Find Academic Supervisors

• Sustainable Tourism
• Tourism Marketing
• Sharing Economy in Tourism
• +6 more

Sergio Moreno Gil is a Professor in the Department of Economics and Business Management at Universidad de Las Palmas de Gran Canaria (ULPGC), Spain. He is affiliated with the GIR TIDES research group (Economy, Environment, Sustainability and Tourism) and the Institute of Tourism and Sustainable Economic Development. His academic work focuses on tourism economics, marketing, and sustainability within the Social and Legal Sciences branch. Dr. Moreno Gil's research interests span across multiple dimensions of contemporary tourism. His primary focus areas include sustainable tourism development, destination marketing, sharing economy platforms in tourism, and consumer behavior analysis. He has made significant contributions to understanding tourism in the Canary Islands, particularly through his work with the Canary Islands Tourism Observatory. His research integrates traditional tourism studies with emerging fields like neurotourism and biometric analysis of tourist experiences. His publication record shows a clear trend toward addressing contemporary challenges in tourism. Recent works focus on sustainability in mass tourism destinations, the impact of sharing economy platforms, gastronomic tourism marketing, and collaborative tourism networks. His research demonstrates a strong methodological diversity, employing bibliometric analysis, social network analysis, and empirical studies of tourist behavior. Dr. Moreno Gil has been actively involved in the Canary Islands Tourism Observatory, contributing to annual reports on tourism sustainability in the region. His collaborative work spans across the Ibero-American region, with numerous co-authorships reflecting international research networks in tourism studies.

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.

Daniel RecasensView profile

Professor

• Experimental Phonetics
• Phonology
• Sound Change
• +7 more

Daniel Recasens is a Full Professor in the Department of Catalan Philology at the Faculty of Arts, Autonomous University of Barcelona, where he has been a permanent faculty member since 1985. He is also affiliated with the Institute for Catalan Studies as a researcher. His academic work centers on experimental phonetics and phonology, with a focus on Romance languages, particularly Catalan. Research Interests: Daniel Recasens specializes in Experimental Phonetics , Phonology , Sound Change , and Phonetic and Phonological Universals . His research investigates articulatory and acoustic aspects of speech production, including coarticulation, assimilation, blending, and the phonetic motivations behind diachronic sound changes in Romance languages. He employs advanced techniques such as ultrasound imaging, electropalatography (EPG), and acoustic analysis to study consonant clusters, vowel-consonant interactions, and the evolution of phonological systems. Scientific Publications and Trends: His recent publications (2020–2025) reflect a consistent focus on the articulatory and acoustic foundations of phonological processes. Key themes include dynamic blending in fricative sequences, tongue configuration in stop-liquid clusters, and the phonetic causes of palatalization and vowel changes. His work combines empirical data with theoretical models such as the Degree of Articulatory Constraint (DAC) model, contributing significantly to laboratory phonology and historical linguistics. Scientific Awards: Member, Academy of Europe (Academia Europaea), 2018 Advising and Research Grants: Daniel Recasens has mentored several doctoral and postdoctoral researchers, including Clara Isabel Rodriguez Casado, Aina Espinosa, and Fernando Sánchez-Miret. While specific grant details are not listed, his extensive publication record and leadership in experimental phonetics suggest sustained research funding and collaborative projects in phonetic and phonological research. Labs and Research Teams: He is associated with the Center for Theoretical Linguistics (CLT) at the Autonomous University of Barcelona, a research group dedicated to theoretical and experimental studies of language. His work often involves collaboration with phonetics laboratories and international scholars in the fields of articulatory phonetics and historical linguistics.

