جستجوی استاد راهنمای دانشگاهی

José del R. Millánمشاهده پروفایل

استاد

• Brain-machine Interfaces (BMI)
• Neuroengineering
• Neuroprosthetics
• +۶ مورد دیگر

Dr. José del R. Millán is a Professor and holds the Linda Steen Norris & Lee Norris Endowed Chair in Neuroengineering at The University of Texas at Austin's Chandra Family Department of Electrical and Computer Engineering. He also serves as a Professor in Dell Medical School's Department of Neurology, a courtesy Professor in Biomedical Engineering, and is affiliated with the Mulva Clinic for the Neurosciences, Institute for Neuroscience, Texas Robotics, and the UT CARE Initiative. His work focuses on brain-machine interfaces (BMI), neuroprosthetics, and translating BMI technologies for individuals with motor/cognitive disabilities and able-bodied users. Education: PhD in Computer Science (1992, Technical University of Catalonia). Previous roles include Defitech Foundation Chair in Brain-Machine Interface at EPFL (Switzerland) and visiting scholar positions at Berkeley, Stanford, and the International Computer Science Institute. Research Interests: Neuroengineering, BMI applications in healthcare and assistive robotics, statistical machine learning for neural signals, and neurorehabilitation. Key contributions include EEG-based BMI systems, closed-loop neurostimulation, and wearable neurotechnology. Awards: IEEE Fellow (2017), Norbert Wiener Award (2011), and Fellow of the International Academy of Medical and Biological Engineering (2020). Grants & Labs: Co-director of UT CARE, leader in clinical neuroprosthetics and neurorobotics. Active in developing BMI-driven wheelchairs, VR integration for BCI, and EEG-based speech prosthetics. Research outputs emphasize translational neurotechnology, with projects funded by industry and governmental agencies. Labs/Teams: Clinical Neuroprosthetics & Brain Interaction Lab, Texas Robotics, Wireless Networking and Communications Group (WNCG).

• Cognitive Neuroscience
• Systems Neuroscience
• Motor Systems
• +۳ مورد دیگر

Mark Churchland is an Associate Professor of Neuroscience at Columbia University's Mortimer B Zuckerman Mind Behavior Institute. His research focuses on understanding neural dynamics in motor cortex, particularly how these dynamics generate movement patterns and contribute to neural prosthetics development. He is also a Co-director of the Grossman Center for the Statistics of Mind and a member of the Kavli Institute for Brain Science. Co-director, Grossman Center for the Statistics of Mind Member, Kavli Institute for Brain Science Member, Behavior Research Churchland's research investigates the computational properties of neural circuits in motor cortex, emphasizing how self-generated neural activity underpins movement. His work bridges theoretical neuroscience with practical applications in brain-machine interfaces (BMIs), translating basic science into technologies that restore movement capabilities by bypassing spinal cord injuries. Key themes include neural population dynamics, movement variability, and cortical preparation mechanisms. His publications reveal consistent exploration of neural dynamics in motor systems, focusing on population coding, variability suppression, and BMI algorithm design. Research spans both fundamental questions about cortical computation and translational efforts to enhance prosthetic devices through improved understanding of preparatory neural activity. McKnight Scholar Award Sloan Research Fellow NIH Director's New Innovator Award Program Searle Scholars Program Burroughs Wellcome Fund Career Award in the Basic Biomedical Sciences Churchland has secured NIH grants for projects on dynamical systems in neuroscience (2012-2017). His collaborative work with Krishna Shenoy and colleagues at Stanford has produced foundational studies on cortical activity patterns and motor control. He actively participates in academic committees and research networks focused on computational neuroscience and mind-brain relationships.

