Find Academic Supervisors

• Neuroengineering
• Neural Signal Processing
• Biomedical Signal Analysis
• +3 more

Balbir Singh is a Research Assistant Professor of Biomedical Engineering at Vanderbilt University, affiliated with the School of Engineering. His research focuses on neural signal processing, brain-computer interfaces, and cognitive neuroscience, with an emphasis on understanding neural mechanisms underlying memory, decision-making, and motor control. His work integrates electrophysiological techniques, advanced signal processing algorithms, and computational models to decode brain activity patterns. Key research areas include analyzing local field potentials (LFPs), studying oscillatory brain activity during cognitive tasks, and developing methods to enhance signal quality in biomedical recordings. He has contributed to advancements in EEG/EOG artifact removal, BCI system design, and neuroplasticity studies. His recent work explores how prefrontal cortex activity relates to working memory performance and decision-making processes in both human and primate models. Dr. Singh’s publications highlight interdisciplinary approaches, combining experimental neuroscience with engineering solutions to address challenges in neural decoding and clinical applications. His studies often involve collaborations to translate findings into practical tools for monitoring cognitive states and improving neuroprosthetic systems.

Jelmer BorstView profile

Associate Professor

• Computational Cognitive Neuroscience
• Neuroimaging Analysis
• Cognitive Modeling
• +5 more

Prof. Jelmer Borst is an Associate Professor in Computational Cognitive Neuroscience at the University of Groningen's Faculty of Science and Engineering, affiliated with the Artificial Intelligence department within the Bernoulli Institute. His research focuses on integrating computational models with neuroimaging data to understand cognitive processes like multitasking, working memory, and decision-making. He advises three PhD candidates and collaborates internationally on projects involving EEG/fMRI analysis and cognitive modeling frameworks such as ACT-R and Nengo. Research Interests: Neuroimaging analysis methods, cognitive bottlenecks in multitasking, memory retrieval localization, and adaptive learning systems. Key Projects: Developed the PREDICTOR tool for semi-automated driving response timing, and advanced models linking symbolic process stages to brain activity via MEG/EEG. Recent work includes large-scale evaluations of adaptive learning systems and interventions to mitigate mind-wandering in driving scenarios. He has received the Allen Newell Best Student-led Paper Award (2021) for contributions to cold-start adaptive learning research. His research group maintains active collaborations on datasets involving working memory, decision-making, and cognitive architecture validation through neuroimaging experiments. He also contributes to open-source tools for cognitive modeling and neuroimaging analysis.

Abraham Vazquez-GuardadoView profile

Assistant Professor

• Optoelectronic Device Physics
• Nano-Photonics
• Biomedical Engineering
• +6 more

Dr. Abraham Vázquez-Guardado is an Assistant Professor in the Department of Electrical and Computer Engineering at NC State University, joining in January 2023. He holds a Ph.D. in Optics and Photonics from the University of Central Florida (2018), a Master's in Optics from the National Institute for Astrophysics, Optics and Electronics (2012), and a B.Eng. in Electrical Engineering (Automation) from Universidad Autónoma de Nayarit (2007). His postdoctoral work at Northwestern University's Querrey Simpson Institute for Bioelectronics focused on wireless implantable devices for neuroscience and biomedical applications. Ph.D. in Optics and Photonics, University of Central Florida (2018) M.S. in Optics, National Institute for Astrophysics, Optics and Electronics (2012) B.Eng. in Electrical Engineering, Universidad Autónoma de Nayarit (2007) His research centers on optoelectronic and nanophotonic devices for bidirectional bio-interfaces, enabling applications in real-time biosensing , neuroscience research , clinical diagnostics , and feedback-controlled human interfaces . The Vázquez Research Group specializes in: Wireless implantable biomedical devices Bioresorbable electronics Machine learning for unsupervised medical systems Micro/nano fabrication of photonic devices Optogenetic control systems His recent publications highlight adaptive energy harvesting for wearables, autonomous opioid overdose prevention devices, and wireless thermo-haptic VR interfaces , demonstrating expertise in merging photonics with biomedical innovation. The group's work emphasizes translating nano-engineered systems into clinical solutions while fostering inclusive academic excellence.

