Francisco A. Gómez Vela is an Associate Professor at Universidad Pablo de Olavide, Spain. His work bridges computational techniques with biomedical and energy data analysis. Education: PhD in Computer Science (Cum Laude) from Universidad Pablo de Olavide; BSc in Computer Science from University of Seville Research interests focus on: Machine learning for genetic and biomedical data Big data and high-performance computing (HPC) in bioinformatics Energy consumption analysis in smart buildings/cities Software tools for gene network reconstruction Recent publications highlight: Ensemble approaches for gene co-expression networks Biclustering methods in biological data mining GPU-accelerated bioinformatics software Applications in cancer biomarker discovery Educational data mining for microcompetency frameworks Teaching duties include: Computer Science (BSc and MSc) Bioinformatics Project Engineering History and Digital Humanities He has participated in national research projects and R+D+I transfer initiatives, developing tools like: bioScience (HPC bioinformatics library) CyEnGNet—App (Cytoscape gene network tool) BIGO (gene enrichment analysis) GNC–app (gene network validation)
Miguel García Folgado is a Momentum Postdoctoral Researcher at the Institute of Corpuscular Physics (IFIC) under CSIC, with a PhD in Astroparticle Physics from the University of Valencia. He holds dual expertise in theoretical physics research and high-performance computing (HPC) systems administration. His academic journey includes a BSc and MSc in Physics from the University of Valencia, followed by a PhD exploring dark matter models and deep learning applications (2016-2021). He has held postdoctoral positions at the Polytechnic University of Valencia and IFIC, focusing on AI-driven climate change solutions and particle physics phenomenology. Research interests span dark matter phenomenology, deep learning applications in physics and environmental science, and HPC infrastructure optimization. His work integrates theoretical physics with computational tools like TensorFlow/PyTorch and C++ for high-performance simulations. He has developed predictive models for urban traffic, wildfire emissions, and political sentiment analysis using NLP techniques. Miguel is actively involved in maintaining the GLUON HPC cluster at IFIC, managing distributed storage systems (e.g., MinIO), and automating workflows via Ansible. He has authored multiple peer-reviewed articles in journals like JCAP and JHEP, and his doctoral work earned the Extraordinary Doctorate Award. His technical skills include system administration (Linux/RedHat), database management (MySQL/PostgreSQL), and static website development using Hugo.
Jose Francisco Llosa Espuny is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Computer Science of Barcelona (FIB) and the Department of Computer Architecture. He is a key member of the PM - Programming Models research group and has been involved in numerous competitive R&D projects related to high-performance computing and computer architecture. University: Universitat Politècnica de Catalunya School: Faculty of Computer Science of Barcelona Department: Department of Computer Architecture Research Group: PM - Programming Models Email: josepll@ac.upc.edu ORCID: 0000-0001-7740-3148 His research focuses on advanced computer architecture topics, particularly VLIW processors, software pipelining, register allocation, and compiler optimizations for high-performance and low-power systems. He has also made significant contributions to computer science education, exploring active learning, problem-based learning, and the use of interactive response systems for formative assessment. His recent publications and projects, including the European Master for HPC Curriculum and various HPC education initiatives, highlight his ongoing engagement in both technical research and pedagogical innovation. The articles reflect a strong trend in optimizing processor architectures for performance and energy efficiency, as well as improving teaching methodologies in computer science. He has been involved in significant research projects funded by the Spanish government and the European Union, such as those under the HORIZON 2020 program and various national R&D plans. Llosa Espuny has advised doctoral students, including Manoj Gupta and Francisco Zalamea, indicating his role in mentoring the next generation of researchers. He has collaborated extensively with leading figures in the field, such as Mateo Valero and Eduard Ayguadé, on numerous publications and projects. His work is associated with the Barcelona Supercomputing Center, a key entity in national and European high-performance computing efforts.
Ramon Canal Corretger is a Full Professor in the Department of Computer Architecture at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He previously served as Vice Dean of Postgraduate Studies at FIB and leads the VirtuOS (Virtualization and Operating Systems) research group. His work bridges computer architecture, hardware security, and system-level reliability. Doctorate from UPC, co-supervised at the University of Wisconsin-Madison Sabbaticals at Harvard University (2006–2007) and the University of Cyprus (2019–2020) Active leadership in EU-funded projects such as Vitamin-V (Horizon Europe) His research focuses on microarchitecture, processor and memory design, reliability under variability, and security at the hardware-software interface. He explores low-power multicore architectures, virtualization optimizations, and secure RISC-V-based systems. His recent work integrates AI for intrusion detection and privacy-preserving federated learning in fog computing environments. The most recent publications demonstrate a strong trend toward security, reliability, and trustworthy computing , particularly in RISC-V ecosystems and cloud/edge infrastructures. There is increasing emphasis on hardware-software co-design , attack detection via performance monitoring , and energy-efficient secure accelerators using emerging technologies like neuromorphic and photonic computing. Scientific Awards: HiPEAC Paper Awards (2017, 2010) IEEE Senior Member (2016) Fulbright Award (2006) IBM Faculty Award (2000) Best Student Paper at HPCA-6 (2000) Multiple teaching excellence recognitions from UPC and AQU Catalunya First Prize in Epson Foundation Rosina Ribalta Award (2001) He has advised several PhD students including Manish Rana, Zoran Jaksic, and Shrikanth Ganapathy, many of whom received honors such as the Intel Doctoral Student Programme recognition. His research is supported by competitive grants from the Spanish government, EU Horizon programs, and industry collaborations. He is actively involved in the design of secure, reliable, and efficient computing systems for future cloud and embedded applications. He leads the VirtuOS research group, which focuses on virtualization, operating systems, and hardware-software interface optimization. The group contributes to open-source RISC-V initiatives and participates in large-scale European R&D projects targeting trustworthy computing infrastructures.
