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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.

• Machine Learning
• Deep Learning
• Graph Analysis
• +6 more

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.

Giovanni CompianiView profile

Associate Professor

• Industrial Organization
• Quantitative Marketing
• Econometrics
• +6 more

Giovanni Compiani is an Associate Professor at the University of Chicago Booth School of Business, specializing in Marketing. His research bridges industrial organization and quantitative marketing, focusing on advanced econometric methods. PhD, MPhil, MA in Economics from Yale University BSc, MSc in Economics from Bocconi University Previous Assistant Professor at Haas School of Business His work explores unstructured data integration in demand estimation, consumer search behavior on online platforms, risk preferences in cryptocurrency markets, and time perception in behavioral economics. He has published in top journals including Journal of Political Economy , Marketing Science , and Review of Economic Studies . Recent publications emphasize machine learning applications in econometrics, equilibrium modeling of lotteries, and crypto mining's economic impact. His research portfolio spans demand analysis, structural modeling, and behavioral insights. Editor's Choice Award, The Review of Asset Pricing Studies (2024) Developed nonparametric demand estimation frameworks Advances dynamic model identification with instrumental variables Compiani teaches Data Science for Marketing Decision Making at Booth and maintains active collaborations with researchers across econometrics, computer science, and behavioral disciplines.

• Quantum Safe Cryptography
• Threat Management
• Artificial Intelligence Security
• +5 more

Dr. Marc Ph. Stoecklin is a Principal Research Staff Member and head of the Security Research Department at IBM Research Europe in Zurich, Switzerland. He co-leads IBM's global research strategy on Quantum Safe Cryptography and Migration, focusing on cryptographic artifact identification and migration implementation. Previously, he directed Threat Management research, applying AI and automation to threat detection, investigation, and response. Education: PhD in Computer, Communication and Information Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland His research spans quantum safe cryptography, threat management, AI security, threat detection, identity management, cyber deception, big data analytics, and security visualization. He pioneered AI-powered security operations, contributing to Watson for Cyber Security and QRadar Advisor with Watson, and analyzes AI/quantum computing misuse in cyber attacks. His 2016-2020 publications emphasize advanced threat detection, malware analysis, and AI security, featuring evasive malware deactivation, threat intelligence computing, and neural network watermarking. These works bridge research and product development, directly influencing IBM security offerings. Dr. Stoecklin led IBM's Cognitive Cyber Security Intelligence group (2014-2019) and the Security Research Department since 2019. As tech lead for IBM's COVID-19 task force (2020-2022), he developed IBM Digital Health Pass, deployed in New York's Excelsior Pass with over 1 million passes issued in two months. He heads the Security Research Department at IBM Research Europe in Zurich and has been integral to the Global Security Analysis Lab (GSAL) and Cognitive Cyber Security Intelligence (CCSI) group at IBM T.J. Watson Research Center.

Alysson Neves BessaniView profile

Associate Professor

• Distributed Systems
• Dependability
• Fault Tolerance
• +5 more

Alysson Neves Bessani is an Associate Professor at the Informatics Department of Faculdade de Ciências da Universidade de Lisboa, Portugal, and a member of the LaSIGE research group. His work focuses on distributed systems, Byzantine fault tolerance, and cybersecurity, with significant contributions to blockchain consensus and intrusion-tolerant architectures. Academic Rank: Associate Professor University: Universidade de Lisboa School: Faculdade de Ciências Department: Informatics Department Research Groups: LaSIGE, Navigators Research Interests span distributed systems design, Byzantine fault tolerance, adaptive consensus protocols, and secure multi-cloud storage. His work bridges theoretical foundations with practical implementations like the BFT-SMaRt library and the Vawlt startup. Scientific Awards include multiple Test-of-Time Awards (DSN'24, DSN'21), IBM Faculty Award (2017), and Best Student Paper at Middleware'19. He has advised numerous PhD and Master’s students, contributing to advancements in fault-tolerant systems. Publications (15 most recent) reveal trends in Byzantine consensus optimization, blockchain integration, and AI-driven threat detection. His interdisciplinary work combines distributed computing with genomics and IoT security, reflecting a broad impact across computer science.

Pablo CallejaView profile

Research Fellow

• Natural Language Processing
• Medical Informatics
• Legal Informatics
• +5 more

Pablo Calleja is a Research Fellow at the Faculty of Computer Science, Polytechnic University of Madrid (UPM), where he has been a member of the Ontology Engineering Group (OEG) since March 2014. His research focuses on Natural Language Processing (NLP), medical terminology mapping, and legal domain applications. He holds a degree in Computer Engineering from San Pablo CEU University (2013) and has prior industry experience as a software developer at IECISA (2008–2011) and a collaboration grant at the Open Access Classroom, San Pablo CEU University (2011–2013). Key contributions include projects like Drugs4covid for pandemic drug discovery, TermitUp for terminological enrichment, and esT5s , a Spanish text summarization model. His work spans legal knowledge graphs, multilingual compliance systems, and NER techniques for academic content analysis. He has also explored accessibility multimedia services and semantic graph applications in tourism ( DBtravel ). Professional roles include collaboration grants at UPM and active participation in interdisciplinary projects such as SNOMED-CT annotation for medical technical sheets. Research trends emphasize cross-domain adaptation (e.g., K-Flares), data augmentation (Widaug), and multilingual NLP solutions. Advising and grants: His current position is supported by a collaboration grant at OEG. Earlier grants include work at San Pablo CEU University. No formal advisees are listed, but he contributes to collaborative research teams. Labs and teams: Core member of the Ontology Engineering Group (OEG), focusing on knowledge representation, NLP, and applied informatics in healthcare and law domains.

