions: Undergraduates should demonstrate excellence in relevant coursework Prospective PhD students must apply through UC Davis admissions Current openings focus on Rust security, formal methods, and distributed systems verification He leads the Guided Incremental Digraphs (GID) research project and collaborates with UCSD on Rust security initiatives. Community contributions include serving as Artifact Evaluation Co-Chair for POPL 2024 and committee member for PLDI and OOPSLA conferences.

Maria Ribera SanchoView profile

Professor

• Software Engineering
• Conceptual Modeling
• Ontologies
• +4 more

Dr. Maria Ribera Sancho is a Professor at the Polytechnic University of Catalonia (BarcelonaTech), holding roles as Dean of the Faculty of Informatics of Barcelona (2004–2010), Vice-Dean (1998–2004), and currently Manager of the Education and Training Department at the Barcelona Supercomputing Center (BSC-CNS). She chairs the EQANIE Accreditation Committee and serves on the ACM-W Europe Executive Committee. Her research focuses on Software Engineering, Conceptual Modeling, Ontologies, Learning Analytics, and IoT applications. Notable contributions include work on automated design using conceptual models, model-driven software development, and semantic-based IoT infrastructure monitoring. She leads projects like LinDaFIX (social welfare data tools) and REMEDiAL (ontology-driven software automation). Key awards include the UPC Quality in Teaching Prize (2005, 2021), Jaume Vicens Vives Distinction (2005), Sapiens Award (2011), and Festibity Award (2011). She advises doctoral and master’s students on topics like IoT semantic monitoring and educational ontology development. Academic director of the inLab FIB Talent Program, she also directs the PRACE Advanced Training Center at BSC. Main projects include TINTIN (SQL integrity tool), e-Catalunya (collaborative government platform), and PILARES (learning analytics for secondary education). Her work bridges academia and industry through competitive projects with companies and institutions.

Pere Millán MarcoView profile

Associate Professor

• Computer Communications
• Mobile and Sensor Networks
• Internet of Things
• +3 more

Pere Millán Marco is an Associate Professor in the Department of Computer Engineering and Mathematics (DEIM) at Universitat Rovira i Virgili (URV), Tarragona, Spain. He serves as coordinator for Non-permanent lecturers. His research focuses on computer communications, mobile/sensor networks, and IoT, with emphasis on quality prediction and performance optimization in wireless environments. Education: M.Sc. in Computer Engineering (Polytechnic University of Catalonia, 1992); PhD in Computer Engineering (URV, 2018). Research Interests : Specializes in mobile and sensor network architectures, underwater acoustic networks, real-time communication protocols, and low-cost hardware solutions for educational and environmental applications. His work integrates time series analysis, predictive modeling, and social behavior patterns to enhance network efficiency. Publications Trends : Recent work emphasizes IoT applications in environmental monitoring (e.g., deep aquifer pumping systems), optimization of underwater acoustic networks, and low-cost multicomputer systems for teaching. Consistently explores predictive techniques for network topology and quality-of-service improvements in ad hoc and community networks. Awards & Recognition : Holds a granted US patent related to location-based information systems. Advising & Infrastructure : Involved in DEIM's teaching laboratory development. Active in organizing international conferences like UCAmI and VISIGRAPP. Member of the CloudLab research group, advancing cloud and distributed computing solutions.