Professor Dario Farina is Chair in Neurorehabilitation Engineering at the Department of Bioengineering, Faculty of Engineering, Imperial College London. He has previously served as Full Professor at Aalborg University, Denmark, and at the University Medical Center Göttingen, Germany, where he founded and directed the Institute of Neurorehabilitation Systems. His research spans biomedical signal processing, neural control of movement, and neurorehabilitation technology, with extensive contributions to electromyography, motor unit analysis, and neural interfaces. Chair in Neurorehabilitation Engineering, Imperial College London Former Full Professor, Aalborg University and University Medical Center Göttingen Founder and Director, Institute of Neurorehabilitation Systems Key Affiliations: Centre for Neurotechnology, Artificial Intelligence Network, Robotics Forum, Neuromechanics and Rehabilitation Technology His research focuses on biomedical signal processing , neural control of movement , and neurorehabilitation technology . He investigates how neural signals control muscles, develops methods to decode motor unit activity from EMG, and designs neural interfaces for prosthetics and rehabilitation. His work integrates computational modeling, signal processing, and clinical applications to improve bionic systems and neurorehabilitation outcomes. The recent publications (2024–2025) show a strong emphasis on high-density EMG , real-time motor unit decomposition , peripheral and cortical neural interfacing , closed-loop control systems , and AI-driven biosignal analysis . Key themes include decoding spinal and cortical signals, improving prosthetic control, understanding tremor mechanisms, and developing open-source tools for motor unit analysis. The work bridges neuroscience, engineering, and clinical practice. Scientific awards and honors include: Royal Society Wolfson Research Merit Award (2016) IEEE EMBS Early Career Achievement Award (2010) Nightingale Prize for best paper in MBEC (2007) Elected Fellow of EAMBES (2016) Elected Fellow of AIMBE (2012) Professor Farina has advised numerous researchers and students in neuroengineering and rehabilitation technology. He has led major research grants in neural interfaces and neurorehabilitation. He is Editor-in-Chief of the Journal of Electromyography and Kinesiology , an editor for IEEE Transactions on Biomedical Engineering and The Journal of Physiology , and has held editorial roles in multiple journals. He was President of ISEK (2012–2014) and is a Senior Member of IEEE. He leads a research group focused on neuromechanics, neural decoding, and bionic systems. The team develops tools like I-Spin live and MUedit for real-time motor unit identification and contributes to open-source platforms such as NeuroMotion . The lab collaborates internationally on projects involving spinal cord stimulation, prosthetic control, and wearable robotics, aiming to translate neural engineering advances into clinical rehabilitation.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۱۶-۳۰ از ۴۸۷ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Virginia de Sa is a Professor in the Department of Cognitive Science at the University of California, San Diego. Her research integrates computational modeling, psychophysics, and machine learning to investigate visual and multi-sensory perception, with a focus on understanding how humans learn and perceive through neural mechanisms. Her work emphasizes the synergy between human learning and machine learning, applying insights from both fields to advance understanding of perception. Notable projects include developing brain-computer interface (BCI) systems and analyzing biases in facial expression recognition algorithms. She leads the de Sa Lab, which explores the neural basis of learning through interdisciplinary methods, including EEG analysis and biologically inspired algorithms. Key research directions include improving BCI usability through adaptive spatial filtering, investigating pain assessment via facial and electrophysiological data fusion, and enhancing AI fairness in facial expression analysis. Dr. de Sa has contributed to grants such as the NSF-funded CHS project to enhance BCI reliability and collaborates on initiatives like AI-READI to improve healthcare data practices. Her lab’s BCI division focuses on interpreting EEG data for assistive technologies, while her work on divisive normalization bridges biological insights with artificial neural network design. Ongoing efforts explore zero-shot learning and the generalization of neural models to unseen tasks. Dr. de Sa’s interdisciplinary approach spans neuroscience, computer science, and engineering, with a commitment to advancing both theoretical understanding and practical applications in human-computer interaction.
