Find Academic Supervisors

• Biomedical Signal Processing
• Respiratory Monitoring
• Sleep Disorders
• +4 more

Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.

• Biomechanics
• Human Balance and Movement
• System Identification
• +7 more

Massimo Cenciarini is a Senior Lecturer in the Department of Mechanical Engineering at the Universitat Politècnica de Catalunya (UPC), affiliated with the School of Industrial Engineering (ETSEIB). He is an active member of the BIOMEC - Biomechanical Engineering Lab, TecSalut - Health Technologies Research Group, and CDEI - Industrial Equipment Design Centre. His work bridges engineering and biomedical applications, focusing on human movement and assistive technologies. Research Interests: Biomechanics and human balance Wearable robotics and exoskeletons System identification and control Assistive mobility technologies Mechatronics and simulation Postural control and motor adaptation His recent publications show a strong trend in wearable robotic devices, particularly knee exoskeletons, with emphasis on human-exoskeleton interaction, misalignment compensation, impedance identification, and gait assistance. His work integrates biomechanical modeling, experimental validation, and translational research for rehabilitation and mobility enhancement. Scientific Awards: 3r Premi UPC al Compromís Social He has participated in competitive R&D projects such as Xartec Salut and technology transfer initiatives, including the development of wearable knee exoskeletons. While no formal students are listed, his collaborative projects suggest mentorship roles within multidisciplinary teams. He is involved in research networks focused on health technologies and Industry 4.0 educational environments. Laboratories and Research Groups: BIOMEC - Biomechanical Engineering Lab (UPC) TecSalut - Health Technologies Research Group CDEI - Centre de Disseny d'Equips Industrials

• Neuroscience of Mindfulness
• Mental Health and Wellbeing
• EEG Microstate Sequencing
• +7 more

Dr. Elena Antonova is a Senior Lecturer in Psychology at Brunel University London's Department of Life Sciences within the College of Health, Medicine and Life Sciences. She holds a PhD in Cognitive Psychology from King's College London and has held postdoctoral and academic positions at the Institute of Psychiatry, Psychology and Neuroscience (IoPPN) and the University of Hertfordshire. Her research bridges contemplative neuroscience and AI, focusing on mindfulness-based interventions for mental health, neural dynamics in meditation, and applications to schizophrenia and healthy aging. Education: BSc (UCL, 2000), PhD (IoPPN, 2004) Affiliations: Visiting Researcher at IoPPN, Senior Visiting Research Fellow at University of Hertfordshire's Adaptive Systems Research Group Her work integrates neuroimaging (fMRI, EEG), psychophysiology, and computational methods to study mindfulness expertise and its clinical applications. Recent projects include Mindfulness-based Environmental Sustainability (MiBEST) and Using mindfulness to reduce schizophrenia vulnerability , funded by the British Academy and the United States Air Force Office of Scientific Research. Key publications analyze neural substrates of mindfulness, Long-COVID cognitive effects, and AI risk mitigation via neurophenomenology. Her 2024 articles explore brain structure in COVID-19 survivors, sensory processing in meditators, and paranoia reduction in schizotypy populations. Scientific Recognition: Templeton Positive Neuroscience Fellowship (2011-2013) Elected Honorary Research Fellow, Mind and Life Institute (2017) Chair, European Varela Awards Review Panel Public engagement includes BBC documentaries and features on mindfulness neuroscience. She collaborates with Prof. Veena Kumari, Prof. Chrystopher Nehaniv, and institutions like King's College London and University of Hertfordshire.

• Brain-Machine Interfaces
• Neuro-robotics
• Rehabilitation Robotics
• +3 more

Jose M. Azorín is a Full Professor at the Department of Systems Engineering and Automation at Miguel Hernández University of Elche (Spain), serving as Director of the Brain-Machine Interface Systems Lab and Assistant Vice-Rector for Research Management. He holds a M.Sc. in Computer Science (University of Alicante, 1997) and a Ph.D. in Robotics (Miguel Hernández University, 2003, award for best thesis). His research focuses on Brain-Machine Interfaces, Neuro-robotics, and Rehabilitation Robotics, funded by European Union grants and international agencies. He leads over 20 projects, including the NSF IUCRC BRAIN UMH Site and the WALK project. He is a Distinguished Lecturer of the IEEE Systems Council and core member of the IEEE Brain Initiative. Research interests include decoding motor/sensory brain activity for exoskeleton control, neurorehabilitation, and low-cost robotic solutions for neurologically impaired patients. His work spans exoskeletons, EEG signal processing, and telerobotics. He has co-authored over 200 publications and three patents. He has held visiting roles at the University of Houston (USA) and Imperial College London (UK). Administrative roles include Head of the Department of Systems Engineering and Automation (2011–2015). Awards include the 2003 Best Thesis Award and his leadership in IEEE committees (e.g., IEEE Brain Initiative, IEEE Systems Council). He organizes international conferences and workshops on neurorehabilitation and wearable robotics, and chairs events like the 2017 Mobile Brain-Body Imaging Conference.

