جستجوی استاد راهنمای دانشگاهی

Luis Miguel Bergasa is a Full Professor at the Department of Electronics, University of Alcalá (UAH), with a career spanning over 25 years. He leads the RobeSafe Lab (since 2010) and serves as Director of Digital Transformation at UAH (since 2022). His academic roles include heading the Department of Electronics (2004–2010) and coordinating multiple educational programs. He teaches Perception Systems (Master in Industrial Engineering) Intelligent Control Systems (Computer Science) Computer Vision (Computer Science) His research focuses on Perception Systems for Intelligent Vehicles , emphasizing driver behavior analysis, scene understanding, and sensor fusion via deep learning. He has authored over 300 papers and holds 9 patents. Notable recognitions include being ranked #81 in Computer Science (Spain) by Research.com (2025) and receiving 30+ awards in Robotics/Automotive fields. Recent publications highlight advancements in Transformer-based driver action recognition (2025) Infrastructure-vehicle cooperative frameworks (2025) 3D semantic segmentation for autonomous perception (2024) Simulation-to-reality gap bridging (2024) Scientific Leadership Senior Editor, IEEE Transactions on ITS (2025) International Program Committee roles in 15+ conferences (2024–2025) Co-founder of Vision Safety Technologies Ltd (2009–2016) He supervises 9 active PhD students and has advised 14 former PhD candidates. His group collaborates with institutions in Germany, USA, China, and Spain, including KIT, UC Berkeley, and Northwestern Polytechnic University.

• Perception Systems
• Autonomous Vehicles
• Deep Learning
• +۴ مورد دیگر

Carlos Antonio Andújar Gran is a faculty member at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Computer Science. He actively contributes to the academic community through teaching and supervising numerous student projects, particularly in advanced computing domains. His research interests center on the application of computer graphics, virtual reality, and artificial intelligence to real-world problems. Key areas include the 3D visualization and virtual reconstruction of cultural heritage, physically accurate rendering, intelligent segmentation of images, and the use of machine learning and reinforcement learning in sports analytics, particularly for padel. His work bridges technical innovation with practical applications in cultural preservation and sports technology. The recent publications under his supervision reflect a strong trend in digital cultural heritage, with multiple projects focused on tools for virtual visits, reconstruction, lighting simulation, and web-based 3D visualization of heritage models. Another significant trend is the application of AI to padel, including ball and player tracking, virtual coaching, and training virtual agents using reinforcement learning. These projects demonstrate a consistent focus on computer vision, interactive systems, and data-driven modeling. Carlos Antonio Andújar Gran has supervised a substantial number of students, guiding both undergraduate and master's theses. While specific grant information is not available in the text, his sustained supervision across many years suggests active involvement in research projects. He plays a key role in mentoring the next generation of computer scientists at UPC. His work is closely tied to the Department of Computer Science at FIB, where he collaborates with other faculty members such as Imanol Muñoz Pandiella, Núria Pelechano Gómez, and Mohammadreza Javadiha on various student projects, indicating a collaborative research environment focused on graphics, AI, and VR applications.

• Computer Graphics
• Virtual Reality
• Cultural Heritage Visualization
• +۷ مورد دیگر

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

• Artificial Intelligence
• Robotics
• Data Science
• +۴ مورد دیگر

Rafael Barea Navarro is a Professor in the Department of Electronics Technology at the University of Alcalá. His research focuses on biomedical engineering, autonomous systems, robotics, artificial intelligence, and driver safety. He leads the Biomedical Engineering Research Group (GIB) and the Robótica de Servicios y Tecnologías para la Seguridad Vial (Robesafe) group. His work bridges AI applications in medical diagnostics (e.g., multiple sclerosis via OCT) and advanced autonomous vehicle technologies, including motion prediction, reinforcement learning, and real-time safety systems. Education: PhD in Electronics from the University of Alcalá (2001), with a thesis on EOG-based human-computer interfaces for mobility assistance. His research spans over 50 peer-reviewed articles since 2000, emphasizing practical applications in healthcare and robotics. Key research themes include: 1) AI-driven medical diagnostics; 2) autonomous vehicle control systems; 3) sensor fusion for navigation; and 4) driver attention monitoring. His work integrates robotics, computer vision, and biomedical signal processing. Publications emphasize interdisciplinary approaches, with recent work combining OCT and explainable AI for early disease diagnosis, and hybrid reinforcement learning frameworks for urban autonomous driving.

