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Antonio GonzálezView profile

Professor

• Computer Architecture
• Microarchitecture
• Compilers
• +7 more

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.

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 .

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

Abolfazl SimorghView profile

Researcher

• Aircraft Trajectory Optimization
• Climate Change Mitigation in Aviation
• Robust Control
• +3 more

Abolfazl Simorgh is a researcher at Charles III University of Madrid's Department of Aerospace Engineering, specializing in climate-optimized aviation systems. His work bridges mathematical control theory with practical climate impact mitigation, focusing on robust trajectory optimization under environmental and operational uncertainties. He leads development of open-source tools for sustainable flight planning while contributing to major European aviation initiatives. Education: B.Sc. in Control Engineering (2017) M.Sc. in Control Engineering (2020) Ph.D. in Aerospace Engineering from Charles III University of Madrid Dr. Simorgh's research centers on developing mathematical frameworks that reconcile aircraft trajectory optimization with climate impact reduction. His expertise spans robust control systems, optimization under uncertainty, and climate modeling integration, with particular emphasis on non-CO₂ emissions. His methodology addresses both CO₂ and non-CO₂ climate forcing mechanisms through computationally efficient algorithms that account for weather variability and climate metric uncertainties. This work directly supports aviation's decarbonization by providing operational strategies that reduce environmental footprint without prohibitive cost increases. Analysis of his 15 most recent publications reveals a cohesive research trajectory focused on operationalizing climate-optimal flight planning. His work consistently integrates climate science with aerospace engineering through robust optimization frameworks, demonstrating particular innovation in handling multiple uncertainty sources (weather, climate models, emissions). The publications cluster around three interconnected themes: 1) Development of open-source computational tools (ROOST, CLIMaCCF), 2) Network-scale implementation of climate-aware air traffic management, and 3) Risk analysis of climate mitigation strategies. This body of work establishes new methodological standards for quantifying and minimizing aviation's total climate impact. Scientific Awards: Luis Azcárraga Aeronautical Innovation Award for collaborative research impact Best Paper Award (2022) from a high-impact aerospace journal Dr. Simorgh secures significant research funding through European Commission projects including FlyATM4E (climate-optimized flight planning), ALARM (aviation emissions reduction), and RefMAP (sustainable aviation pathways). His grant portfolio emphasizes practical implementation of climate mitigation strategies, with strong industry-academia collaboration. He mentors junior researchers through project teams and has developed three major open-source Python libraries (CLIMaCCF, ROOST, ROC) that have become community standards for climate impact assessment in aviation research. His current work focuses on scaling climate-optimized trajectories to continental airspace while addressing operational constraints and economic viability. He leads a research group focused on climate-aware air traffic management, developing the ROOST simulation framework for GPU-accelerated trajectory optimization and the CLIMaCCF library for standardized climate metric calculations. His team collaborates with European air navigation service providers and aircraft manufacturers to transition research into operational practice, with current projects emphasizing real-time implementation and regulatory compliance frameworks.

• Computer Architecture
• Microarchitecture
• Compilers
• +7 more

Antonio Maria Gonzalez Colas is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Faculty of Computer Science of Barcelona (FIB). He leads the ARCO research group focused on Microarchitecture and Compilers and is actively engaged in high-impact research in computer architecture, GPUs, and energy-efficient computing. His collaborations extend to the Barcelona Supercomputing Center and various national and European research initiatives. Research Interests: His primary research areas include computer architecture, microarchitecture, compilers, GPUs, and processor design. He focuses on energy-efficient computing, deep neural network (DNN) accelerators, GPU simulation and optimization, memory systems, and architectural support for machine learning and autonomous systems. His work often integrates compiler techniques with hardware design for performance and efficiency. Scientific Production Trends: His recent publications demonstrate a strong focus on energy-efficient hardware for AI workloads, particularly DNN and speech recognition acceleration, GPU architectural innovations, memory optimization, and real-time rendering. He frequently publishes in top-tier venues such as ISCA, MICRO, HPCA, and IEEE/ACM journals. ICREA Academia Award 2024 HiPEAC 2024 Paper Award ACM Senior Member (2020) Advising and Grants: He has advised numerous PhD students whose theses cover topics like energy-efficient architectures for autonomous driving, speech recognition, and neural networks. He leads competitive R&D projects, including an ERC Advanced Grant and projects funded by the Spanish National Program and the ICREA Academia program, focusing on domain-specific architectures and cognitive computing units. Labs and Teams: He is the principal investigator of the ARCO (Microarchitecture and Compilers) research group at UPC, a leading team in computer architecture research in Spain. The group is part of a larger collaborative network within UPC and with international partners.

