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Claudia Patricia Ayala MartinezView profile

Lecturer

• Empirical Software Engineering
• Off-The-Shelf Adoption
• Requirements Engineering
• +4 more

Claudia Patricia Ayala Martinez serves as a Lecturer in the Department of Service and Information Systems Engineering at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia (UPC). She is actively involved in research through the GESSI - Group of Software and Service Engineering and the UPC inSSIDE - integrated Software, Services, Information and Data Engineering research groups. Her career spans over two decades of academic contributions in software engineering with consistent publication output. Dr. Ayala Martinez's research focuses on Empirical Software Engineering, Off-The-Shelf Adoption, Requirements Engineering, and Software and Architectural Quality. Her work demonstrates an evolution from traditional software engineering topics toward increasing integration with machine learning and AI systems. Recent publications show particular emphasis on software quality indicators, ML pipeline design principles, trustworthiness of ML models, and green computing in software systems. Analyzing her publication trends reveals a consistent research trajectory with growing focus on AI/ML integration in software engineering. Her work spans empirical studies, systematic literature reviews, and practical industrial applications. The research shows strong connections between software quality metrics, architectural decisions, and emerging technologies, with increasing attention to ethical considerations in ML systems and sustainability in software development. Most-Influential Paper Award at the 30th IEEE International Requirements Engineering Conference Dr. Ayala Martinez has participated in numerous competitive R&D projects including those funded by the Spanish National Research Plan, Horizon 2020, and the Catalan Innovation Strategy. Her collaborative network includes extensive work with Professor Javier Franch Gutierrez (69 joint publications), Silverio Juan Martinez Fernandez (26 joint publications), and Cristina Gomez Seoane (20 joint publications). Her research has been supported by various national and European funding programs focusing on software engineering, quality assessment, and open source adoption. She is actively involved with the GESSI and inSSIDE research groups at UPC, which focus on integrated software, services, information, and data engineering. These groups maintain strong industry connections and have produced significant research in empirical software engineering, reference architectures, and quality assessment methodologies. Her recent work shows increasing collaboration with researchers working at the intersection of software engineering and artificial intelligence.

Pere-Pau VázquezView profile

Assistant Professor

• Information Visualization
• Scientific Visualization
• Medical Data Visualization
• +6 more

Pere-Pau Vázquez is an Assistant Professor in AI for Visual Computing at the Computer Vision Lab, TU Wien, Austria . Previously, he held academic positions at the ViRVIG Group and Facultat d'Informàtica de Barcelona (UPC) , where he taught courses in Programming, Computer Graphics, and Visualization for over 20 years. His research focuses on Information Visualization, Scientific Visualization, Medical Data Visualization, Molecular Visualization, and AI applications to Visual Computing . Current Teaching : Data Visualization, Fast Realistic Rendering, Information Visualization, Medical Images, Scientific Visualization, Virtual Reality, and 3D Medical Visualization. Former PhD Students : Elena Molina, Alexandra Cortez, Jesús Díaz, Pedro Hermosilla, Eva Monclús. His scientific awards include the Best PhD Thesis Award (UPC, 2003), Best Student Paper Award (SPIE, 2012), and Best Paper Award (International Conference on Computer Graphics Theory and Applications, 2013). Recent publications explore AI integration in biomedical visualization, molecular data analysis, and interactive techniques for volume rendering. He serves on the EuroGraphics Executive Board as Secretary and is active in steering committees for EuroVis and Visual Computing for Biology and Medicine . His work bridges Computer Graphics, Artificial Intelligence, and Human-Computer Interaction , with applications in medical and molecular data analysis.

• Statistical Modeling
• Biostatistics
• Biomedicine
• +7 more

Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.