• Biomedical Engineering
• Autonomous Systems
• Robotics
• +۴ مورد دیگر

Toni Susin is an Associate Professor of Applied Mathematics at UPC-BarcelonaTech. He leads the Dynamic Simulation Lab, part of the ViRVIG research group in Barcelona. His research focuses on numerical methods, physically-based simulation, and applications in computer graphics and biomechanics. Key research areas include Physically-Based Animation Surgical Simulation Biomechanical Applications Image-Based Modeling Fluid Animation Techniques His recent work spans microbiome data analysis, sports performance tracking, and biomedical simulations. While no scientific awards are listed, his career includes founding three tech companies and mentoring numerous PhD/Master's students in computational methods and simulation technologies.

• Numerical Methods
• Physically-Based Simulation
• Computer Graphics
• +۴ مورد دیگر

Antonio Calomarde Palomino is an Associate Professor at the Department of Electronic Engineering, Polytechnic University of Catalonia (UPC), located at the Higher Technical School of Industrial Engineering of Barcelona. He holds a B.S. in Telecommunications Engineering (UPC, 1986), an M.S. in Electronic Engineering (Universitat Autònoma de Barcelona, 1993), and a Ph.D. in Electronic Engineering (UPC, 2007) with honors. His research focuses on low-power/high-performance digital circuits and soft-error resilient designs, addressing challenges in semiconductor technology and radiation-hardened electronics. Key research areas include FinFET-based memory cells, RRAM modeling, and radiation mitigation strategies for advanced nanoelectronics. He contributes to the High Performance Integrated Circuits and Systems Design (HIPICS) Group, advancing circuit design methodologies and educational initiatives such as integrating autonomous vehicle systems into undergraduate curricula. His work bridges theoretical advancements with practical applications in automotive electronics and embedded systems. Publications span topics from memristor-based associative memory to approximation techniques in object detection systems, reflecting a balance between fundamental research and applied engineering. His recent work emphasizes variability-aware design and energy-efficient architectures, as seen in 2023-2024 studies on RRAM models and automotive case studies. Calomarde actively organizes academic events like the 2022 ETS conference and develops educational materials for digital electronics and audiovisual systems courses. His contributions to both technical research and pedagogical innovation solidify his role as a key figure in electronic engineering education and innovation.

• Low Power Digital Circuits
• Soft Error Resilient Circuits
• Integrated Circuit Design
• +۴ مورد دیگر

Ignacio Arganda Carreras is an Associate Professor at the Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU) and an Ikerbasque Research Associate, affiliated with the Donostia International Physics Center (DIPC). His research focuses on biomedical computer vision, with a strong emphasis on deep learning applications in microscopy and medical imaging. Key areas include bioimage analysis pipelines, domain adaptation for cross-modal image segmentation, and AI-driven solutions for healthcare diagnostics. He has contributed extensively to open-source tools like BiaPy, CartoCell, and DL4MicEverywhere, which advance accessibility to deep learning in bioimaging. His work bridges computational methods with biological and medical challenges, addressing issues like 3D object detection, super-resolution imaging, and automated classification in microscopy and clinical settings. Research highlights include developing the MitoEM and Nucmm datasets for mitochondria and neuronal nuclei segmentation, as well as innovative applications in wound healing modeling and aquaculture monitoring. His methodologies emphasize reproducibility, generalization, and mitigation of overfitting in deep learning models.

• Biomedical Computer Vision
• Deep Learning
• Medical Imaging Analysis
• +۳ مورد دیگر

Víctor Manuel Brea Sánchez is an Associate Professor at the Research Center on Intelligent Technologies (CiTIUS) within the University of Santiago de Compostela (Spain). His work focuses on hardware implementations for computer vision and CMOS-3D technologies . Research Areas : Computer Vision, Electronic Design, Low-Power Embedded Systems Projects : AZOR (Search, Location, Rescue), Power Management Unit Chip for Energy Harvesting His recent publications emphasize CMOS-based vision sensors , analog computing-in-memory , and spatio-temporal feature extraction . Trends include deep learning acceleration, event cameras, and hardware for real-time object detection. Scientific contributions include the Best Paper Award at the 2003 European Conference on Circuit Theory and Design. Collaborations with researchers like Manuel Mucientes and Paula López highlight interdisciplinary efforts in AI-driven hardware design.