• Biomedical Imaging
• Medical Image Processing
• Neuroimaging
• +2 more

Dr. Manuel Desco Menéndez is a Full Professor at the Department of Bioengineering , Carlos III University of Madrid , focusing on biomedical imaging and instrumentation. He leads the Biomedical Imaging and Instrumentation Group. Key research domains: Medical Imaging , Neuroimaging , Biomedical Instrumentation Primary publication areas: Computed Tomography , PET Imaging , MRI , Biomaterials His scientific contributions (2022-2025) include: Neuroanatomical changes in pregnancy/postpartum and psychiatric disorders Advanced CT reconstruction algorithms with beam-hardening compensation Innovative biomedical nanoplatforms using milk exosomes Preclinical bone regeneration scaffolds in animal models Current technical developments focus on GPU-accelerated image processing and deep learning applications in tomographic reconstruction.

• 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.

Tiark RompfView profile

Assistant Professor

• Compiler Design
• Programming Languages
• Runtime Code Generation
• +8 more

Tiark Rompf is an Assistant Professor at Purdue University , with research spanning programming languages, compilers, and systems. His work bridges domains including architecture, databases, machine learning, and AI through projects like Reachability Types and Rhyme. Co-director of the Purdue Center for Programming Principles and Software Systems (PurPL) Scientific Advisor at SambaNova Systems Previously a member of the Scala team at EPFL His research focuses on: Runtime code generation and advanced compiler technology Expressive data-centric query languages (Rhyme, Datalog) Reachability type systems for memory safety and effect handling Metaprogramming and logical relations for formal verification Recent publications highlight contributions to Datalog compilation (Flan), nested data structures (Rhyme), and polymorphic reachability types. He leads projects exploring: Compiler optimizations for emerging architectures (GPU, TPU, FPGA) ML-driven compiler improvements Secure multi-party computation via metaprogramming Scientific awards include: NSF CAREER Award (2016) Google Faculty Research Awards (2017, 2018) DOE Early Career Research Award (2017) ACM SIGPLAN PL Software Award (2019) GPCE Test of Time Award (2020) Students and alumni from his group have joined institutions like Databricks, DeepMind, Galois, and Meta. He teaches advanced compiler courses including: CS 590 - Advanced Topics in Compilers CS 352 - Compilers CS 502 - Graduate Compilers

• Time Series Analysis
• Energy Analytics
• Statistical Clustering
• +3 more

Dr. Andrés Modesto Alonso is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Energy Analytics research group and Flores de Lemus Institute. His work spans computer science, economics, and statistics through advanced time series analysis and energy forecasting methodologies. Primary affiliation: Department of Statistics, UC3M Research groups: Energy Analytics, Flores de Lemus Institute His research focuses on time series analysis , energy forecasting , and statistical clustering with applications in electricity markets, smart grids, and environmental data. Recent publications emphasize deep learning models for energy prediction, dynamic factor models, and market-based distance metrics. Scientific output trends show 15 recent articles (2018-2024) covering topics like: Electricity market price forecasting Smart grid optimization through clustering Adaptive control charts for industrial processes Precision matrix estimation in high-dimensional statistics Extreme value analysis for environmental monitoring Dr. Alonso has supervised multiple theses on time series modeling and classification techniques, while collaborating on grants related to stochastic optimization and responsible AI applications in economic forecasting.

DAVID LOPEZ VILARIÑOView profile

Professor

• Computer Architecture
• Computer Vision
• FPGA Acceleration
• +3 more

David Lopez Vilariño is a **Professor** at the **University of Santiago de Compostela**, affiliated with the **Department of Electronics and Computing** within the **Faculty of Physics**. He earned his PhD in 2001 with a thesis titled *"Active contours at the pixel level: design and implementation on cellular network architectures,"* advised by Dr. Diego Cabello Ferrer. His research focuses on **Computer Architecture**, **FPGA Acceleration**, **LiDAR Data Analysis**, and **Embedded Systems**, with notable contributions to LiDAR-based applications in urban planning, infrastructure monitoring, and medical imaging. He is part of the **ARQCOMP (Computer Architecture)** and **Artificial Vision** research groups. His work spans topics such as high-performance computing, parallel processing, and hardware optimization for vision-capable systems. Key projects include developing FPGA-based solutions for real-time video surveillance, retinal vessel analysis, and autonomous navigation systems. Publications emphasize **LiDAR data processing**, including algorithms for road detection, power line characterization, and 3D point cloud analysis. He also pioneered tools like the *Open Lidar Visualizer and Analyser* for 3D stereoscopic visualization. His expertise bridges hardware design and software development, particularly in leveraging FPGAs for embedded vision systems. No scientific awards or grants are explicitly listed, but his prolific publication record highlights sustained innovation in computer vision and geospatial technologies. His research team collaborates on projects involving manycore systems, GPU acceleration, and reconfigurable computing architectures.