• Wind Turbines
• Autonomous Learning
• Deep Learning
• +8 more

Yolanda Vidal Segui is an Associate Professor in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola d'Enginyeria de Barcelona Est (EEBE). Her research focuses on wind energy systems, predictive maintenance, and structural health monitoring of wind turbines. She leads projects in the CoDAlab and WinTurCoM research groups, specializing in data-driven models, condition monitoring, and failure prognosis. Her work integrates machine learning, mathematical modeling, and sensor technology to enhance turbine reliability and energy efficiency. Dr. Vidal holds a PhD in Applied Mathematics and has authored over 350 publications. Her contributions include advancements in SCADA data analysis, vibration-based diagnostics, and AI-driven condition monitoring systems. She has received several accolades, including the WindEurope Technology Workshop recognition and the IFIT Distinction in Mechanism and Machine Science. Her research bridges academia and industry, addressing challenges in offshore wind turbine integrity and maintenance strategies. Active in professional service, she serves on conference committees and editorial boards (e.g., Mechanical Systems and Signal Processing, Wind Energy). Her work emphasizes sustainable energy solutions and has been applied in real-world scenarios like the Alpha Ventus wind farm. She also contributes to educational initiatives, developing innovative teaching materials for engineering students.

• Fluid Dynamics
• Turbulence
• Heat Transfer
• +6 more

Andrea Ianiro is a Full Professor in the Aerospace Engineering Department at Universidad Carlos III de Madrid (UC3M), where he leads research in fluid dynamics, turbulence, and heat transfer. His work bridges experimental techniques and machine learning applications for flow analysis and control. He serves as Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) and directs the EFM Lab (Experimental Fluid Mechanics Laboratory) at UC3M. Professor Ianiro's research focuses on turbulence characterization, boundary layer flows, and the application of machine learning to fluid mechanics problems. His work spans experimental techniques including Particle Image Velocimetry (PIV), infrared thermography, and advanced data processing methods. Recent research emphasizes data-driven approaches for flow field reconstruction, turbulence control, and heat transfer optimization in wall-bounded flows. His projects often combine theoretical, experimental, and computational approaches to address complex fluid mechanics challenges. The analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional fluid mechanics. His work increasingly focuses on using deep learning techniques (particularly CNNs and GANs) for flow field prediction from limited measurements, developing meshless computational methods for flow analysis, and applying optimization techniques (including genetic algorithms) to heat transfer enhancement. His research maintains a strong experimental foundation while embracing data-driven approaches to tackle turbulence modeling challenges. Associate Editor of the International Journal of Heat and Mass Transfer (2025-2028) Professor Ianiro leads multiple significant research projects including SPANDRELS (SParse AND paRsimonious Event-based fLow Sensing, 2025-2030), HumanIC (Human-Centric Indoor Climate for Healthcare Facilities, 2024-2027), and EXCALIBUR (Extraction of machine learning strategies for turbulent flow control, 2023-2026). His work has attracted funding from the European Commission, Spanish National Research Agency, and industry partners including Airbus. He has supervised numerous theses on topics including AI-based sensing of turbulent flows, convective heat transfer control, and turbulent boundary layers. At UC3M, Professor Ianiro directs the Experimental Fluid Mechanics Laboratory (EFM Lab), which focuses on advanced measurement techniques for fluid flow and heat transfer characterization. The lab specializes in PIV/PTV techniques, infrared thermography, and the development of novel experimental approaches for turbulence research. Current research directions include machine learning applications for flow field reconstruction, plasma-based flow control, and heat transfer optimization in complex flow configurations.

• Cloud Computing
• Green Computing
• High-Performance Computing
• +7 more

Jordi Guitart Fernández is a Professor at the Department of Computer Architecture, Barcelona School of Informatics (FIB), Universitat Politècnica de Catalunya (UPC). He is also affiliated with the Barcelona Supercomputing Center (BSC-CNS), a leading national supercomputing facility. He leads the CROMAI research group, focusing on Computing Resources Orchestration and Management for AI. His work bridges high-performance computing, cloud systems, and artificial intelligence. Research Interests: Cloud Computing and Edge Computing Green and Energy-Efficient Computing Containerization and Virtualization for HPC Resource Orchestration and Management Autonomic and Self-Adaptive Systems Machine Learning Workflow Management AI-Driven System Optimization His recent publications reveal a strong focus on intelligent management of computing resources across cloud, edge, and HPC environments using machine learning and agent-based frameworks. He investigates performance, efficiency, and reliability in containerized AI and HPC workloads, particularly within Kubernetes and distributed infrastructures. His work increasingly integrates human-in-the-loop and trustworthiness aspects into AI systems. Scientific Awards: CLOUD Conference 2025 Best Paper Award VISIGRAPP 2025 Best Student Paper Award Premi Extraordinari de Doctorat 2025 - Àmbit d'Enginyeria de les TIC Test of Time Award Honorable Mention (e-Energy) Reconeixement als Mèrits Docents d'Especial Qualitat Top reviewers for Polytechnic University of Catalonia (Computer Science) - September 2017 Advising and Grants: He has advised doctoral students, including Peini Liu. He leads and participates in numerous competitive R+D+i projects, such as CROMAI and DALEST, funded by national and European programs like HORIZON 2020 and the Spanish State Research Plans. His work is supported by grants focused on knowledge generation and industrial leadership in computing technologies. Labs and Teams: He is the leader of the CROMAI - Computing Resources Orchestration and Management for AI research group at UPC. He also collaborates closely with the Barcelona Supercomputing Center (BSC-CNS), contributing to large-scale computing initiatives and strategic research agendas in Europe.