• Computer Vision
• Electronic Design of Intelligent Devices
• CMOS-3D Hardware Implementation
• +۲ مورد دیگر

Daniel Cores Costa is an Assistant Professor at the University of Santiago de Compostela, affiliated with the Department of Electronics and Computing under the Higher Technical School of Engineering. His research focuses on advanced computer vision techniques, including spatio-temporal convolutional neural networks, few-shot learning, and generative adversarial networks (GANs) for small object detection. He earned his Doctorate from the same university in 2022 with a thesis titled Spatio-temporal convolutional neural networks for video object detection , advised by Dr. Victor Manuel Brea Sánchez and Dr. Manuel Mucientes Molina. His work spans video object detection, medical imaging applications (e.g., COVID-19 diagnosis), and autonomous systems like UAV navigation. Key areas include improving data efficiency via pseudo-label mining, enhancing GAN-based data augmentation pipelines, and developing spatio-temporal feature aggregation techniques for dynamic scenes. He collaborates with the GSI Group (Intelligent Systems Group) , contributing to interdisciplinary projects at the intersection of AI, robotics, and healthcare. Publications highlight trends in few-shot learning for medical imaging, video-language evaluation benchmarks (TVBench), and real-time UAV-based object detection. Notable contributions address challenges in small object detection in videos and X-ray vision systems. His research emphasizes practical applications in healthcare diagnostics and autonomous systems. Despite no explicit mention of awards or grants, his prolific publication record (2015–2025) underscores sustained academic engagement. Advising details are unavailable, but his involvement in thesis supervision and collaborative groups like GSI indicates active mentorship.

• Computer Vision
• Deep Learning
• Machine Learning
• +۳ مورد دیگر

Antonio Agudo Martínez is a Research Scientist at the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of the Spanish National Research Council (CSIC) and Polytechnic University of Catalonia (UPC). His work spans computer vision, robotics, and machine learning with applications in medical rehabilitation, sports analytics, and renewable energy systems. He holds advanced degrees in Industrial Engineering, Computer Science, and a PhD in Computer Vision and Robotics from the University of Zaragoza. His research focuses on non-rigid structure from motion , 3D reconstruction , and deformation analysis , with significant contributions to medical applications (stroke rehabilitation), sports analytics (soccer trajectory modeling), and industrial systems (wind turbine monitoring). His publication record shows consistent high-impact output at top venues like CVPR, ICCV, and ECCV, with recent work emphasizing uncertainty quantification in diffusion models and multi-agent trajectory prediction. Notable scientific recognition includes a Best Paper Award Honorable Mention at ECCV 2018 for GANimation: One-Shot Anatomically Consistent Facial Animation. His research portfolio includes leadership of national projects like GreenVAR (renewable energy systems) and GRAvatar (realistic avatar generation), alongside contributions to SMARTGAZE II for low-vision mobility assistance. Dr. Agudo actively supervises doctoral research in computer vision applications, with current PhD candidates focusing on multimodal tracking for autonomous vehicles, soccer video understanding, sports metrics, and avatar generation. His international collaborations include work at Harvard University, Queen Mary University of London, University College London, Bordeaux University, and National University of Entre Ríos.

• Computer Vision
• Robotics
• Non-Rigid Structure from Motion
• +۳ مورد دیگر

Javier Ruiz Atencia is a PartTime Lecturer in the Department of Computer Engineering at Miguel Hernández University of Elche, specializing in Computer Architecture and Telecommunications Technology. He teaches courses including Operating Systems, Programming Languages, and Network Architecture. Current academic appointments at UMH Teaching responsibilities across multiple engineering programs Active research in video coding and perceptual quality optimization Technical expertise in HEVC/VVC standards and neural network applications Research focus centers on perceptual video coding techniques that leverage human visual system characteristics. His work explores: Hybrid contrast & texture masking models QP optimization algorithms Rate-distortion performance analysis Next-generation video compression strategies Perceptual quality assessment frameworks Technical Contributions include development of HEVC intra prediction emulators and network architecture implementations. Current projects focus on integrating dual hybrid neural networks for improved VVC encoding efficiency.

• Video Coding
• Perceptual Quality Optimization
• Neural Networks
• +۳ مورد دیگر