Pooran MemariView profile

Researcher

• Geometric Modeling
• Computer Graphics
• Computational Geometry
• +5 more

Pooran Memari is a CNRS Researcher at the Laboratoire d'Informatique de l'École Polytechnique (LIX), UMR CNRS 7161, Institut Polytechnique de Paris, and an Affiliate Professor (part-time) at École Polytechnique. She leads research in geometric modeling within the GeomeriX team at LIX-Inria, focusing on theoretical foundations and applications in accessibility and neurocognition. Her academic journey includes: HDR (Habilitation à diriger des recherches), Institut Polytechnique de Paris, 2024 Ph.D. in Geometric Modeling, INRIA Sophia-Antipolis, 2010 Master in Image and Geometry, University of Nice-Sophia Antipolis, 2006 Engineering Degree, École Polytechnique, 2005 Bachelor in Mathematics, Sharif University of Technology, 2002 Dr. Memari's research bridges geometric modeling, computational geometry, and computer graphics with real-world impact. She pioneers techniques in shape representation, point pattern synthesis, and surface reconstruction, advancing proximity encoding and clustering algorithms. Her work extends to accessibility applications—developing geometric models for visually impaired navigation through multisensory perception—and neurocognition validation via tactile interfaces. This interdisciplinary approach integrates theoretical rigor with practical tools like the CGAL library. Recent publications (2024-2019) reveal a cohesive trajectory in geometric processing: advancing point pattern synthesis through image-based editing (Patternshop), stability-incorporated neighborhood graphs (SING), and multi-class disk distributions; innovating surface reconstruction via Voronoi-based methods (BallMerge); and expanding applications to virtual worlds simulation and neurocognitive accessibility. Key themes include bridging discrete geometry operators with high-dimensional data analysis and translating theoretical insights into tools for visual computing. Dr. Memari actively mentors the next generation of researchers, advising eight PhD students on topics ranging from generalized Voronoi diagrams to neurocognition-driven tactile navigation. She coordinates Computer Science Projects for École Polytechnique's Bachelor program since 2019 and co-leads the Interaction, Graphics & Design master's program at IP-Paris. Her leadership extends to editorial roles at Computer Graphics Forum and Graphical Models Journal, alongside prominent conference positions including SGP Program Co-Chair (2023) and Eurographics STARs Co-Chair (2025). As a core GeomeriX team member, she drives collaborative projects in geometric modeling and virtual environments. She coordinates the LIX Seminar since May 2025 and serves on the French Eurographics Chapter board, fostering community engagement through initiatives like the IGD master's program and Eurographics symposia.

Auxiliadora Sarmiento VegaView profile

Professor

• Audio Source Separation
• Biomedical Signal Processing
• Machine Learning
• +5 more

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.

• Reinforcement Learning
• Internet of Things (IoT)
• Cognitive Robotics
• +4 more

Iñaki Vazquez Gomez is a Lecturer and Researcher at the University of Deusto , affiliated with the College of Engineering and Department of Computing, Electronics and Communication Technologies . He holds a PhD in Computer Science and Artificial Intelligence (University of Deusto and Lancaster University) and served as Director of Deusto Institute of Technology (2020-2023). His research focuses on Reinforcement Learning , Internet of Things , and Cognitive Robotics , with applications in human-robot interaction and ambient intelligence. Member of European Internet of Things Council Founder of technology startups Editorial board member of International Journal of Ambient Computing and Intelligence His 15 most recent publications (2005-2024) span themes of Autonomous Systems , Context-Aware Computing , and Smart Environments , with subtopics including semantic device communication, adaptive positioning algorithms, and collaborative robotics architectures. 2024: Adaptive Robot Behavior via Reinforcement Learning 2021: Gesture Recognition for Resource-Constrained Devices 2015: Context-Aware Service Matching in AAL Scientific honors include: X Premio UD-Banco Santander de Investigación (2015) Sexenio de Investigación (2018-2025) Profesor Doctor de Universidad Privada (2009-2025) He supervised PhD students like Asier Gonzalez Santocildes and Ignacio Fidalgo Astorquia, while leading grants such as AI-Driven Cognitive Robotics (2021-2024) and Quantum Technologies (2021-2022). His work includes developing middleware for smart spaces and founding the Sentient Things project for self-adapting environmental systems.