Agustín Zaballos DiegoView profile

Assistant Professor

• Smart Grids
• Internet of Things (IoT)
• Sensor Networks
• +3 more

Agustín Zaballos Diego is an Assistant Professor in the Department of Computer Engineering at University Ramon Llull (URL), Barcelona, Spain, since 1999. He serves as Research Coordinator in the Department of Engineering at La Salle Campus Barcelona and leads the R&D Networking and Security Area since 2002. His academic background includes a PhD in Data Networks and Internet Technologies (2012), an International MBA (2014), and an M.S. in Electronic Engineering (2000). University: University Ramon Llull (URL) Department: Department of Computer Engineering Research Group: GRITS Research Focus: Real-time QoS-aware routing protocols in Smart Grids, Ubiquitous Sensor Networks, and IoT communications. His work bridges telecommunications, computer science, and energy systems through projects like OPERA (FP6), INTEGRIS (FP7), and FINESCE (FP7). Publication Trends: Recent articles highlight advancements in HF communications for Antarctic research, hybrid genetic algorithms for traffic engineering, IPv6 testing, and Industry 4.0-related networking solutions. Keywords span Smart Grids, IoT, Sensor Networks, and QoS optimization. Collaborative Projects: Key initiatives include the Antarctica Project , ATHIKA (ICT in healthcare), ENVISERA (environmental sensor networks), HOTSUP (online teaching innovation), PLANET4 (AI/ML in industry), and XIoT (IoT scalability challenges).

Alejandro VillaverdeView profile

Research Professor

• Systems Biology
• Control Theory
• Mathematical Modeling
• +5 more

Alejandro F. Villaverde is a Ramón y Cajal research fellow in the Department of Systems & Control Engineering at the School of Industrial Engineering, University of Vigo, Spain. He also serves as a Research fellow at CITMAga since 2022. Previously, he worked as a postdoctoral researcher at IIM-CSIC from 2016-2020. His research focuses on the modeling of dynamical systems with particular emphasis on biological applications. Villaverde earned his PhD in Systems and Control Engineering from University of Vigo between 2005 and 2009. His academic career has centered at Spanish institutions with a strong interdisciplinary approach bridging engineering, mathematics, and biology. His primary research interests include systems biology, control theory, and mathematical modeling, with specialized expertise in structural identifiability, observability analysis, and computational tools for dynamic modeling of biological systems. Villaverde's work addresses fundamental challenges in building reliable mathematical models of complex biological processes, with applications spanning immunology to microbial communities. His theoretical contributions have practical implications for improving model reliability and predictive power in biological research. Villaverde has published extensively in top journals including PLOS Computational Biology, Bioinformatics, and IEEE/ACM Transactions on Computational Biology. His recent publications (2023-2025) reveal a consistent research trajectory focused on developing theoretical frameworks for biological model analysis, creating practical software tools, and applying these methods to cutting-edge problems. His work shows particular strength in identifying and addressing fundamental limitations in modeling approaches, especially regarding parameter identifiability and model observability constraints. Among the top 2% Scientists Worldwide 2024 (Stanford University list) Recognition as one of the EEI's top valued instructors at University of Vigo's School of Industrial Engineering Villaverde leads multiple significant research projects including DYNAMO-bio (funded by Ministry of Science, Innovation and Universities), SICOMORO (focusing on symmetries in biological communities), and PREDYCTBIO. His group actively develops open-source software tools such as STRIKE-GOLDD for structural identifiability and observability analysis. The laboratory, part of the BICO research group, includes several researchers and students working on various aspects of dynamic modeling in biology, with recent additions including Mahmoud Shams Falavarjani, Adriana González Vázquez, and multiple interns working on specialized projects.

