جستجوی استاد راهنمای دانشگاهی

• Computer Vision
• Mobile Devices
• Assistive Technology
• +۳ مورد دیگر

Dr. Juan Manuel Sáez Martínez is a full professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante's Higher Polytechnic School II. With a PhD in Computer Engineering and over two decades of academic experience since 2000, he specializes in Computer Vision and Mobile Device Applications for accessibility, particularly for the blind and visually impaired. His groundbreaking NaviLens project—a digital signage system patented internationally by the University of Alicante and licensed to Neosistec SL—represents a significant contribution to assistive technology. Teaching experience across 15+ years in programming fundamentals, computer systems, and internships for telecommunications and multimedia engineering Supervised one PhD thesis on structural similarity in object recognition Active in public research projects including airport navigation systems and airborne obstacle detection for the visually impaired His research interests focus on computer vision algorithms , stereo-based wearable devices , and ultra-efficient artificial vision systems for mobile platforms . Recent work includes biomedical informatics applications like 3D obstacle detection and accessibility-oriented UI design. He has participated in 10+ competitive public research projects (2016-2021) while contributing to technology transfer initiatives. Teaching contributions include 4 quinquennials (five-year terms) and 14+ years of instruction in Programming Fundamentals I for Telecommunication Sound and Image Engineering. Notable collaborations include co-authorship with Dr. F. Escolano on entropy-based SLAM algorithms and Gaussian mixture models for image processing. Current research emphasizes practical applications through the Mobile Vision Research Laboratory (MVRLab) .

José María Guerrero Rodríguezمشاهده پروفایل

استاد

• Electronics
• Sensor Design
• Space Instrumentation
• +۳ مورد دیگر

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.

Ana Puig Puigمشاهده پروفایل

استاد

• Gamification in Education
• Serious Games
• 3D Point Clouds
• +۹ مورد دیگر

Dr. Anna Puig Puig is a Professor at the University of Barcelona, affiliated with the Faculty of Mathematics and Computer Science within the Department of Mathematics and Computer Science. Her research focuses on gamification in education, 3D point cloud processing, data visualization, and biomedical applications of computer science. Key research areas include: Gamification and serious games for education 3D point cloud segmentation and analysis Machine learning applications in geospatial and biomedical domains Interactive visualization systems Virtual reality for archaeological and educational purposes Notable projects include: INDOMAIN - Pedagogical Module in Intelligent Tutoring Systems HORIZON 2020 European Cancer Image Platform GRAPES - Machine Learning for Shape Processing CLiC Research Group in Language and Computing Digital Reconstruction of Neolithic Social Life

Auxiliadora Sarmiento Vegaمشاهده پروفایل

استاد

• Audio Source Separation
• Biomedical Signal Processing
• Machine Learning
• +۵ مورد دیگر

Auxiliadora Sarmiento Vega is a full professor at the University of Seville 's School of Engineering within the Department of Signal Theory and Communications . With over two decades of research experience, her work bridges audio signal processing and biomedical applications, focusing on blind source separation, entropy-based methods, and machine learning for healthcare diagnostics. Research Pillars : Audio source separation, biomedical signal/image analysis, and virtual reality integration Key Projects : ACACIA (Signal Analysis), NEUBIAS (Bioimage Analysts Network), and multiple NIH-funded biomedical imaging initiatives Academic Contributions span 15+ years, with groundbreaking work in: Alpha-Beta divergence clustering algorithms EEG processing for motor imagery BCI systems Automated breast cancer grading from histological images Glaucoma and diabetic retinopathy diagnostics via retinal image analysis Virtual reality platforms for emotion analysis research She actively collaborates with institutions like the IEEE Women in Engineering (Spanish section secretary) and NEUBIAS network , while mentoring through outreach programs like g4g Day that empower young women in STEM.