Rikky Mullerمشاهده پروفایل
دانشیار
Rikky Muller is an Associate Professor of Electrical Engineering and Computer Sciences at UC Berkeley, holding the S. Shankar Sastry Professorship in Emerging Technologies. She is Co-director of the Berkeley Wireless Research Center (BWRC), a Core Member of the Center for Neural Engineering and Prostheses (CNEP), and an Investigator at the Chan-Zuckerberg Biohub. Her research focuses on implantable/wearable medical devices, low-power wireless systems, and neurotechnology for neurological applications. Education: PhD (2013), UC Berkeley; BS and M.Eng. (2004), MIT, all in EECS. Prior roles include IC designer at Analog Devices and co-founder of Cortera Neurotechnologies (acquired). Research interests include neural interfaces, closed-loop neuromodulation, and biomedical microelectronics. Notable contributions include Neural Dust (ultrasonic implants), wireless EEG systems, and seizure prediction hardware. Awards: MIT TR35 Innovator, NAE Gilbreth Lectureship, NSF CAREER Award, IEEE SSCS New Frontier Award Grants: Bakar Fellows, Hellman Fellowship, NSF CAREER Labs: Muller Lab (UC Berkeley EECS), Chan-Zuckerberg Biohub collaborations
Hasan Ayazمشاهده پروفایل
دانشیار
Hasan Ayaz, PhD, is an Associate Professor at Drexel University’s School of Biomedical Engineering, Science and Health Systems, and the Department of Psychology in the College of Arts and Sciences. He is a core member of the CONQUER Collaborative and has affiliations with the University of Pennsylvania and Children’s Hospital of Philadelphia. His research focuses on neuroengineering, neuroergonomics, and clinical applications of optical brain imaging, particularly using fNIRS and EEG. He has over 200 publications and has secured funding from federal agencies and industry partners. Dr. Ayaz serves on editorial boards for journals like PLOS One and Frontiers in Human Neuroscience and has organized international neuroergonomics conferences. Education: BSc (Electrical and Electronics Engineering, Boğaziçi University, Turkey), MSc and PhD (Drexel University). Research Interests: Neuroergonomics, functional neuroimaging, biomedical signal processing, neuroengineering, fNIRS, EEG, brain-computer interfaces, and mobile neuroimaging. His work aims to develop next-generation brain imaging technologies for applications ranging from aerospace to healthcare. Key Awards: Received a Wellcome LEAP Grant for Addiction Research in 2024. Grants & Advising: Extensive federal and corporate funding; no explicit student list provided. His research involves interdisciplinary collaborations and clinical partnerships. Labs/Teams: Leads the CONQUER Collaborative and contributes to the Cognitive Neuroengineering group at Drexel.
Antonio Krüger is a Professor at Saarland University and the CEO & Scientific Director of the German Research Center for Artificial Intelligence (DFKI). He leads the Ubiquitous Media Technology Lab and the Cognitive Assistance Systems department at DFKI, specializing in human-machine interaction, artificial intelligence, and intelligent user interfaces. University: Saarland University Department: Ubiquitous Media Technology Lab Organizational Role: CEO & Scientific Director, DFKI Research Interests Human-Machine Interaction User Modeling Cognitive Sciences Ubiquitous Computing Publications (2025) span disciplines including neuroscience, VR, and federated learning, focusing on topics like EEG-based BCIs, immersive plant trait analysis, and long-term mental modeling for well-being. Contact: ceo@dfki.de
Hatem Abou-Zeidمشاهده پروفایل
استادیار
Dr. Hatem Abou-Zeid is an Assistant Professor in the Department of Electrical and Software Engineering at the University of Calgary, directing the WAVES Research Group. He holds adjunct positions at Queen’s University, Carleton University, and Ontario Tech University. His research focuses on AI-driven 6G networks, wireless sensing, extended reality (XR), and brain-computer interfaces (BCI). Prior to academia, he spent 7 years in industry at Ericsson and Cisco, leading projects in 5G radio access and network intelligence, resulting in 20+ patents and $3.5M+ collaborative research projects. Education: PhD in Electrical and Computer Engineering (Queen’s University), M.Sc. and B.Sc. in Electronics and Communications Engineering (Arab Academy, Egypt). Research interests span trustworthy AI for 6G, joint sensing & communication systems, and AI for immersive networking. Notable work includes foundational models for 6G radios, self-supervised learning for spectrograms, and safe reinforcement learning for network slicing. His lab explores pediatric BCI, IoT edge computing, and low-power wireless prototyping. Publications highlight contributions to AI-driven resource allocation, 5G/6G slicing, and federated learning in tactical networks. Awards include the 2023 Early Research Excellence Award and 2023 Software Engineering Professor of the Year. Advising involves 15+ students and postdocs, with collaborations spanning academia (e.g., Hotchkiss Brain Institute) and industry (Ericsson, Telus). Current openings exist for PDF/PhD researchers in AI/6G wireless systems and pediatric BCI. Labs/Teams: Director of WAVES Lab; collaborates with Ericsson Canada, Alberta Innovates, and Canadian Space Agency on applied research projects. Teaching includes advanced networking, machine learning systems, and IoT courses.