• Neuroscience
• Biomedical Engineering
• Signal Processing
• +4 more

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.

• Signal Processing
• Wireless Communication Systems
• Power Amplifier Modeling
• +4 more

María José Madero Ayora is a Professor at the Department of Signal Theory and Communications , Universidad de Sevilla , specializing in nonlinear system modeling and digital predistortion for wireless communication systems. Her research focuses on Volterra series applications in power amplifier linearization, microwave measurements , and machine learning techniques for signal processing. Principal Investigator for projects like Statistical Signal Modeling for Brain-Computer Interfaces (PID2021-123090NB-I00) Recipient of the Arftg Roger Pollard Student Fellowship in microwave measurement Her work spans 5G waveform linearization , I/Q modulator impairments , and thermal memory effects in RF amplifiers. Recent publications combine sparse Bayesian methods with Volterra models to address nonlinear distortion in OFDM and visible light communication systems. She has supervised doctoral theses and participated in international conferences across the U.S., Europe, and Asia.

• Brain-Machine Interfaces
• Electroencephalography
• Neuroprosthetics
• +3 more

Dr. José del R. Millán is a Professor holding the Carol Cockrell Curran Chair in Electrical and Computer Engineering at The University of Texas at Austin, with joint appointments in Neurology (Dell Medical School) and Biomedical Engineering. He serves as co-director of the UT CARE Initiative and associate director of Texas Robotics, and is affiliated with the Mulva Clinic for the Neurosciences and Institute for Neuroscience. His pioneering research focuses on brain-machine interfaces (BMI) , particularly EEG-based systems for controlling robots and neuroprosthetics. He prioritizes translating BMI technology to assist individuals with motor and cognitive disabilities, bridging neural engineering, robotics, and clinical neuroscience. Notable recognitions include: IEEE-SMC Norbert Wiener Award (2011) IEEE Fellow (2017) Fellow of International Academy of Medical and Biological Engineering (2020)

• Computer Science
• Artificial Intelligence

Luis Lago is an Associate Professor at the Polytechnic School of the Autonomous University of Madrid . His office is located in EPS, B-307, and he can be contacted via email at luis.lago@uam.es . His research primarily spans Computer Science and Artificial Intelligence , with a focus on neural networks, natural language processing, and computational neuroscience. His work integrates theoretical and applied machine learning, addressing challenges in sequence modeling, formal automata, and anomaly detection. Recent publications highlight innovations in recurrent neural networks for brain-computer interfaces (e.g., P300-ERP detection) and text analysis (e.g., authorship attribution, fake news detection). No awards, grants, or student advising details are documented in the available information.

• Robotics
• Control Systems
• Automation
• +10 more

ABDERRAHIM FICHOUCHE, MOHAMED is an Associate Professor in the Department of Systems Engineering and Automation at the Carlos III University of Madrid (UC3M), where he also serves as Deputy Director of the Doctorate. He is affiliated with the Robotics Lab research group and the Pedro Juan de Lastanosa Institute of Technology Development and Innovation. His academic and research profile spans robotics, control systems, biomedical engineering, and power systems. His research interests include robotics , automation , control systems , biomedical signal processing , computer vision , and renewable energy integration . His work focuses on intelligent robotic systems, rehabilitation robotics, EEG-based brain-computer interfaces, fault detection in power systems, and advanced control strategies for industrial and autonomous systems. The recent publications reflect a strong trend in interdisciplinary research, combining machine learning with signal processing for applications in medical diagnostics , robotic control , and smart grid monitoring . His work increasingly integrates deep learning , wavelet analysis , and adaptive control across domains. He has led major research projects such as: HANDLE (EU and national funding): Focused on dexterous in-hand manipulation. SARAH: Enhancing robot autonomy with artificial hands. PAPREC: Automatic grasping of disordered parts. PROSAVE: Eco-efficient aircraft systems. GRAND-PA: Assistive technologies for the elderly. He currently participates in ongoing projects including: SEGVAUTO5G-CM (2025–2028): Future mobility innovation. RoboCity2030-DIH-CM (2019–2023): Madrid Robotics Digital Innovation Hub. HYPER: Neuroprosthetic and neurorobotic devices for rehabilitation. He has supervised several theses on topics such as transmission line fault detection , autonomous decision-making in robots , and 3D perception . His research is conducted primarily within the Robotics Lab at UC3M, a multidisciplinary team focusing on advanced robotic systems for industrial, medical, and service applications.