Anna Queralt Calafat is an Associate Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics. Her research focuses on High-Performance Computing (HPC), distributed systems, and data governance, with notable contributions in knowledge graphs, cloud-edge continuum management, and parallel workflow optimization. She leads projects funded by European and national grants, including contributions to strategic research agendas like ETP4HPC. Queralt has supervised doctoral students like Jonathan Marti and Rizkallah Touma, and her work spans over 100 publications in top venues such as Future Generation Computer Systems and the International Semantic Web Conference. She actively participates in conference committees and has received a Best Student Paper Award for collaborative research. Her educational background includes a degree in Computer Engineering and a doctorate in Software. She is part of research groups inSSIDE and DTIM, advancing areas like HPC integration with big data analytics. Key projects include automated data lifecycle management and fog-to-cloud distributed processing. Her work bridges theoretical models with practical systems like DataClay and PyCOMPSs, emphasizing scalable and efficient computing solutions.
Josep Lluís Berral García is an Associate Professor in the Department of Computer Architecture at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia · BarcelonaTech (UPC). He is actively engaged in teaching and research, with a strong focus on Artificial Intelligence, Cloud Computing, and sustainable computing practices. He leads innovative educational initiatives and is affiliated with the CROMAI research group and the Barcelona Supercomputing Center (BSC-CNS). Research Interests: Artificial Intelligence and Deep Learning Cloud and High-Performance Computing Resource Orchestration and Management Sustainable and Ethical AI AI Education and Pedagogy His recent research and teaching projects center on integrating sustainability and ethical responsibility into AI education, using active learning methodologies. The trend in his work shows a consistent focus on optimizing computing resources through AI, particularly in cloud and HPC environments, with increasing emphasis on environmental impact and responsible innovation. Scientific Awards: UPC Award for Quality in University Teaching 2025 (Teaching Initiative for Newly Recruited Professors) Advising and Grants: While specific students are not listed, his leadership in competitive R&D+i projects and innovation initiatives indicates active supervision and grant-funded research. His involvement in multiple competitive and non-competitive R&D projects demonstrates sustained funding and research leadership. Labs and Teams: He is a key member of the CROMAI (Computing Resources Orchestration and Management for AI) research group at UPC and maintains a strong collaborative link with the Barcelona Supercomputing Center (BSC-CNS), leveraging the MareNostrum supercomputing infrastructure for AI and systems research.
Rosa Maria Badia is a Professor at the Barcelona School of Informatics (FIB) of the Universitat Politècnica de Catalunya (UPC) and the manager of the Workflows and Distributed Computing group at the Barcelona Supercomputing Center (BSC). She is also the principal investigator of the EuroHPC eFlows4HPC project, focusing on the integration of HPC, Big Data, and Machine Learning through advanced workflow systems. Her research interests lie in high-performance computing, parallel and distributed computing, and task-based programming models. She is a key contributor to the development of COMPSs/PyCOMPSs, a framework enabling efficient parallel execution of complex workflows across heterogeneous computing platforms. Her work has significantly influenced the field of high-performance computing, as recognized by her receipt of the HPDC Achievement Award 2021—the first such award given to a researcher based in Europe. She has also been honored with the Euro-Par Achievement Award 2019 and the DonaTIC Award 2019 in the Academic/Researcher category. Dr. Badia has published nearly 200 papers in international conferences and journals and has been actively involved in European Commission-funded projects and industry collaborations. She is a member of the HiPEAC Network of Excellence and has contributed to the BDEC international initiative. HPDC Achievement Award 2021 Euro-Par Achievement Award 2019 DonaTIC Award 2019 (Academic/Researcher category) She mentors researchers within her group at BSC and leads a team focused on advancing programming models for complex computing environments. Her leadership extends to major European projects and international research communities, shaping the future of scalable computing systems.