Manuel Felipe Muciéntes Molina is a Professor of Computer Science and Artificial Intelligence at the Research Center on Intelligent Technologies (CiTIUS), University of Santiago de Compostela (USC). He holds a PhD from USC (2002) focusing on fuzzy logic-based control systems for autonomous robots. His research emphasizes machine learning applications in computer vision, process mining, robotics, and healthcare. He teaches courses like Statistical Learning, Supervised Machine Learning, and Deep Learning. Key Projects: RAI4P: Responsible AI for Process Mining 2.0 QC4SUSTEX: Authentication and security of sustainable textiles CAREBOT: Intelligent robotic units for hospital care Research Interests: Machine learning, computer vision (object detection, tracking), process mining, robotics navigation, and healthcare AI applications. Labs/Teams: Leading the Grupo GSI (Systems Intelligence) within CiTIUS, focusing on intelligent systems and AI-driven solutions.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Cristina Losada Gutiérrez is a Professor at the Department of Electronics, Universidad de Alcalá, Spain. She leads research in the GEINTRA group, focusing on Electronic Engineering applied to Intelligent Spaces and Transport. Her work integrates computer vision, robotics, and deep learning for applications in smart surveillance, human activity analysis, and assistive technologies. She earned a PhD in 2010 with a thesis on 3D robot localization using static camera networks. Her research spans real-time action recognition, anomaly detection, and sensor fusion for healthcare and transportation systems. Notable projects include the GEMS Erasmus+ initiative (Ruby sensory module) and EU-funded EYEFUL systems for functional evaluation. Her recent publications emphasize edge computing frameworks for video analysis, weakly-supervised learning models, and robust object detection in dynamic environments. She actively contributes to educational robotics projects like Eurobot Spain and explores portable architectures for intelligent wheelchairs. Her lab develops multisensory systems for applications such as crowd safety (stampede detection) and railway obstacle detection, leveraging time-of-flight cameras and deep learning pipelines. She collaborates internationally on projects blending academic research with industrial and societal challenges.

• Computer Vision
• Robotics
• Deep Learning
• +۳ مورد دیگر

Victor Manuel Brea Sánchez is a Professor at the University of Santiago de Compostela , affiliated with the Department of Electronics and Computing within the Higher Technical School of Engineering . He holds a PhD from the same university, completing his thesis on Design of a mixed-signal CMOS integrated circuit for pixel-level snakes in 2003 under the guidance of Dr. Diego Cabello Ferrer and Dr. David López Vilariño. His research focuses on Computer Vision , CMOS Sensors , and Embedded Systems , with particular emphasis on real-time vision systems , time-of-flight imaging , and deep learning . He leads the Artificial Vision research group and contributes to the Center for Research in Intelligent Technologies (CITIUS) . His recent work includes advancements in high dynamic range vision sensors , event-based cameras , and analog computing-in-memory architectures . He has pioneered energy-efficient algorithms for multi-object tracking and small object detection in videos, often implemented on low-power embedded systems. Key contributions include the HOPBAS10K CMOS vision sensor for background subtraction and the STDnet convolutional neural network for small target detection. His research bridges hardware design (e.g., CMOS sensor architectures) with software (e.g., deep learning algorithms), emphasizing algorithm-hardware co-design for real-world applications like traffic monitoring and biomedical systems. Notable projects include the iCaveats initiative integrating vision architectures for embedded systems and a low-power voltage reference circuit for implantable devices. He has also developed a capacitance-based wireless sensor network for agricultural pest monitoring.

• Computer Vision
• CMOS Sensors
• Embedded Systems
• +۵ مورد دیگر

Manuel Bendaña Gómez is a predoctoral researcher at the University of Santiago de Compostela (USC), holding an FPU Research Fellowship granted by the Spanish Ministry of Education. He is currently enrolled in the Doctorate Program in Information Technology Research and conducts his research at Laboratorio P1. His educational background: Bachelor's Degree in Computer Engineering, Universidade de Santiago de Compostela (2022) Master's Degree in Computer Vision, coordinated by the universities of Santiago de Compostela, A Coruña, Vigo, and Porto His research focuses on computer vision with emphasis on multi-object tracking and image segmentation techniques applied to life-critical scenarios. He develops solutions for search and rescue operations and disaster response systems, particularly addressing object recovery challenges in complex environments through segmentation-enhanced tracking algorithms. This work bridges theoretical computer vision with practical emergency response applications. His publication on segmentation-mask-enhanced multi-object tracking demonstrates a key trend in leveraging deep learning for robust video analysis in high-stakes scenarios, contributing to the broader fields of artificial intelligence and public safety technology. Scientific recognition: FPU Research Fellowship (Spanish Ministry of Education) He is supported by the FPU grant and previously participated in research initiatives including CiTIUS summer scholarships (2021) and a merit-based research hiring at USC (2023) for master's thesis development. His work integrates academic training with practical emergency response applications. He actively contributes to research teams at Laboratorio P1 focused on Search, Location and Rescue of People on Land, and Fire Fighting and Prevention projects.

• Computer Vision
• Multi-Object Tracking
• Image Segmentation
• +۳ مورد دیگر