• Biomedical Signal Processing
• Embedded Systems
• FPGA Hardware Implementation
• +7 more

Jose Vicente Frances Villora is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is an active researcher in digital systems and biomedical signal processing, with a strong focus on real-time embedded systems and hardware acceleration. His research interests include: Biomedical Signal Processing, particularly ECG and microelectrode recordings FPGA-based hardware implementation of neural networks and real-time algorithms Machine learning applications in healthcare, including arrhythmia detection and Parkinson’s disease treatment Neuromorphic computing and spiking neural networks Digital signal processing in embedded and real-time systems The analysis of his recent publications (2017–2015) reveals a consistent focus on leveraging machine learning—especially KNN and neural networks—for clinical applications such as ventricular fibrillation detection and deep brain stimulation. A significant portion of his work emphasizes hardware implementation on FPGAs, aiming to achieve real-time performance and low resource consumption in medical and robotic systems. He has contributed to educational materials, including textbooks and OCW resources, and supervises academic theses. While no specific scientific awards are mentioned in the provided text, his sustained publication output and integration of theory with hardware systems underscore his technical expertise. He advises students in thesis work and has supervised doctoral research, though specific student names are not listed. His research is supported by practical applications in medical diagnostics and robotics, and he is affiliated with the GPDD (Group for Digital Design and Processing), which focuses on digital system design and processing. He is actively involved in laboratory development, including a remote laboratory for industrial robots, and his team works on co-design environments for neural network hardware, FPGA acceleration, and real-time biomedical systems.

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +5 more

Elias Marqués Valderrama is a Professor at the University of Seville in the Department of Signal Theory and Communications . His research is centered around radiocommunication systems and signal processing. Research focus: Signal Processing , Machine Learning , and Microwave Engineering Projects: Integration of statistical signal modeling and data-driven learning for brain-machine interfaces , and AI for emotion recognition . Fellowship: Arftg Roger Pollard Student Fellowship in Microwave Measurement . His publications highlight work on power amplifier modeling , digital predistortion , and EEG-signal databases using virtual reality . Collaborations include researchers like Juan Antonio Becerra González and Miguel Nogales González-Regueral . Key technical areas span nonlinear behavioral modeling , orthogonal matching pursuit , and virtual reality-based emotion analysis .

Jose L Contreras-VidalView profile

Professor

• Brain-machine interfaces
• Neurotechnology
• Biomedical Engineering
• +4 more

Dr. Jose 'Pepe' L Contreras-Vidal serves as the Hugh Roy and Lillie Cranz Cullen Distinguished Professor and director of the U.S. National Science Foundation Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) at the University of Houston. An elected Fellow of both IEEE and AIMBE, his pioneering work bridges neuroscience, engineering, and art. His research focuses on developing brain-machine interfaces for controlling wearable exoskeletons for rehabilitation and mapping art-evoked brain activity. Dr. Contreras-Vidal's interdisciplinary approach explores the neural basis of human creativity while advancing understanding of arts as active neuromodulators of brain function, with applications to personalized arts prescriptions. His recent work includes leading the first comprehensive knowledge integration assessment of clinical trials of implantable brain-computer interfaces, published in Nature Reviews Bioengineering. His art-science team received the Chamber Music America's 2025 Interdisciplinary Collaboration of the Year award for 'Meeting of Minds', which was performed at the United Nations' 2024 AI for Good Summit in Geneva. Fellow of IEEE Fellow of AIMBE Chamber Music America's 2025 Interdisciplinary Collaboration of the Year Dr. Contreras-Vidal serves on the National Advisory Board for Medical Rehabilitation Research at the U.S. National Institute of Health. His career development in biomedical engineering was highlighted by the journal Science, underscoring his significant contributions to the field.