James Hubbard Jr.View profile

Professor

• Smart Structures
• Morphing Aircraft
• Autonomous Vehicles
• +3 more

James Hubbard Jr. is Professor and Oscar S. Wyatt, Jr. '45 Chair I Professor at Texas A&M University's College of Engineering, Department of Mechanical Engineering. He holds a Ph.D., M.S., and B.S. from MIT and serves as Director of the RELLIS Starlab Facility. Research focuses on adaptive structures, morphing aircraft, drone control systems, and human-machine interfaces. Key areas include real-time shape control of aerospace systems and quantum decision-making for human-robot teaming. Awards include the ASME Adaptive Structures Award (2022), National Academy of Inventors Fellowship (2021), and SPIE Lifetime Achievement Award (2016).

Maia FraserView profile

Associate Professor

• Machine Learning
• Computer Vision
• Neuroscience
• +4 more

Maia Fraser is an Associate Professor in the Department of Mathematics and Statistics at the University of Ottawa. She holds an MSc and PhD from the University of Chicago and a PhD from Stanford University. Her research spans interdisciplinary areas including machine learning, computational neuroscience, and applied mathematics. Notably, she explores the intersection of AI with structural engineering, focusing on automated damage assessment in concrete and neural mechanisms underlying continual learning. Her work also delves into theoretical aspects of contact geometry and reinforcement learning. Her academic contributions include pioneering methods for damage quantification using digital images, theoretical frameworks for temporal abstractions in neural systems, and algorithmic advancements in decision-making under uncertainty. Fraser’s research often bridges abstract mathematical theory with practical applications, such as optimizing nautical navigation and analyzing fish behavior through spatial learning models. Education: MSc in Mathematics, University of Chicago PhD in Mathematics, University of Chicago PhD in Computer Science, Stanford University Fraser’s recent publications highlight advancements in automated assessment techniques for civil infrastructure, the role of temporal hierarchies in neural organization, and foundational work in reinforcement learning theory. Her interdisciplinary approach reflects a commitment to solving complex problems through collaborative, mathematically rigorous methods.

Robert RoweView profile

Professor

• Music Technology
• Interactive Music Systems
• Algorithmic Composition
• +3 more

Robert Rowe is a Professor of Music Technology at New York University's Steinhardt School of Culture, Education, and Human Development, and an Affiliated Faculty member at NYU Abu Dhabi. He is a core member of the Music & Audio Research Laboratory (MARL), specializing in interactive music systems, algorithmic composition, and music cognition. His work bridges music technology, computer science, and artistic performance. Bachelor of Music in Music History & Theory (University of Wisconsin, 1976) Master of Arts in Composition (University of Iowa, 1978) Ph.D. in Music and Cognition (MIT Media Lab, 1991) Rowe's research focuses on interactive systems, real-time music processing, and the intersection of music with cognitive science and artificial intelligence. His compositions have been performed globally and are featured on multiple record labels. Key contributions include the books Interactive Music Systems (1993) and Machine Musicianship (2001), which explore computational approaches to music creation and performance. His recent work emphasizes distributed performance systems, emotion recognition in music, and the application of machine learning to interactive composition. Notable projects include Melting the Darkness (2014) and Arcturus (2006), which showcase real-time interaction between performers and computational systems. Awards : First Prize in the Bourges International Electroacoustic Music Competition (1990) Grants : Multiple grants from NYU and external institutions for research in music technology and cognition. Rowe has led initiatives in NYU’s Steinhardt School, including serving as Associate Dean of Research & Doctoral Affairs, and collaborates internationally with institutions like IRCAM (Paris) and the Sweelinck Conservatory (Amsterdam). His lab, MARL, develops cutting-edge tools for music analysis, generation, and performance.

Florentin WörgötterView profile

Professor

• Computational Neuroscience
• Artificial Neural Networks
• Experimental Neuroscience
• +3 more

Prof. Dr. Florentin Wörgötter is a Professor of Physics at Georg-August University Göttingen, specializing in Computational Neuroscience and Biophysics. He holds affiliations with the Bernstein Center for Computational Neuroscience and the Computational Neuroscience Group (CNS) . His career includes roles as a visiting professor at institutions like the Academia Sinica (Beijing, China) and KTH (Stockholm, Sweden), and directorships such as leading the INCITE Institute. Research focuses on computational models of neural systems, synaptic plasticity, and visual system dynamics. Active in interdisciplinary fields including brain-machine interfaces and developmental neuroscience. His work bridges experimental and theoretical approaches, with recent publications addressing dynamical systems modeling, unsupervised learning, and infant motor behavior analysis. Notably, he has contributed to frameworks for smart environment control via neural interfaces. Awards section: No specific scientific awards listed in the provided text. Advising/Grants: The text does not explicitly list advised students or grants, though his extensive collaborations suggest active mentorship and funding. Labs/Teams: Leads the CNS Group at Göttingen, affiliated with the Physics of Biological and Complex Systems (IMPRS) and Theoretical and Computational Neuroscience research clusters.