Robert J.K. Jacob is a Professor of Computer Science at Tufts University, affiliated with the School of Engineering's Department of Computer Science. His research focuses on Human-Computer Interaction (HCI), particularly implicit brain-computer interfaces (BCI) using fNIRS and EEG technologies. He has held visiting positions at University College London, Université Paris-Sud, and MIT Media Lab. Education: Ph.D. in Computer Science from Johns Hopkins University. Research Interests : Jacob's work explores novel interaction techniques, adaptive interfaces, and BCI applications. Current projects emphasize real-time fNIRS-based systems for effortless user input, cognitive workload assessment, and neuroadaptive technologies. His lab investigates how brain signals can enhance user interfaces in domains like music learning, gaming, and urban design. Recent Trends in Articles : Recent publications highlight advancements in BCI design, neuroadaptive systems, and interdisciplinary applications of fNIRS. Work spans theoretical frameworks (e.g., NeuroCHI ethics) to practical tools like the Tufts fNIRS dataset. Key themes include improving BCI calibration, exploring AI's role in urban environments, and integrating affective computing into artistic interfaces. Awards : ACM Fellow (2016) ACM CHI Academy Membership (2007) Best Paper Award at CHI 2016 Advising & Grants : Supervised 15+ Ph.D. alumni in HCI and BCI. Served as Vice-President of ACM SIGCHI and co-chair of UIST/CHI conferences. Active in editorial roles for Human-Computer Interaction and ACM Transactions on Computer-Human Interaction . Labs & Teams : Directs the Tufts HCI Lab in the Joyce Cummings Center. Collaborates with interdisciplinary teams on projects like the Marble Track Audio Manipulator and Reality-Based Interaction Framework.
- Robotics
- Human-Robot Interaction
- Machine Learning
- +۷ مورد دیگر
Christian Smith is an Associate Professor and Lecturer at the Department of Robotics, Perception and Learning at Kungliga Tekniska Högskolan (KTH Royal Institute of Technology). His research focuses on robotics and applications in human-centered environments like home environments, small workshops, and healthcare facilities, including the development of new robotic systems for research. Teaching Roles: Course Coordinator/Teacher/Examiner for courses such as Introduction to Robotics (DD2410), Research Project in Robotics (DD2411), and Java Programming for Python Programmers (DD1380) Research Themes: Human-Robot Interaction, Behavior Trees, Exoskeletons, Intent Recognition, and Multimodal Perception Awards: No specific scientific awards mentioned in the provided text His KTH profile highlights work on adaptive robotics systems and formalized control strategies. The research portfolio spans from theoretical studies on behavior tree programming to applied work in assistive technologies and teleoperation systems.
Lee M. Miller is a Professor and Vice Chair of Academic Affairs in the Department of Neurobiology, Physiology and Behavior at the University of California, Davis, affiliated with the Center for Mind and Brain. His research focuses on neuroengineering, computational neuroscience, and neural mechanisms underlying attention, speech processing, and multisensory integration. Research interests include the development of neural prosthetics, decoding of neuromuscular signals for prosthetic control, and understanding how auditory and visual systems interact during speech perception and attentional processes. His work bridges clinical applications (e.g., cochlear implants) with fundamental neuroscience, leveraging tools like electrophysiological recordings, EEG/MEG, and advanced signal processing techniques. Recent publications highlight innovations in electromyographic speech neuroprosthetics, the topology of neuromuscular signals, and the neural basis of speech-in-noise processing. Miller’s studies emphasize translational potential, such as improving speech synthesis from brain signals and designing haptic feedback systems for motor coordination. His contributions have advanced understanding of neural mechanisms in sensory integration, auditory attention, and the impact of cognitive factors on perception. Miller maintains a lab dedicated to these interdisciplinary efforts, with a focus on both basic science and clinical applications.