Felix Gomez MarmolView profile

Associate Professor

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +5 more

Felix Gomez Marmol is an Associate Professor at the University of Murcia's Faculty of Informatics, Department of Information and Communication Engineering. His research focuses on cybersecurity, artificial intelligence, network security, and IoT security. He holds a PhD in Computer Science from the University of Murcia (2010), supervised by Dr. Gregorio Martínez Pérez. Key research interests include adaptive intrusion detection systems, dark web analysis, and AI-driven cybersecurity frameworks. He leads the Intelligent Systems and Telematics research group and previously contributed to the Sistemas Inteligentes group. His work emphasizes practical applications such as the SCORPION Cyber Range platform for cybersecurity training and gamification. Recent projects involve detecting hate networks on social media, optimizing malware defense using transfer learning, and developing SIEM systems for IoT environments. His contributions span technical papers on cybersecurity education, ethical hacking fundamentals, and blockchain-based security solutions. Prof. Gomez Marmol has collaborated on initiatives like the COBRA framework for simulating advanced persistent threats (APTs) and the COnVIDa dashboard for pandemic-related data analysis. His research bridges theoretical advancements with real-world cybersecurity challenges.

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +7 more

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework

• Bayesian Statistics
• Financial Econometrics
• Markov Chain Monte Carlo
• +5 more

Nicholas Polson is the Robert Law, Jr. Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His academic career centers on Bayesian statistics with applications in financial econometrics and machine learning. Polson's research interests span Bayesian statistics, financial econometrics, Markov chain Monte Carlo methods, particle learning, and deep learning applications in finance. His work has significantly contributed to understanding stochastic volatility models and developing new algorithms for Bayesian inference. He has pioneered applications of deep learning in asset pricing, portfolio management, and financial prediction, demonstrating how neural networks can detect complex patterns invisible to traditional financial models. His recent publication trends reveal a strong focus on integrating deep learning with financial econometrics, particularly in developing characteristics-sorted factor models, portfolio optimization techniques, and explaining the performance differences between active and passive investment strategies. His work consistently bridges theoretical statistical methods with practical financial applications, with a particular emphasis on nonlinear modeling and high-dimensional data analysis. His article 'Bayesian Analysis of Stochastic Volatility Models' was named one of the most influential articles in the 20th anniversary issue of the Journal of Business and Economic Statistics Polson teaches courses including 'Bayes, AI and Deep Learning' and 'Business Statistics' at Chicago Booth, with scheduled offerings for both 2024-2025 and 2025-2026 academic years. His work has been featured in Chicago Booth Review, where he has contributed insights on statistical analysis in chess, machine learning applications in money management, and the odds of cheating in competitive settings. His research demonstrates the powerful intersection of Bayesian statistics, financial modeling, and modern machine learning techniques.

TANIA FERNÁNDEZ VILLAView profile

Associate Professor

• Preventive Medicine
• Public Health
• Nutritional Epidemiology
• +5 more

Tania Fernández Villa is an Associate Professor in the Department of Biomedical Sciences at the Faculty of Veterinary Medicine, University of León. Her primary academic affiliation centers on preventive medicine and public health research within the GIIGAS (Interactions Gene-Environment-Health) research group. Her research spans nutritional epidemiology, gender studies in food systems, substance use disorders, and chronic disease prevention. Key focus areas include alcohol consumption patterns among university students (via the UniHcos cohort), sarcopenia in metabolic syndrome, breast cancer risk factors, and pandemic impacts on health behaviors. Her work integrates longitudinal cohort analysis, systematic reviews, and psychometric validation studies. Recent publications demonstrate strong trends in gender-disaggregated health research, pandemic-related behavioral shifts, and methodological innovations in dietary assessment. Her 2024-2025 output shows increasing emphasis on environmental sustainability of diets and multi-cancer risk prediction models. She serves on the editorial board of the Spanish Journal of Human Nutrition and Dietetics, contributing to strategic planning (2020-2026) and open science initiatives. Her PhD from Universidad de Granada focused on ICT usage patterns among university students. Current projects include the UniHcos longitudinal study tracking health behaviors in Spanish university students, with particular attention to alcohol use, sleep patterns, and nutritional status. Her research group actively investigates gene-environment interactions in public health contexts.