Pedro Javier García García is a Professor at the Department of Computer Systems, Universidad de Castilla-La Mancha, Spain. His work focuses on high-performance interconnection networks, congestion control, and routing algorithms for large-scale systems. Research Themes: High-Performance Computing (HPC), Congestion Management, Adaptive Routing, Fat-Tree Networks, Network Simulation, Quality of Service (QoS) Publication Trends: Recent articles address congestion control in Dragonfly/Slim Fly networks, hybrid routing strategies, energy-efficient interconnects, and scalable simulation frameworks for exascale/big-data architectures.
Francisco J. Andújar Muñoz is an Associate Professor at the University of Valladolid in the Department of Computer Science since January 2024. His career spans multiple institutions including Universidad de Castilla-La Mancha (2008-2015) and Universitat Politècnica de València (2017-2018), with academic roles ranging from Research Assistant to Juan de la Cierva Formación Researcher. PhD in Advanced Computer Science Technologies (2011-2015) MsC in Advanced Computer Science Technologies (2010-2011) Computer Science Engineering (2008-2010) Computer Science Technical Engineering (2004-2008) His research focuses on high-performance interconnection networks , with significant contributions to quality-of-service mechanisms, energy-efficient network topologies, and heterogeneous programming optimization. He maintains the open-source VEF Traces framework for network workload modeling. Recent publications (2023-2025) demonstrate expertise in FPGA high-level synthesis portability, SYCL-based GPU optimization, and machine learning applications for Twitch streaming analysis. His work combines theoretical network design with practical implementations in the Journal of Supercomputing and IEEE Transactions on Computers .
Tomás Fernández Pena is a Full Professor at the University of Santiago de Compostela (USC) and Senior Researcher at the Research Center in Intelligent Technologies (CiTIUS) . With a career spanning over three decades, he has held academic positions since 1990 and contributed extensively to High Performance Computing (HPC), Big Data, and emerging quantum computing fields. Ph.D. in Physics from USC (1994) Senior Member of IEEE Associate Editor for IEEE Transactions on Computers and IEEE Access Research Contributions : His work focuses on parallel systems architecture, cloud computing middleware, and quantum simulation optimization. He has pioneered methods for NUMA systems, LiDAR data processing, and Big Data applications in bioinformatics/cheminformatics. His recent articles show increasing emphasis on quantum computing frameworks and distributed quantum processing. Scientific Recognition : Holds four Spanish Ministry of Education six-year research excellence periods (sexenios de investigación) and has served as Principal Investigator in 3 public projects and co-investigator in 31 EU/Xunta de Galicia funded initiatives. Supervised 7 Ph.D. theses and published 43+ international journal papers. International Collaborations : Maintains academic connections through funded research stays at Loughborough University, University of Tennessee, and University of Illinois Urbana-Champaign. Active in IEEE and participates in global conferences like Euro-Par and CHEP.
Juan Carlos Pichel Campos is a Full Professor at the University of Santiago de Compostela (USC) specializing in high performance computing and language technologies. His research spans quantum computing, distributed systems, and Big Data technologies with a focus on practical applications in health informatics and computational physics. He received his B.Sc. and M.Sc. in Physics from University of Santiago de Compostela (Spain) and completed his Ph.D. there in 2006. He conducted postdoctoral research at University Carlos III de Madrid and University of Illinois at Urbana-Champaign, and worked as a researcher and project manager at Galicia Supercomputing Center. Professor Pichel's research interests include parallel and distributed computing, Big Data technologies, programming models, and software optimization techniques for emerging architectures. His recent work has focused on bridging quantum computing with classical high performance computing systems, developing efficient algorithms for processing massive biological datasets, and creating tools for health-related information retrieval and misinformation detection. His interdisciplinary approach combines techniques from computer science, physics, and biomedical informatics to solve complex computational problems. Analysis of his recent publications reveals a strong trend toward quantum computing applications and integration with classical HPC systems (accounting for approximately 40% of his recent work), followed by health informatics and natural language processing (about 30%), and bioinformatics and computational physics (about 30%). His research demonstrates a consistent focus on developing practical tools and frameworks that address real-world computational challenges across multiple domains. rePowerSiC: High-Efficiency High-Power Laser Beaming In-Space Systems Based On Sic (2024-2028) C3HS: Content curation for consumer health search - Search and misinformation detection (2023-2026) Big-eRisk: Early Prediction of Personal Risks on Massive Data (2021-2024) eRISK: Technologies for the early prediction of signs related with psychological disorders (2019-2021) BigNLP: Approaching High Performance Computing to Big Data Technologies: Natural Language Processing as Case Study (2015-2018) Professor Pichel has established strong collaborations with research groups across Europe and the United States, particularly in the fields of quantum computing and biomedical informatics. He is actively involved with CiTIUS (Centro singular de investigación en tecnoloxías da información e da comunicación de USC), contributing to its mission of advancing information and communication technologies through interdisciplinary research.