Associate Professor Mathias Baumert is affiliated with the University of Adelaide, where he holds a position in the School of Electrical and Mechanical Engineering under the Faculty of Sciences, Engineering and Technology. He leads the Health Technology research theme in the School of Electrical Electronic Engineering and specializes in biomedical signal processing, focusing on dynamic electrocardiography and sleep-related phenomena. His work integrates clinical applications and technological advancements to address challenges in cardiology and sleep disorders. His research interests include the physiological underpinnings of ventricular repolarization variability and its clinical implications, particularly in post-myocardial infarction patients and those with sleep-disordered breathing. He also develops brain-computer interface (BCI) systems for stroke rehabilitation, leveraging real-time EEG analysis and motor function recovery techniques. Collaborations with clinical partners such as the Women’s and Children’s Hospital, Adelaide Institute of Sleep Health, and the Victor Chang Cardiac Research Institute highlight his translational research focus. His recent articles emphasize signal processing applications for risk stratification in cardiovascular disease, sleep apnea, and diabetes. Key themes include nocturnal hypoxemic burden prediction, REM sleep dynamics, and the development of novel diagnostic markers using ECG and EEG data. His work often bridges engineering and medicine, aiming to translate findings into clinical tools like adaptive servo-ventilation treatment optimization and personalized BCI systems. No scientific awards or fellowships are explicitly listed in the provided texts. He is eligible to supervise Masters and PhD students but current advisee names are not available. His research projects are supported by grants such as ARC DP110102049 (as noted in some articles). He teaches courses including Biomedical Instrumentation and Introduction to Medical Technology . His facilities include ECG equipment, polysomnogram repositories, and a BCI workstation with 64-channel EEG capabilities. He collaborates on lab-based and clinical partner studies to advance cardiac sensing algorithms and sleep-related diagnostic technologies.
Christopher John Rozell is the Julian T. Hightower Chaired Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology's College of Engineering. He serves as Executive Director of the Institute for Neuroscience, Neurotechnology & Society (INNS) and directs the Sensory Information Processing Lab (SIPLab). His research bridges computational neuroscience, machine learning, and neurotechnology, with clinical applications in treatment-resistant depression and neuromodulation therapies. Education: B.S.E. in Computer Engineering & B.F.A. in Music, University of Michigan (2000) M.S. and Ph.D. in Electrical Engineering, Rice University (2002, 2007) Postdoctoral Scholar, Redwood Center for Theoretical Neuroscience, UC Berkeley Research Focus: Dr. Rozell's interdisciplinary work spans computational neuroengineering, theoretical neuroscience, and artificial intelligence. He develops data analysis tools inspired by neural processing and creates therapeutic neurotechnologies. Key areas include: computational psychiatry (developing DBS therapies for depression), neural dynamics modeling, brain-computer interfaces, and societal impacts of neurotechnology. His lab focuses on high-dimensional data analysis, neural coding principles, and scalable neuromodulation approaches. Publication Trends (2023-2025): Recent works concentrate on deep brain stimulation mechanisms for depression, computational modeling of neural/autonomic dynamics, and machine learning applications in neuroscience. Dominant themes include biomarker discovery for treatment response, neural interoception modulation, probabilistic modeling of latent states, and brain-computer interface taxonomy. Clinical translation of neurotechnology is a consistent focus across publications. Awards & Honors: Elected AIMBE Fellow (2025) NIH BRAIN Initiative Photo/Video Award (2024) Congressional Panelist for BRAIN Initiative 10th Anniversary (2024) Sigma Xi Best Faculty Paper (2024) Neuro Open Science International Prize (2022) W. Howard Ector Outstanding Teacher Award (2019) McDonnell Foundation 21st Century Science Award (2014) NSF CAREER Award (2014) Leadership & Training: Dr. Rozell co-founded Neuromatch, Inc. to build global computational neuroscience communities. He advises Motif Neurotech and the Institute of Neuroethics. His mentees have received prestigious fellowships (Schmidt, Fulbright, NIH K99/R00) and hold leadership positions across academia and industry. Research is supported by NIH BRAIN Initiative, NSF, and private foundations. Labs & Initiatives: Directs the Sensory Information Processing Lab (theoretical neuroscience/neuroengineering) and the Institute for Neuroscience, Neurotechnology & Society (addressing ethical/societal implications). Neuromatch promotes open, accessible computational neuroscience training globally.
William F. Speier is an Associate Professor in the Department of Radiological Sciences at the University of California, Los Angeles (UCLA) School of Medicine . His work spans Medical Informatics , Biomedical Engineering , and Neurology , focusing on applying Artificial Intelligence and Deep Learning to medical imaging and patient monitoring systems. Speier's research emphasizes improving diagnostics for Thyroid Cancer via multimodal ultrasound and molecular testing, advancing Brain-Computer Interfaces (BCIs) for ALS patients, and optimizing Heart Failure remote monitoring through biometric data analysis. He leads the NIH-funded project Predicting Clinically Significant Thyroid Cancer using Ultrasound (R21EB030691), integrating AI into clinical workflows. His recent publications highlight trends in High-Frequency Oscillations for epilepsy, Gleason Grading in prostate cancer, and Language Models for BCI communication. Collaborations with co-authors like Corey Arnold and Hiroki Nariai underscore his interdisciplinary approach. Speier's work also addresses Diagnostic Imaging , Neural Signal Processing , and Health Technology accessibility. Grants and clinical trial integrations further demonstrate his commitment to translating AI into practical healthcare solutions. His methodologies include 3D ConvNets , Federated Learning , and Active Learning frameworks for histopathology and radiology.