Martín Alegre, SaraView profile

Professor

• Gender Studies
• Science Fiction Literature
• Cultural Analysis
• +5 more

Sara Martín Alegre is a Professor in the Department of English and German Studies at the Autonomous University of Barcelona (UAB). She specializes in Gender Studies, Science Fiction, and Cultural Analysis, with a focus on posthumanism and masculinity in media. Her research explores themes such as gender fluidity, AI ethics, and the intersection of popular culture with academic discourse. She has directed numerous research projects and supervised doctoral theses, including works on human-eating monsters in film and transhumanism in literature. Her teaching includes courses on contemporary Anglophone literature, gender studies, and Victorian literature. She actively collaborates in publishing initiatives, such as e-books with students and academic monographs on topics like masculinities in cinema and science fiction. Her work bridges traditional literary analysis with modern digital humanities, emphasizing the role of popular fiction in academic exploration. Award-winning scholar and author, her contributions include monographs like *Detrás de la máscara: masculinidades americanas en el documental contemporáneo* (2023) and edited volumes on topics ranging from children in cinema to 21st-century miniseries. Her research highlights the sociopolitical implications of gender representation in media, from Shakespearean adaptations to modern science fiction narratives.

• Cybersecurity
• Internet of Things (IoT)
• Post-Quantum Cryptography
• +5 more

Florina Almenares Mendoza is an Associate Professor at the Telematics Engineering Department of Carlos III University of Madrid , where she also serves as the Director of the University Master's Degree in Cybersecurity. Her research focuses on addressing security challenges in emerging technologies such as IoT, post-quantum cryptography, and privacy-preserving systems. Email: florina.almenares@uc3m.es Contact: 916246234 Location: 4.0.F06 - Quevedo Towers (Leganés) Research Interests Florina's work spans cybersecurity , Internet of Things (IoT) , and post-quantum cryptography , with a focus on scalable authentication, quantum-resistant protocols, and privacy. She explores machine learning applications for security, federated identity management , and smart grid security frameworks. Recent Publications Her recent research includes papers on DNSSEC soft delegation, hybrid quantum security for TLS/IPsec, PUF-based authentication in IoT, and blockchain-enabled auditability. These studies emphasize IoT security , quantum-resistant algorithms , and privacy-enhancing technologies .

• Decision Making
• Information Sampling
• Machine Learning Applications
• +4 more

Gaël Georges Marcel Le Mens is a Full Professor at Pompeu Fabra University (UPF), holding a position in the Department of Economics and Business. He is also affiliated with the Barcelona School of Economics and serves as academic co-director of the Executive Master in Business Administration (EMBA) at the UPF Barcelona School of Management. His academic journey includes teaching roles at INSEAD, London Business School, ESADE, and the University of Lugano, alongside positions at the universities of Southern Denmark and New York. Education: Doctor in Business Administration, Stanford Graduate School of Business MSc in Management Science and Engineering, Stanford University Diploma in Engineering, Supélec Bachelor of Economics, University of Paris XI His research focuses on decision-making processes, information sampling, machine learning applications in semantics, and organizational behavior. Key themes include cognitive heuristics, social media impact on political expression, and the interplay between popularity and evaluation dynamics. He has explored how feedback mechanisms shape political communication and developed methodologies to compare human and machine conceptual judgments using models like BERT. His publications span journals such as PNAS , Psychological Review , and Industrial and Corporate Change , reflecting his interdisciplinary approach. Though no explicit awards are noted, his prolific output highlights sustained academic impact. He has advised multiple institutions on curriculum design and executive education, leveraging his cross-university teaching experience. Le Mens is affiliated with the Barcelona School of Management’s research teams and contributes to initiatives bridging artificial intelligence and social sciences. His work often addresses practical challenges in organizational decision-making and digital communication strategies.