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

Carlos Alvarez Martinez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture at the Barcelona School of Informatics (FIB). He is a key member of the Programming Models (PM) research group and collaborates closely with the Barcelona Supercomputing Center (BSC). His research focuses on high-performance computing, FPGA acceleration, task-based programming models like OmpSs, and hardware-software co-design for heterogeneous systems. His research interests center on advancing parallel computing through innovative programming models and hardware acceleration. He investigates efficient task scheduling, resource management in multicore and FPGA-based systems, and runtime support for dataflow models. His work enables high-performance execution of complex applications in domains such as scientific computing and cyber-physical systems. He actively contributes to European initiatives like TEXTAROSSA and AXIOM, aiming to develop next-generation exascale supercomputing technologies. The trend in his recent publications shows a strong focus on leveraging FPGAs for HPC, optimizing SpMV operations, improving task scheduling with hardware support, and developing frameworks for multi-FPGA clusters. His work consistently bridges theoretical models with practical implementations, emphasizing performance, scalability, and energy efficiency in heterogeneous computing environments. Scientific Awards: Premi UPC al Compromís Social 2019 Premi Disseny per al Reciclatge 2013 Alvarez Martinez has advised or collaborated with several doctoral students, including Jaume Bosch, Xubin Tan, and Fahimeh Yazdanpanah. He has been involved in numerous competitive R&D projects, often related to high-performance computing and parallel programming models. His work includes significant contributions to educational innovation, particularly in active learning methodologies and formative assessment using interactive systems. He leads and participates in research labs and teams focused on programming models and computer architecture, notably the PM group at UPC/BSC. These teams develop runtime systems, compilers, and hardware accelerators to push the boundaries of parallel computing efficiency and programmability.

• 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.

Josep Marco PallaresView profile

Professor

• Neuroscience
• Cognitive Psychology
• Neuroimaging
• +3 more

Josep Marco Pallares is a Professor at the University of Barcelona's Faculty of Psychology, affiliated with the Department of Cognition, Development and Educational Psychology. He leads the Brain Dynamics and Structure of Human Cognition (BraCo) research group and holds an ICREA Academia Fellowship (2018-2023). His work focuses on neural mechanisms underlying reward processing, music perception, and social cognition using neuroimaging techniques. Education: Licenciatura in Psychology (University of Barcelona, 2000), Doctorate in Neuroscience (2012), and Doctorat (University of Barcelona, 2005) Research interests include brain oscillations in reward systems, music-evoked emotions, and decision-making. His recent studies explore how theta and gamma oscillations underpin pleasantness responses to music and social information. Ongoing projects investigate neural correlates of gambling behaviors and white matter correlates of music reward sensitivity. Awards: ICREA Academia Fellowship (2018) Has supervised doctoral theses on music reward processing and atypical reinforcer anticipation mechanisms. Active in teaching courses on neuroimaging techniques and music psychology at the University of Barcelona. Labs/Teams: Principal Investigator of the BraCo group, collaborating with AGAUR and Ministerio de Ciencia-funded projects.

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.

• 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.

• Aeronautics
• Mechanical Engineering
• Fluid Dynamics
• +5 more

Marco Raiola is an Associate Professor at the Department of Aerospace Engineering , Universidad Carlos III de Madrid (UC3M). His research focuses on fluid dynamics, turbulence, and aerodynamics, with applications in flow diagnostics, heat transfer, and control systems. Research Interests: Turbulent flows, data-driven modeling, particle image velocimetry (PIV), convective heat transfer, and bio-inspired aerodynamics. Projects: Principal researcher in INFLUENTIA-CM-UC3M (2024-2026) and Diagnóstico del ruido de chorro (2022-2025). Collaborator in EU-funded initiatives like HumanIC and ODE4HERA . Contact: Email mraiola@ing.uc3m.es | ORCID: 0000-0003-2744-6347

• Geotechnical Engineering
• Rock Mechanics
• Landslides
• +3 more

Eduardo Alonso Pérez de Agreda is a faculty member at the Universitat Politècnica de Catalunya in the Departament d'Enginyeria del Terreny, Cartogràfica i Geofísica . He leads research in geotechnical engineering and rock mechanics, particularly focusing on landslides, tunneling in expansive rocks, and multiphase soil interactions. Research Highlights Analyzing soil saturation dynamics using digital imaging Modeling tunnel lining in anhydritic claystones Studying mineral precipitation impacts on infrastructure Recent Article Trends Over 15 articles (2021–2025) on landslides, tunneling, soil liquefaction, and multiphase interactions Keywords span geotechnical engineering, computational methods, rock mechanics, and material science Subfields include stress-dilatancy, material heterogeneity, swelling rocks, and landslide triggering mechanisms Awards Baker Medal (2017) Telford Gold Medal (2019) Advising Advised PhD students: G. Di Carluccio (2020), C. Alvarado (2017), M. Alvarado (2021) Co-advised: L. Tapias, Y. Salami, R. Fuentes Labs & Collaborations Active in the MSR - Mecànica del Sòls i de les Roques and GGMM - Grup de Geotècnia i Mecànica de Materials research groups Collaborated with institutions in Spain, Italy, and China