Sungyong Ahnمشاهده پروفایل

مدرس

• Digital Ontology
• New Materialism
• Speculative Capitalist Realism
• +۸ مورد دیگر

Dr. Sungyong Ahn is a Lecturer in Digital Media and Cultures at the School of Communication and Arts, University of Queensland, and an affiliate of the Centre for Digital Cultures & Societies. His research focuses on digital ontology, new materialism, and speculative (capitalist) realism, examining how smart technologies reshape human perception and governance. He holds a PhD in Media Studies from the University of Illinois at Urbana-Champaign. Education: Bachelor of Philosophy (Yonsei University), Master of Film and Television (Chung-Ang University), PhD in Media Studies (UIUC). His research investigates the interplay between technology, capitalism, and cultural production, particularly through the lens of the Internet of Things (IoT), quantum physics as cultural imagination, and French philosophy (Foucault, Deleuze, Badiou). Recent work includes analyzing IoT’s role in self-governance and the ontological anxieties of digital existence. Publications span journals like Media Theory , Journal of Cultural Economy , and Postmodern Culture . He is the author of Internet-ontologies-Things: Smart Objects, Hidden Problems, and Their Symmetries (2023). Available for supervision in media studies, digital cultures, and science/technology studies. Active in interdisciplinary collaborations, including the Centre for Digital Cultures & Societies.

Jose M. Carmenaمشاهده پروفایل

استاد

• Neuroscience
• Neuroprosthetics
• Sensorimotor Learning
• +۵ مورد دیگر

Jose M. Carmena is the Chancellor's Professor of Electrical Engineering and Neuroscience at the University of California-Berkeley and Co-Director of the Center for Neural Engineering and Prostheses (CNEP). His research focuses on brain-machine interfaces (BMIs), neuroprosthetics, and sensorimotor learning mechanisms. Ph.D., Robotics, University of Edinburgh (2002) M.S., Artificial Intelligence, University of Edinburgh (1998) M.S., Electrical Engineering, University of Valencia (1997) B.S., Electrical Engineering, Polytechnic University of Valencia (1995) Dr. Carmena's work bridges neural engineering and systems neuroscience, investigating corticostriatal plasticity, wireless neural recording systems (e.g., neural dust), and closed-loop BMI adaptation. His publications reveal expertise in Neuroprosthetic Algorithms , Wireless Neural Interfaces , and Sensorimotor Learning with applications in chronic neuroprosthetic systems. McKnight Technological Innovations in Neuroscience Award (2017) IEEE Fellow (2017) NSF CAREER Award (2010) Sloan Research Fellow (2009) Hellman Fellow (2007) His advisees include Paul Botros, Archit Gupta, and Vivek Athalye. Dr. Carmena has published extensively in journals like Nature , Neuron , and Nature Neuroscience , developing technologies such as ultrasonic neural dust for cortical recording and adaptive control algorithms for prosthetics.

Ramana Vinjamuriمشاهده پروفایل

دانشیار

• Brain-Machine Interfaces
• Neuroprosthetics and Exoskeletons
• Machine Learning
• +۴ مورد دیگر

Ramana Vinjamuri is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He holds a secondary appointment as Visiting Professor at the Indian Institute of Technology, Hyderabad, India. His academic journey includes a Ph.D. in Electrical Engineering from the University of Pittsburgh (2008), M.S. in Bioinstrumentation from Villanova University (2004), and B.Tech. in Electrical and Electronics Engineering from Kakatiya University (2002). Dr. Vinjamuri's research focuses on Brain-Machine Interfaces (BMIs) for upper-limb prostheses control , neuroprosthetics and exoskeletons , machine learning in motor control , and neurophysiological signal processing . His work extends synergy-based models to control 37-dimensional hand movements, addresses human-robot interaction through emotionally intelligent systems, and develops neurotechnologies for substance use disorder using wearable sensors and AI. NSF CAREER Award (2019) NSF IUCRC BRAIN Center Planning Grant (2020) Harvey N Davis Distinguished Teaching Assistant Professor Award (2018) His publications demonstrate expertise in EEG and EMG signal analysis , deep learning for motor decoding , synergy modeling , and humanoid robot control . The Vinjamuri Lab at UMBC involves graduate, undergraduate, and high school researchers, with international collaborations in India and the US.