Paula López Martínezمشاهده پروفایل

استاد

• Computer Vision
• Electronic Design of Intelligent Devices
• Energy Harvesting
• +۴ مورد دیگر

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

Federico Divinaمشاهده پروفایل

استاد

• Bioinformatics
• Evolutionary Computation
• Machine Learning
• +۵ مورد دیگر

Federico Divina is a Professor in the Department of Sports and Computer Science at Universidad Pablo de Olavide, Spain. His research focuses on evolutionary computation, machine learning, and bioinformatics. PhD in Artificial Intelligence from Vrije Universiteit Amsterdam Former postdoc at University of Tilburg (NEWTIES EU project) Research Interests : Bioinformatics, Evolutionary Computation, Machine Learning, Big Data, and Soft Computing. He specializes in knowledge extraction from massive datasets through genetic algorithms and heuristic optimization. Recent Publications span time series forecasting, feature selection in high-dimensional data, and biomedical image analysis. His work combines evolutionary algorithms with practical applications in energy consumption prediction, ocular disease diagnosis, and genomic data analysis. Labs & Collaborations : Leads the DS&BD Data Science & Big Data Lab and has collaborated with groups like DATAi and DASE. His projects include Differential (multi-university energy analysis), GALICIAME (SMA genetic data analysis), and NEWTIES (artificial society modeling).

Ignacio Arganda Carrerasمشاهده پروفایل

دانشیار

• Biomedical Computer Vision
• Deep Learning
• Medical Imaging Analysis
• +۳ 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.

Emilio Serranoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Dr. Emilio Serrano is an Associate Professor at the Department of Artificial Intelligence, Technical University of Madrid (UPM). He holds an M.Sc. in Computer Science from the University of Murcia (2006) and a Ph.D. with European Mention from the same institution (2011). His research focuses on AI ethics, machine learning, bioinformatics, and multi-agent systems. He has authored over 50 international publications, including 10 in top-tier JCR journals. He has participated in EU projects like FP7’s SMARTOPENDATA, EUROSentiment, and Omelette. Dr. Serrano has received the National Research Fellowship (FPU) and the Extraordinary Ph.D. Award. His teaching spans 20+ education science courses, 12 university modules, and postgraduate seminars. His work integrates AI with social simulations, environmental forecasting, and healthcare applications, emphasizing ethical AI and bias mitigation. Awards: FPU Fellowship (2008), Extraordinary Ph.D. Award (2012) Grants: FP7 Projects (SMARTOPENDATA, EUROSentiment, Omelette) Labs/Teams: Active in AI ethics, bioinformatics, and multi-agent systems research

Marcos Loureiro Garciaمشاهده پروفایل

استادیار

• Mathematics Applied to Cardiology
• Biomechanical Simulation
• Aortic Stenosis
• +۳ مورد دیگر

Dr. Marcos Loureiro Garcia is an Assistant Professor at the University of Vigo, affiliated with the Department of Mathematics within the Faculty of Education and Sport Sciences. He obtained his PhD in 2020 with a thesis on "Modeling and numerical approach of aortic valve stages: healthy, stenotic and transcatheter replaced (TAVI)". His career demonstrates interdisciplinary expertise combining mathematics, cardiology, and educational innovation. Doctorate in Mathematics Applied to Cardiology (2020) Master's in Industrial Mathematics Master's in Teacher Training Bachelor's Degree in Mathematics His research focuses on applying mathematical methods to cardiology, particularly for simulating aortic valve pathologies and transcatheter aortic valve implantation (TAVI) procedures. He has developed biomechanical models of aortic valves, investigated TAVI outcomes through numerical simulation, and explored educational technology applications for mathematics teaching. His academic work spans both computational cardiology and mathematics education. He has published on topics including deep learning for TAVI characterization, digital valve twins, and augmented reality applications in teacher training. His research addresses clinical challenges through mathematical modeling while contributing to innovative teaching methodologies. Scientific achievements include: Best Poster Prize in Numerical Simulation at ESC Congress (2018) Six-Year Research Career Recognition Grants from Ministry of Science, Xunta de Galicia, GAIN He collaborates with leading institutions like the Czech Technical University in Prague and the Center for Computational Medicine in Cardiology (Lugano), developed his doctoral research at the Galicia Sur Health Research Institute (IISGS), and currently participates in the RITME research group focused on educational innovation and mathematical modeling.