• Internet of Things
• Transport Protocol
• Cybersecurity
• +8 more

Alan Briones Delgado is a researcher at the La Salle School of Engineering , Universitat Ramon Llull , with a focus on Internet of Things , Cybersecurity , and Transport Protocols . His work spans projects funded by the European Commission and national grants, including EXCEL4HOUSING4.0 , WeB-Nimbus , and NG-SOC , addressing challenges in cloud computing education, ecological monitoring, and security operations. His research integrates Artificial Intelligence and Wireless Sensor Networks for sustainable solutions. Key research areas include Quality of Service in heterogeneous networks, Environmental Conservation via IoT, and Teaching and Learning strategies for Big Data. Projects like EcoSentinel and BTL-COP highlight his commitment to Environmental Monitoring and Community Policing applications. His collaborations extend to institutions in the UK , Albania , and Western Balkans . Contact: alan.briones@salle.url.edu

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.

• Entrepreneurship
• 3D Scanning
• Additive Manufacturing
• +4 more

Roger Uceda Molera is an Associate Professor at the Universitat Politècnica de Catalunya (UPC) in the Department of Mechanical Engineering, affiliated with the Barcelona Higher Technical School of Industrial Engineering (ETSEIB). He is a member of the DigiFACT research group, focusing on advanced manufacturing technologies. His expertise spans entrepreneurship, 3D scanning, additive manufacturing, 3D printing, Industry 4.0, IoT, and mechanization. He has contributed to over 25 research activities, including articles, patents, and awards. Roger’s research emphasizes innovative additive manufacturing techniques, such as hybrid multi-material 3D printing and applications in medical prototyping. His work includes developing materials for concrete additive manufacturing and optimizing ceramic sintering processes. He has collaborated with companies like BCN3D Technologies and led projects funded by public-private partnerships. Key awards include the 2019 Premi Nacional al Partenariat Publicoprivat en R+I and the World to NYC Global Industry Challenge (W2NYC). He holds a patent for extrusion systems in cementitious material manufacturing. His research group, DigiFACT, drives advancements in digital factory technologies and industrial innovation.

Paolo RotaView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Multimodal AI
• +9 more

Paolo Rota is a tenure-track Assistant Professor at the University of Trento, affiliated with the Department of Information Engineering and Computer Science (DISI) and the Center for Mind/Brain Sciences (CIMeC). His research lies at the intersection of computer vision, machine learning, and multimodal AI, with a strong emphasis on vision-language models and activity recognition. His research interests include zero-shot action recognition, temporal action localization, open-world recognition, and person image synthesis. He explores how large multimodal models can be leveraged for practical applications in video analytics and industrial AI, often developing training-free or source-free adaptation methods that improve model generalization. Recent publications show a consistent trend in utilizing large vision-language models (e.g., CLIP, LMMs) for tasks such as image classification, domain adaptation, and action recognition, emphasizing simplicity, zero-shot capabilities, and real-world applicability. His work frequently appears in top venues including CVPR, NeurIPS, ICCV, and ICIAP. He actively mentors PhD students including Benedetta Liberatori, Jiaqi Liu, Yan Shu, Shiyao Xu, and Alessandro Conti, often co-advising with faculty such as Elisa Ricci and Nicu Sebe. He also contributes to teaching, including delivering lectures on machine learning for the MSc in Data Science program. He co-founded Mountain Maps, a startup using AI to enhance outdoor navigation and mountain exploration. His work bridges academic research and practical innovation, aiming to increase the real-world impact of AI systems.