Kathryn Hess Bellwaldمشاهده پروفایل

استاد

• Algebraic Topology
• Homotopy Theory
• Neuroscience
• +۴ مورد دیگر

Kathryn Hess Bellwald is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in both the School of Life Sciences and School of Basic Sciences . She leads the Laboratory for Topology and Neuroscience and serves as Academic Director for the Euler Programme . Her work bridges pure mathematics and interdisciplinary applications in neuroscience, materials science, and data analysis. Education : PhD in Mathematics (MIT, 1989), preceded by positions at Stockholm, Nice, and Toronto universities. Her research spans algebraic topology , homotopy theory , operad theory , and algebraic K-theory , with applications in neuroscience and materials science . She has pioneered topological data analysis methods for classifying neuronal morphologies , microglia phenotypes , and nanoporous materials , creating a parameter-free framework linking neural network structure to activity. The 15 most recent publications highlight her work on topological inverse problems , neuroinflammation , and equivariant homotopy . These studies often involve collaborations with the Blue Brain Project and EPFL teams in neuroscience , machine learning , and materials science . Scientific Awards : Fellow, American Mathematical Society (2017); Distinguished Speaker, European Mathematical Society (2017); Crédit Suisse Teaching Prize (2012); Polysphère d'Or (2013); Full Member, Swiss Academy of Engineering Sciences (2016); Chaire de la Vallée Poussin (2023); Fellow, Association for Women in Mathematics (2024). She has mentored numerous PhD students in mathematics and neuroscience, including Adélie Eliane Garin , Varvara Karpova , and Dimitri Zaganidis . Her EPFL Mathematics affiliations include the DIVISION MATH , while her Neuroscience lab operates under the Brain Mind Institute (BMI) in the School of Life Sciences (SV). Grants and collaborations are evident in her work on neurodegenerative diseases , synthetic materials , and machine learning frameworks .

Karim Oweissمشاهده پروفایل

استاد

• Neuroscience
• Biomedical Engineering
• Electrical Engineering
• +۵ مورد دیگر

Karim Oweiss is a Pre-eminent Professor at the University of Florida, with joint appointments in the Department of Biomedical Engineering (Herbert Wertheim College of Engineering), Electrical and Computer Engineering, and Neuroscience (McKnight Brain Institute). He holds a Ph.D. in Electrical Engineering and Computer Science from the University of Michigan (2002). His research focuses on neural mechanisms of sensorimotor integration and the development of clinically viable brain-machine interfaces (BMIs) to restore damaged neurological function. His work spans computational neuroscience, neural decoding, optogenetics, and advanced neurotechnology, with a strong emphasis on closed-loop systems and neural plasticity. 2025 : Chemogenetic stimulation of phrenic motor output and diaphragm activity 2024 : Chemogenetic phrenic motoneuron activation enables increased tidal volume 2023 : Compressive sensing of functional connectivity maps from patterned optogenetic stimulation Oweiss has received the NSF Excellence in Neural Engineering Award (2001) and is a Senior Member of the IEEE. He has published extensively on topics including neural decoding, compressive sensing, and multiscale neural interfacing. As editor of Statistical Signal Processing for Neuroscience and Neurotechnology (2010), he has contributed significantly to the field's methodological foundations. His lab develops tools like NeuroQuest for large-scale neural data analysis and implantable neuroprocessors for wireless BMI applications.