Yann PontyView profile

Researcher

• RNA Folding
• RNA Design
• RNA Evolution
• +9 more

Yann Ponty is a tenured CNRS Researcher at the Computer Science Department (LIX) of École Polytechnique (Institut Polytechnique de Paris, France). He leads the AMIBio team and serves as Deputy Director of LIX. His work focuses on developing bioinformatics methods at the intersection of computer science, mathematics, and molecular biology, particularly for RNA structure prediction, design, and evolution. He holds leadership roles in the ISCB Board of Directors (2025-2027) and the HDR referent for the IDIA department (CS&Interactions) at IP Paris. Research Interests: RNA folding/design/evolution, RNA-RNA/RNA-protein interactions, random generation, enumerative combinatorics, discrete algorithms, parameterized complexity, RNA visualization Key Contributions: Developed algorithms for RNA inverse folding, pseudoknot modeling, and dynamic programming optimization Collaborations: Partnerships with institutions like Simon Fraser University, Boston College, and Université Paris-Saclay His recent publications (15 most recent) span RNA structure prediction, pseudoknot partition functions, linear-time inverse folding algorithms, and parameterized sampling techniques. The work emphasizes dynamic programming, combinatorial approaches, and integration of experimental data for improving RNA modeling. Scientific awards include election to the ISCB Board of Directors (2025-2027) and leadership roles in academic networks like GdR BIM. He actively contributes to software development (VARNA, RNANR, SPARCS, IncaRNAtion, RNARedPrint) and serves as Associate Editor for Bioinformatics (OUP). Teaching engagements include graduate-level courses in combinatorial optimization, RNA bioinformatics, and algorithms at Université Paris-Saclay and École Polytechnique.

• Natural Language Processing
• Machine Learning
• Deep Learning
• +5 more

Ahmed AbuRa'ed is a Researcher at the Department of Information and Communication Technologies (DTIC) at Universitat Pompeu Fabra (UPF), Barcelona. He is affiliated with the TALN research group and the Large-Scale Text Understanding Systems Lab. His work focuses on advancing knowledge in scientific text summarization, information extraction, and machine learning. Education: PhD in Computer Science (2020), UPF, Barcelona, Spain M.Sc. in Computer Science (2015), University of Trento, Italy B.Sc. in Computer Information Systems (2007), An-Najah University, Nablus, Palestine Research Interests: Natural Language Processing (NLP), Machine Learning/Deep Learning, Semantic Web, Information Extraction, Data Mining, and Scientific Document Summarization. His projects include developing systems for automatic generation of state-of-the-art reports, scientific text summarization, and cross-document relation discovery. Publications Focus: His 15 most recent articles (2016–2021) emphasize advancements in scientific literature analysis, including citation detection, text simplification, and cross-document summarization. Notable works involve systems like LaSTUS/TALN for scientific text processing and OlloBot for Arabic health dialogue agents. Labs & Teams: Active member of the TALN research group and the Large-Scale Text Understanding Systems Lab at UPF's DTIC department. Open to collaborations in NLP, Machine Learning, and related fields via email or Skype.

• Signal Processing
• Biomedical Engineering
• Audio Signal Processing
• +3 more

Antonio Salvador Pena Gimenez is a full-time researcher at the Universidade de Vigo , affiliated with the School of Telecommunications Engineering and the Department of Signal Theory and Communications . He holds a PhD from the Polytechnic University of Madrid (1995) for his work on Psychoacoustic modeling techniques applied to very high quality audio coding , supervised by Dr. Luis Alfonso Hernández Gómez. Education: PhD in Signal Theory and Communications (Polytechnic University of Madrid, 1995) University Degree in Full-Time Mode His research spans signal processing , biomedical engineering , and audio signal analysis , with a focus on medical applications (e.g., ECG processing) and music information retrieval (e.g., beat tracking, onset detection). He has contributed to non-invasive vital sign monitoring and data compression techniques in audio and biomedical contexts. The 15 most recent articles (prioritized by year) highlight his work in polyphonic music analysis , rhythmic structure modeling , and medical signal compression . Key trends include machine learning for audio processing , biomedical signal analysis , and algorithm development for telemedicine . He has collaborated with institutions including Instituto Politécnico de Setúbal , Instituto de Telecomunicações , and University of Cambridge . His work has been cited 567 times, with an h-index of 9. He is involved in underwater acoustic databases (e.g., ShipsEar) and smartphone-based telecare systems , indicating ongoing work in sensor networks and mobile health applications .