• 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.

Kim DremstrupView profile

Professor

• Biomedical Engineering
• Brain-Computer Interfaces
• EEG Analysis
• +7 more

Kim Dremstrup is Head of Department at the Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Denmark. He is a leading researcher in biomedical engineering with a focus on brain-computer interfaces (BCI), EEG analysis, sleep research, and rehabilitation technology. He has played a pivotal role in establishing BCI and sleep research at AAU and leads the Aalborg BCI group, recognized internationally for early applications in stroke, spinal cord injury, and ALS rehabilitation. PhD in Computerized Sleep Analysis (1993) MSc in Biomedical Engineering (1984) His research centers on decoding brain signals for clinical applications, with major contributions including the invention of the Nightingale Automatic Sleep Analyzer and co-invention of the EDF bio-signal format used globally. His work enables assistive technologies for severely disabled individuals, particularly through hybrid control systems integrating brain and tongue signals. He is also involved in telerehabilitation and neonatal care technologies. Recent publications highlight advancements in error-related potential detection in stroke and motor disabilities, hybrid BCI-tongue control for ALS, and assistive robotics. His work spans neuroscience, biomedical engineering, robotics, and clinical medicine, with a strong translational focus. Inventor of the Nightingale Sleep Analyzer Co-inventor of EDF bio-signal format President, Danish MedTech Society (DMTS) Danish representative in IFMBE Chairman, Den Nordjyske Trivselsfond He actively supervises PhD students and participates in multiple research projects, including assistive robotics and multimodal control systems. He is also engaged in public discourse through media contributions on digital health and personalized medicine. He leads the Center for Rehabilitation Robotics and contributes to UN Sustainable Development Goals in health and well-being.

• Artificial Intelligence
• Brain-Computer Interfaces
• Neural Networks
• +2 more

Manuel Eder is a researcher affiliated with the Faculty of Computer Science's Research Group Neuroinformatics. He holds a Diplom-Ingenieur (Dipl.-Ing.) engineering degree and a Bachelor of Science (BSc), indicating strong technical foundations in computational neuroscience and engineering disciplines. His research centers on the critical intersection of artificial intelligence and neural systems, with primary expertise in Brain-Computer Interfaces (BCIs), neural network architectures, and neuroprosthetic applications. He investigates how AI can decode neural signals to create assistive technologies, particularly focusing on cognitive psychology aspects of human-machine interaction and addressing communication impairments through innovative BCI solutions. Analysis of his 2024 publications reveals a cohesive research trajectory advancing practical BCI implementations. His work demonstrates methodological rigor in benchmarking algorithmic approaches while maintaining strong clinical relevance, particularly in developing EMG-integrated systems for patient populations and conversational interfaces that bridge neural activity with natural language processing. This dual focus on theoretical algorithm development and real-world medical applications represents a significant contribution to the neurotechnology field. Eder operates within the Neuroinformatics research group, which specializes in computational modeling of neural systems and developing AI-driven tools for neuroscience research. While specific laboratory infrastructure details aren't provided, his publication patterns indicate active collaboration with both computational researchers and clinical specialists in neuroscience and rehabilitation medicine.

• Cognitive Neuroscience
• Decision Making
• Attention & Cognitive Control
• +4 more

Professor Nick Yeung is a Professor of Cognitive Neuroscience at the University of Oxford and Tutorial Fellow at University College, Oxford. His primary affiliation is with the Department of Experimental Psychology. His research focuses on the cognitive and neural mechanisms underlying human learning, memory, attention, and decision-making. He explores how subjective confidence in choices influences information-seeking behavior and error detection, with applications in mental health (e.g., depression) and human-machine collaboration. Yeung holds a BA (Hons) and PhD in Psychology. His work integrates computational modeling, neuroimaging (EEG/fMRI), and experimental psychology to study metacognition, task switching, and attentional control. Recent studies investigate domain-general error signals, advice utilization dynamics, and neural correlates of confidence. Research highlights include EEG-based attention monitoring for safety-critical tasks (e.g., driving) and exploring how confidence biases affect decision-making in healthcare and collaborative systems. His lab also develops practical applications of cognitive neuroscience findings, such as attention-stabilizing neurostimulation devices. Key contributions span over 80 publications, including high-impact work on decision confidence, error monitoring, and cognitive control. His 2024 perspective paper outlines foundational advancements in cognitive control research, while 2025 studies reveal shared neural error signals across sensory modalities.