Surjo R. Soekadarمشاهده پروفایل

استاد

• Clinical Neurotechnology
• Brain-Computer Interfaces (BCI)
• Neuromodulation
• +۸ مورد دیگر

Surjo R. Soekadar is the Einstein Professor of Clinical Neurotechnology at Charité – University Medicine Berlin. He leads the Clinical Neurotechnology Laboratory , which focuses on developing noninvasive neurotechnologies for treating neurological and psychiatric disorders through closed-loop brain stimulation and advanced brain-machine interfaces (BCI/BMI). His work integrates real-time EEG/MEG monitoring with electromagnetic stimulation to modulate pathological brain oscillations and enhance neuroplasticity in conditions like stroke, spinal cord injury, and psychiatric disorders. Education : Studied medicine in Mainz, Heidelberg, and Baltimore Clinical Training : Residency in Psychiatry and Psychotherapy at University of Tübingen Academic Journey : 2008-2011 Research Fellow at NINDS (USA); 2017 Venia Legendi at University of Tübingen; 2018 First Professor of Clinical Neurotechnology in Germany His research interests span: • Closed-loop neurostimulation combining real-time brain state monitoring with targeted intervention • Next-generation BCI using optically pumped magnetometers (OPM) for mobile MEG recordings • Neurorehabilitation through exoskeleton control and sensory feedback • Neurophysiological modeling of entropy measures and phase flows Recent publications highlight: • Adaptive deep brain stimulation protocols • Real-time phase-sensitive tACS applications • OPM-based BCI innovations • Stroke recovery mechanisms through corticospinal tract analysis Scientific recognition includes: International BCI Research Award BIOMAG Award NARSAD Young Investigator Award Funded by the European Research Council (ERC) , his lab trains doctoral students like David Haslacher (EEG/MEG integration), Khaled Nasr (multicoil TMS optimization), and Annalisa Colucci (entropy-driven BCI development). The team also explores quantum AI applications in clinical decision-making and bidirectional BCI systems using OPM and tES.

Paul Nuyujukianمشاهده پروفایل

استادیار

• Motor Systems Neuroscience
• Neuroengineering
• Brain-Machine Interfaces
• +۴ مورد دیگر

Paul Nuyujukian serves as an Assistant Professor of Bioengineering and Neurosurgery, with courtesy appointment in Electrical Engineering at Stanford University. He is a Faculty Scholar of the Wu Tsai Neurosciences Institute, directing the Brain Interfacing Laboratory where his team develops neural interface technologies for clinical applications in stroke and epilepsy. Education: MD, Stanford University (2014) PhD in Bioengineering, Stanford University (2012) BS, UCLA (2006) Dr. Nuyujukian's research integrates motor systems neuroscience with neuroengineering to decode brain activity during movement and recovery from injury. His laboratory pioneers brain-machine interface (BMI) platforms that translate neural signals into communication and control systems, with particular emphasis on intracranial EEG recording and real-time neural decoding. Current work focuses on developing clinically viable BMI solutions for neurological conditions through both preclinical models and human trials, advancing our understanding of neural population dynamics in health and disease. Recent publications reveal strong trends in intracranial EEG acquisition systems, seizure detection algorithms using information theory, and closed-loop BMI applications for ambulatory neuroscience. His work bridges fundamental neuroscience with clinical translation, particularly in epilepsy monitoring, chronic pain management, and neural prosthetics for paralysis. A notable emphasis exists on creating scalable, minimally invasive recording platforms that reduce clinical burden while maintaining high-fidelity neural data. Scientific Awards: No specific awards listed in provided materials As director of the Brain Interfacing Laboratory, Dr. Nuyujukian mentors students and collaborators in neural engineering research while securing grant funding for BMI development. His group maintains active collaborations with Stanford's Department of Neurosurgery and Neurology for clinical translation, with current projects including real-time decision-state decoding and personalized network mapping for pain management. The laboratory operates advanced facilities for both animal and human neural recording, emphasizing seamless integration of engineering innovation with clinical neuroscience. The Brain Interfacing Laboratory comprises multidisciplinary scientists and engineers developing next-generation neural interfaces. Current initiatives include the LiCoRICE platform for ambulatory neuroscience, seizure detection systems using compression-enabled entropy estimation, and ketamine's effects on hippocampal connectivity. The team actively participates in clinical trials for BMI applications in stroke rehabilitation and epilepsy, with strong partnerships across Stanford's medical and engineering schools to accelerate technology translation.