Adrián García GutiérrezView profile

Professor

• Aerospace Engineering
• Uncertainty Quantification
• Computational Fluid Dynamics
• +3 more

Adrián García Gutiérrez is a Professor in the Department of Aerospace Engineering at the University of León's College of Engineering. His research focuses on aerospace systems, uncertainty quantification in CFD, atmospheric boundary layer modeling, and airship technology. Recent publications highlight his work in parallel orbital propagation algorithms, stochastic optimization of high-altitude platforms, and neural network applications for wind profiling. Key trends include aerodynamic modeling under uncertainty and interdisciplinary approaches combining turbulence analysis with LiDAR measurements. He contributes to educational innovation in aerospace engineering through simulation-based learning tools and leads the GITA Tecnología Aeroespacial research group.

• Artificial Intelligence
• Data Fusion
• Sensor Fusion
• +4 more

JESUS GARCIA HERRERO is a Full Professor at the Department of Computer Science , Carlos III University of Madrid , and serves as Director of the Postgraduate School of Engineering and Basic Sciences and the Master's Degree in Applied Artificial Intelligence. His research focuses on Artificial Intelligence , Data Fusion , and Maritime Surveillance , with applications in UAV navigation , sensor integration , and fuzzy systems . Key Research Areas: Machine Learning, Trajectory Analysis, Contextual Data Fusion, Robotics, Maritime Security, and Smart Grids. Notable Projects: ASPID (2024-2027), MARVISION (2024-2025), SIMBAT (2021-2024), and HADA (2023-2024). Contact: jesus.garcia@uc3m.es , jgherrer@inf.uc3m.es

• Artificial Intelligence
• Machine Learning
• Social Network Analysis
• +4 more

Andreas Kaltenbrunner is a Part-Time Lecturer at the Universitat Pompeu Fabra (UPF) in Barcelona, specializing in Data Driven Social Analytics and Information Retrieval. He serves as Director of Research on AI and Data Science at IN3 - UOC R&I and is an Affiliate Researcher at ISI Turin, Italy. His research focuses on computational social science, user behavior analysis, and applications of AI/ML in social media and network dynamics. Key interests include large language models (LLMs), personality assessment simulations, and digital epidemiology. His work bridges theoretical frameworks with practical applications, such as predicting urban mobility patterns and evaluating AI systems’ performance in political contexts. He collaborates with institutions like UNICEF and the ISI Foundation, contributing to projects on data-driven decision-making and social impact. His interdisciplinary approach integrates network science, computational methods, and socio-cultural analysis. Recent publications highlight innovations in LLM validation, cross-lingual Wikipedia reliability modeling, and climate-related forecasting. While no explicit awards are listed, his prolific output reflects sustained contributions to computational social science and AI ethics.

José María Guerrero RodríguezView profile

Professor

• Electronics
• Sensor Design
• Space Instrumentation
• +3 more

José María Guerrero Rodríguez serves as a Professor in the Department of Automation, Electronics, Architecture and Computer Networks Engineering at the University of Cadiz, Spain. His research is anchored within the TIC138 Electronic and Electromagnetic Design group under the PAIDI Areas for Information and Communication Technologies, focusing on advanced instrumentation systems for space and industrial applications. He completed his doctorate at the University of Cadiz in 2009 with the thesis A contribution to reconfigurability in intelligent instrumentation based on sensors and programmable devices , supervised by Dr. Diego Gómez Vela. His educational background forms the foundation for his expertise in sensor reconfigurability and programmable device integration. Guerrero Rodríguez's research spans Electronics, Embedded Systems, and Space Instrumentation, with significant contributions in magnetic measurement subsystems for CubeSats, low-frequency noise reduction in orbital environments, and neuromorphic sensor design. His work bridges theoretical electromagnetic modeling with practical embedded implementations, particularly in spiking neuron-based movement classification and optical flow algorithms for real-time tracking. Educational innovation is a secondary focus, evidenced by flipped classroom methodologies in analog electronics instruction. Analysis of his 2020-2024 publications reveals three dominant trends: (1) Space instrumentation for CubeSats emphasizing magnetic field measurement accuracy, (2) Novel sensor architectures using asynchronous vision and spiking neurons for flame detection and movement tracking, and (3) Educational technology integration in engineering pedagogy. These areas demonstrate consistent progression from fundamental sensor design to in-orbit validation. No scientific awards were documented in the provided source material. While the source references thesis supervision and funding sections, specific student names, grant details, or advising methodologies remain undisclosed. His research direction suggests ongoing collaboration with space technology initiatives, particularly in low-technology-readiness-level subsystem validation. Guerrero Rodríguez operates within the TIC138 research group, which specializes in electronic and electromagnetic design for aerospace applications. The group's work centers on advancing the technological maturity of measurement systems through CubeSat platforms, with recent focus on in-orbit demonstrators for magnetic field characterization in low-Earth orbit environments.

• Computer Architecture
• Parallel Processing
• Memory Systems
• +7 more

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

• Computational Engineering
• Numerical Methods for PDEs
• High-order Methods
• +6 more

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