• Neuroscience
• Biomedical Engineering
• Signal Processing
• +۴ مورد دیگر

Carlos Guerrero Mosquera is a Contracted Professor at La Salle School of Engineering, Department of Engineering, La Salle University. His research focuses on the intersection of neuroscience, biomedical engineering, and signal processing, with particular emphasis on brain imaging techniques and cognitive processes. Dr. Guerrero Mosquera's research interests span multiple areas including: Electroencephalogram (EEG) signal processing and analysis Functional Near Infrared Spectroscopy (fNIRS) applications in neuroscience Cognitive fatigue and sleep deprivation effects on brain function Neuroergonomics and human performance monitoring Machine learning applications for biomedical signal processing Epileptic seizure detection and characterization His recent publication activity demonstrates a strong focus on multimodal neuroimaging approaches, particularly combining EEG and fNIRS to study cognitive processes. His work has increasingly incorporated machine learning techniques for signal processing and classification, with applications ranging from medical diagnostics to urban soundscapes analysis. The research shows a consistent thread of investigating brain connectivity changes during cognitive tasks and fatigue states, with significant contributions to noise reduction methods in neuroimaging data. Dr. Guerrero Mosquera has been actively involved in several research projects: Human-Environment Research (2022-2025) funded by Agència de Gestió d'Ajuts Universitaris i de Recerca MENTALTIC: Application of Information and Communication Technologies in Mental Health (2021) Deep learning for classification of anomalous acoustic events in real urban and suburban environments (2020) His laboratory work focuses on developing and optimizing methods for neuroimaging data acquisition and analysis, with particular expertise in handling noisy signals and improving the reliability of brain imaging techniques. He maintains an active research profile with publications spanning from 2005 to present, showing increased productivity in recent years with multiple publications in 2023 alone.

• Medical Image Analysis
• Machine Learning for Personalised Medicine
• Biomedical Engineering
• +۳ مورد دیگر

Gemma Piella Fenoy is a Full Professor at the Universitat Pompeu Fabra (UPF), Department of Engineering. She holds a Telecommunication Engineering degree from the Universitat Politècnica de Catalunya (UPC) and a PhD in Applied Sciences from the Universiteit van Amsterdam. Her career includes postdoctoral research at Télécom Paris (Marie Curie Fellowship) and a Ramón y Cajal Researcher position. She co-founded the SIMBIOsys research group and the Barcelona Center of New Medical Technologies (BCN-MedTech), leading its research in Medical Image Analysis and Machine Learning for Personalized Medicine. Her work bridges theoretical foundations with clinical applications, recognized by awards like the ICREA Academia (2021) and DonaTIC (2022). She is a member of ELLIS and Sigma Xi, and served as co-chair of the Avicenna Alliance's AI Task Force (2023-2024). Research focuses on medical imaging, machine learning in healthcare, fetal MRI analysis, and AI-driven diagnostics. Notable projects include the LiverColor platform for liver graft assessment and BabyFM for 3D fetal facial modeling. Her work emphasizes interdisciplinary collaboration between engineering and clinical medicine, addressing challenges in prenatal care, neurological disorders, and personalized medicine. She actively contributes to global health initiatives and AI ethics in clinical applications. Education PhD in Applied Sciences, Universiteit van Amsterdam (The Netherlands) Telecommunication Engineering Degree, Universitat Politècnica de Catalunya (Spain) Her scientific contributions span over 150 peer-reviewed articles in top journals and conferences, with a focus on medical image analysis, AI in healthcare, and clinical decision support systems. She leads BCN-MedTech, a consolidated research group recognized by the Catalan government, and collaborates with international institutions on AI-driven medical solutions.

• Biometrics
• Behavioral Biometrics
• Human-Computer Interaction (HCI)
• +۸ مورد دیگر

Rubén Vera-Rodríguez is a researcher at the Biometrics and Data Pattern Analytics (BiDA) Lab, part of the Escuela Politécnica Superior at Universidad Autónoma de Madrid, Spain. His work focuses on biometrics, human-computer interaction, and privacy-enhancing technologies, with strong emphasis on mobile and wearable systems. Research Interests: His primary research areas include behavioral biometrics (keystroke dynamics, gait recognition), face recognition, synthetic data generation, and privacy-preserving methods in biometric systems. He explores the use of deep learning and transformer models for mobile authentication and digital phenotyping. The trends in his recent publications show increasing use of synthetic data, explainable AI, and transformer architectures in biometric systems. His work spans both technical innovation and privacy-aware design, contributing to benchmark datasets and open challenges. Scientific Contributions: Co-organizer of the SVC-onGoing and WAMWB workshops. Contributor to major biometric databases including BehavePassDB and ChildCIdb. Active in privacy-focused biometric research, as seen in surveys on sensor vulnerabilities. Advising and Collaborations: He collaborates extensively with researchers such as Ruben Tolosana, Aythami Morales, and Julian Fierrez. While no formal students are listed, he leads and contributes to team-based research projects and competitions. Labs and Teams: He is a core member of the Biometrics and Data Pattern Analytics (BiDA) Lab at UAM, a research group focused on advancing biometric technologies with attention to real-world usability and ethical considerations.