• Computer Architecture
• Programming Models
• Parallel Computing
• +4 more

Daniel Jimenez Gonzalez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the School of Informatics (FIB). He is an active researcher in programming models and high-performance computing, contributing extensively to the field through publications, projects, and academic supervision. Research Interests: His work focuses on Computer Architecture , Programming Models , and Parallel Computing . He investigates task-based programming, runtime systems, compiler optimizations, and performance modeling for multi-core and heterogeneous architectures. His research addresses challenges in scalability, energy efficiency, and resilience in modern computing environments. His recent publications show a consistent trend in task-based programming models , runtime scheduling , and performance optimization across diverse architectures, including NUMA and GPU-accelerated systems. The work spans from theoretical modeling to practical implementation, often targeting real-world HPC applications. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students are not listed, his involvement in doctoral theses and R&D projects suggests an active role in mentoring graduate students. He has participated in both competitive and non-competitive R&D+i projects, indicating grant acquisition and project leadership experience in areas related to programming models and computer architecture. Labs and Teams: He is a member of the UPC PM - Programming Models research group, which focuses on the design, analysis, and optimization of modern programming paradigms for high-performance systems.

Oscar Romero MoralView profile

Professor

• Data Management
• Data Science
• Data Governance
• +7 more

Oscar Romero Moral is a Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Services and Information Systems Engineering at the Barcelona School of Informatics (FIB). He leads research in the inSSIDE, inLab FIB, and DTIM groups, focusing on data management, data science, and big data technologies. His work emphasizes knowledge graphs, data governance, and machine learning integration with data systems. Affiliations: UPC, inSSIDE, inLab FIB, DTIM Group Research Interests: Data Management, Data Engineering, Big Data, Knowledge Graphs, Data Governance, Machine Learning Integration He has authored over 276 academic contributions, including peer-reviewed articles on federated healthcare data systems, GPU-accelerated workflows, and graph-driven data integration. His recent work addresses challenges in heterogeneous computing, automated data governance, and scalable data architectures. Romero has served on the program committees of major conferences like VLDB, ICDE, and EDBT, and led competitive research projects in data systems and analytics. He collaborates extensively with industry partners and academic institutions, driving innovations in distributed data management and edge computing.

Paula López MartínezView profile

Professor

• Computer Vision
• Electronic Design of Intelligent Devices
• Energy Harvesting
• +4 more

Paula López Martínez is a Full Professor at the University of Santiago de Compostela (USC) and Deputy Director of the Singular Research Centre on Intelligent Technologies (CiTIUS) . Since June 2024, she has held the Televés Microelectronics Chair at USC. Her work bridges Computer Vision and Electronic Design of Intelligent Devices , with a focus on CMOS Image Sensors and Biomedical Applications . Her research spans Energy Harvesting , Device Modeling , and Low-Power Embedded Systems . Key projects include SEMIoTICS (low-power AI at the edge), MISEL (multispectral vision), and NANOEATERS (nanotechnology transfer). She has authored over 100 peer-reviewed papers and served in IEEE leadership roles, including Secretary of the Sensory Systems Technical Committee and Treasury of IEEE Women in Engineering Spain (2006–2008). Recent publications highlight Analog Computing-In-Memory , HDR CMOS Sensors , and Energy-Efficient DC-DC Converters . Her work integrates Photovoltaic Energy Harvesting with Biomedical Device Design , emphasizing Self-Powered Systems and Dynamic Vision Sensors . She employs Verilog-AMS and ATLAS Simulations for CMOS Photodiode Modeling , addressing Crosstalk and Shot Noise in pixel arrays. Notable collaborations include Victor M. Brea, Diego Cabello, and Miguel Heredia Conde. She contributes to IEEE Transactions and Q1-Ranked Journals , with a focus on Hardware Acceleration via FPGA and Custom IC Design . Her expertise spans Thermoelectric Generators , Smart Energy Systems , and Radiation-Hard Transistors .

• Computer Science
• Parallel Computing
• Embedded Systems
• +3 more

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.