Prof. Dr. Matthias Krauledat is a faculty member at Hochschule Rhein-Waal , specifically within the Faculty of Technology and Bionics . His academic career spans both theoretical research and industrial application, with a focus on Machine Learning and Brain-Computer Interfaces . After completing his PhD in Electrical Engineering/Computer Science at Technische Universität Berlin , he has contributed significantly to the advancement of EEG-based communication systems and neural signal processing methodologies. Born in Essen, Germany Studied Mathematics with a minor in Computer Science at University of Münster/Oxford Doctoral research at TU Berlin on Brain-Computer Interfaces Industrial experience at Henkel AG & DMT GmbH Research Interests focus on Machine Learning applications in Neuroscience and Biomedical Engineering , specifically Brain-Computer Interfaces , EEG Signal Processing , and Adaptive Classification Systems . His work explores how algorithms can be developed to enable self-learning computers to solve complex tasks involving neural data interpretation and prediction for previously unseen data in clinical and technological contexts. Publications demonstrate a consistent contribution to Neuroscience and Machine Learning fields, with particular emphasis on Brain-Computer Interface systems from 2004 through 2009. His research has focused on reducing training requirements, improving signal processing accuracy, and developing novel interaction paradigms like the Hex-o-Spell mental typewriter while addressing statistical challenges like covariate shift in neural data analysis. Professional Experience includes academic research at TU Berlin's Intelligent Data Analysis group, industrial software development roles at Henkel AG's Scientific Computing department, and TÜV Nord Group's Optical Metrology and Machine Diagnostics divisions. He maintains active research connections through collaborative publications with leading experts in the field.
Dr. Ernesto Elias Vidal Rosas is a Lecturer at the School of Electronics and Computer Science, University of Southampton. Previously, he was a Postdoctoral Researcher at UCL Medical Physics and Biomedical Engineering Department. He specializes in neuroimaging and optical technologies with extensive expertise in near-infrared spectroscopy (NIRS), diffuse optical tomography (DOT), and wearable brain imaging systems. He is currently accepting PhD students. Research Focus: Dr. Vidal-Rosas leads research in neuroimaging methodologies and brain-computer interfaces. His work emphasizes wearable technologies for clinical and home-based monitoring, with applications spanning from neonatal care to motor rehabilitation. Key research domains include: Development of high-density diffuse optical tomography (HD-DOT) systems Integration of EEG and fNIRS for brain-computer interfaces Real-time processing algorithms for neurofeedback applications Non-invasive monitoring of therapeutic responses in oncology He is currently involved in the Horizon Europe PUREMIND project focused on preventing mental illness through multimodal biomarker analysis. Academic Activities: Actively advises PhD candidates in Computer Science and teaches undergraduate courses including Healthcare Technology Design (ELEC2231) and Digital Control System Design (ELEC3206). His pedagogical research includes developing virtual tools for engineering education. Affiliations: Member of the Digital Health and Biomedical Engineering research group and the Institute for Life Sciences at Southampton.
Vahid Behzadanمشاهده پروفایل
استادیار
Vahid Behzadan is an Assistant Professor in Data Science and Computer Science at the University of New Haven's Tagliatela College of Engineering. He leads the SAIL Lab, focusing on AI safety and security, particularly in autonomous systems like driverless cars and smart cities. His work addresses adversarial attacks on machine learning and reinforcement learning, with applications in cybersecurity and healthcare. Behzadan has held prior positions at Kansas State University, University of Nevada Reno, and University of Birmingham, UK. He holds a Ph.D. in Computer Science and an M.S. from the University of Nevada Reno, and a B.Eng. from the University of Birmingham. His research spans AI ethics, cybersecurity, and complex systems. Behzadan advises the UNH hacking team and actively participates in policy initiatives, including Connecticut's AI Working Group and the Connecticut AI Alliance. He has contributed to over 30 peer-reviewed articles and frequently engages in media discussions on topics like AI safety, facial recognition, and cybersecurity threats. Key research areas include adversarial machine learning, AI forensics, and ethical AI design. His work bridges theoretical advancements with real-world applications in transportation, healthcare, and national security. Behzadan collaborates with organizations such as the Transportation Research Laboratory (TRL) and Open Web Application Security Project (OWASP).