• Biomedical Imaging
• Artificial Intelligence
• Image Processing
• +۳ مورد دیگر

María Jesús Ledesma Carbayo is a Professor at the Department of Electronic Engineering, Polytechnic University of Madrid, and leads the Biomedical Imaging Technologies Laboratory. She obtained her Telecommunications Engineering degree in 1998, followed by two Masters in Biomedical Engineering and Medical Physics at Universidad de Patras (Greece) and UNED (Spain), and earned her PhD in 2003 for contributions to non-rigid spatiotemporal registration methods in myocardial motion estimation. Education MSc in Biomedical Engineering and Medical Physics, Universidad de Patras MSc in Bioengineering, UNED PhD in Telecommunications Engineering, Polytechnic University of Madrid (2003) Her research focuses on biomedical image analysis, artificial intelligence for cardiac imaging, image-guided surgery, microscopy image analysis, and motion correction techniques. She has established international collaborations with institutions including Oxford University, EPFL, NIH, Johns Hopkins University, and UC San Diego. She has contributed to over 250 publications and leads national/international research projects, including collaborations with MIT through the MVision Consortium and MIT-linQ programs. She also actively participates in NGOs ONGAWA and Fundación EHAS. Scientific Recognition Senior Member, IEEE Coordinator, Catalyst Europe PhD Fellowship Program (EIT-Health) Faculty Member, MIT-linQ Program

• Artificial Intelligence
• Internet of Things
• Medical Imaging
• +۳ مورد دیگر

Begoña García-Zapirain Soto is a researcher at the School of Engineering , University of Deusto , with a focus on applying artificial intelligence, machine learning, and IoT systems to healthcare challenges. Her work spans medical imaging, biomedical signal processing, and eHealth technologies , addressing aging populations, chronic disease management, and preventive care. University of Deusto, School of Engineering, Computing, Electronics and Communication Technologies Researcher role since 2000 with continuous publication activity Research Interests include: AI/ML for disease classification (e.g., AMD, ischemic heart disease, breast cancer) Smart healthcare systems (e.g., air quality monitoring, gluten detection) Geriatric technologies (e.g., frailty/sarcopenia assessment, loneliness monitoring) Medical imaging segmentation (CT, MRI, dermoscopy) Elderly exergames (FRED platform) Signal processing for clinical applications Article Trends show consistent work in AI healthcare applications (2011-2025), with recent emphasis on quantum machine learning (2023), serverless architectures (2023), and 3D printing (2023). Her publications cover medical imaging (8+ papers), pressure ulcer analysis (4+ papers), and serious games (7+ papers). Scientific Awards : Premio de Investigación UD-Grupo Santander 2007 Advising includes Oscar Javier Bastidas Jossa (2024), Zabit Hameed (2023), and Ornela Bardhi (2022). Key grants involve EU projects and EIP on AHA collaborations.

• Medical Imaging
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Jose Luis Correa Pombo is a Professor at the Department of Electronics and Computing, affiliated with the Higher Polytechnic School of Engineering at the University of Santiago de Compostela. His research focuses on medical imaging and artificial intelligence, particularly in spatial filtering techniques for pulmonary nodule detection. He completed his PhD in 1994 at the same university under the supervision of Dr. Lorenzo Gómez González and Dr. Juan José Vidal Carrera. He has been part of research groups including the GSI Group (Intelligent Systems Group) and the LIIR (Radiological Imaging Research Laboratory). His work bridges signal processing and biomedical applications, emphasizing adaptive filtering methodologies. No academic awards, specific grants, or recent publications are explicitly mentioned in the provided text.