• 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.

Mohamad EidView profile

Associate Professor

• Haptics
• Multi-modal Human-Computer Interaction
• Affective Haptics
• +7 more

Mohamad Eid is an Associate Professor of Electrical and Computer Engineering at New York University Abu Dhabi (NYUAD), with a Global Network Associate Professor appointment at NYU Tandon School of Engineering. He leads the Applied Interactive Multimedia Laboratory (AIMLab), focusing on cutting-edge research in haptics and multimodal interaction. His research interests include haptics, affective haptics, EEG-based neurohaptics, soft haptics, biofeedback technologies, and human-computer interaction. He explores how tactile feedback can enhance virtual and augmented reality, assistive technologies, and medical simulation. His recent publications demonstrate strong activity in IEEE Transactions on Haptics, ACM TOMM, and other top venues, with work spanning vibrotactile alarms, mid-air haptics, emotion communication via touch, and haptic dental simulation. His research integrates engineering, neuroscience, and multimedia to create immersive and affective user experiences. Best Paper Award, ICBAE 2016 Best Paper Award, DS-RT 2008 ACM Multimedia 2009 Grand Challenge Most Entertaining Award Mohamad Eid advises students through capstone projects in computer science and biology and has contributed to educational initiatives such as robotic handwriting assistance for Arabic script. He holds multiple patents in haptic feedback systems and is an active editor for IEEE Transactions on Haptics. His work is supported by grants and has been featured in media outlets like Wired and The National. He leads the Applied Interactive Multimedia Laboratory (AIMLab), a multidisciplinary research group developing innovative haptic and interactive systems for healthcare, education, and entertainment.

• Artificial Intelligence
• Machine Learning
• Brain-Computer Interfaces
• +5 more

Naomi Du Bois is a Research Associate in Artificial Intelligence and Machine Learning at the Bath Institute for the Augmented Human, University of Bath, affiliated with the Department of Computer Science under the Faculty of Science. Her work bridges AI, neuroscience, and clinical applications to support human augmentation and neurorehabilitation. Research Interests: Her primary research focuses on developing objective neurophysiological markers using AI and machine learning to assess prolonged disorders of consciousness (PDoC). This includes analyzing brain signals such as auditory evoked potentials to differentiate wakefulness and awareness in patients with locked-in syndrome and related conditions. Her work lies at the intersection of biomedical signal processing, cognitive neuroscience, and intelligent systems. The available publication highlights a trend toward applying machine learning techniques to clinical neurophysiology, particularly for improving diagnostic accuracy in non-communicative patients. Her research contributes to the growing field of assistive and augmentative technologies for individuals with severe neurological impairments. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no information available regarding student supervision, grants, or funding roles in the provided content. Labs and Teams: Naomi Du Bois is a member of the Bath Institute for the Augmented Human, a multidisciplinary research center focused on enhancing human capabilities through technology, particularly in health and well-being contexts. The institute emphasizes wearable systems, sensory augmentation, and brain-computer interfaces.

• Medical Image Analysis
• Computer Vision
• Biomedical Imaging
• +6 more

Constantino Carlos Reyes-Aldasoro is a Professor in the Department of Electrical and Electronic Engineering within the School of Mathematics, Computer Science and Engineering at City, University of London. With an active research career spanning over two decades, he maintains a prolific publication record with recent contributions in 2025 across multiple disciplines including medical image analysis, computer vision, and biomedical engineering. His research interests focus on Medical Image Analysis, Computer Vision, Biomedical Imaging, Cancer Research, and Image Processing. His work bridges engineering and medical sciences, developing computational methods for biomedical applications. He has made significant contributions to tumor microenvironment modeling, vascular analysis, and electropalatography studies. Analysis of his recent publications reveals a strong emphasis on deep learning applications in medical imaging, with particular focus on cancer diagnostics, vascular analysis, and neuroimaging. His research demonstrates consistent methodological innovation in image processing techniques applied to biomedical problems, with publications spanning from fundamental algorithm development to clinical applications. Professor Reyes-Aldasoro has mentored numerous researchers, with several co-authors appearing consistently across multiple publications as likely students and postdoctoral researchers including C. Karabağ, J.A. Solis-Lemus, and M.A. Ortega-Ruiz. His work demonstrates interdisciplinary collaboration across engineering, computer science, medicine, and biology, reflecting the increasingly interconnected nature of modern biomedical research. He maintains active research programs with recent publications indicating ongoing projects in AI implementation in medical imaging, tumor modeling, and cardiovascular analysis.