Lee Millerمشاهده پروفایل

استاد

• Motor Control
• Neural Networks
• Brain-Machine Interfaces
• +۳ مورد دیگر

Lee Miller is a Professor of Physiology, Physical Medicine & Rehabilitation, and Biomedical Engineering at the University of Chicago. His research focuses on understanding how the brain encodes movement commands through neural signals, with applications in developing brain-machine interfaces (BMIs) to restore motor function in paralyzed patients. His work integrates neuroscience, engineering, and computational methods to study neural networks in motor systems. Key research areas include decoding cortical signals to predict muscle activity, developing closed-loop BMIs, and investigating functional connectivity in neural circuits. Miller collaborates extensively with the Biomedical Engineering Department and the Interdepartmental Neuroscience Program (NUIN). His lab combines experimental approaches (e.g., chronic neural recordings) with computational tools to study neural dynamics and develop therapeutic technologies. Recent work emphasizes restoring hand function via cortically controlled functional electrical stimulation (FES), translating neural signals into muscle activation. His publications span neural decoding algorithms, sensory feedback systems, and the neurobiology of motor control. Miller’s contributions bridge fundamental neuroscience and clinical neuroengineering, with potential impacts on spinal cord injury rehabilitation and prosthetic control systems.

Sujith Vijayanمشاهده پروفایل

دانشیار

• Computational modeling
• Brain oscillatory dynamics in health and disease
• Development of stimulation and pharmacological therapies for brain disorders
• +۷ مورد دیگر

Sujith Vijayan is an Associate Professor at the School of Neuroscience , part of the Virginia Tech College of Science . His research focuses on neural dynamics during sleep and active behavior , leveraging computational modeling , signal processing , and invasive/non-invasive recording to explore memory consolidation, emotional regulation, and therapeutic applications for neurological disorders. Research Interests include: Sleep-mediated memory processing Brain-computer interface (BMI) learning Pathological brain rhythms in PTSD and Parkinson’s Neural stimulation therapy development REM sleep emotional memory mechanisms His 15 most recent publications reveal a trajectory from anesthetic-induced neural oscillations (2013), through schizophrenia network analysis (2015), to targeted memory reactivation during sleep (2017). Key scientific contributions include the 2022 NSF CAREER award for BCI learning enhancement via sleep manipulation . Lab members include graduate students in Biomedical Engineering, TBMH, and Neuroscience programs, with alumni now at institutions like Barrow Neurological Institute and University of Arizona’s Sleep Lab.

• Neural Engineering
• Neural Prosthetics
• Brain Plasticity
• +۷ مورد دیگر

Stephen Helms Tillery is a professor at Arizona State University (ASU), holding the Lincoln Chair in Neural Engineering and Ethics. He is affiliated with the School of Biological and Health Systems Engineering and the Lincoln Center for Applied Ethics. His research focuses on neural mechanisms underlying sensorimotor control, particularly how the brain integrates sensory inputs to guide movement. He also leads ethical discussions on aligning research with societal benefit. Education: B.S. in Psychology (ASU), neuroscience studies at University of Minnesota (under John Soechting and Tim Ebner), postdoctoral work at SUNY Syracuse (under Peter Strick), and neuroprosthetics research at ASU with Andrew Schwartz. Research interests include brain plasticity, cortical neurophysiology, and neural prosthetics. His work combines human and nonhuman primate studies, with contributions to tactile feedback systems, cortical mapping, and neuroethical frameworks for translational research. Key research trends in his articles emphasize sensory integration, neuroprosthetic design, and ethical considerations in neural engineering. He has developed novel methods for non-invasive cortical mapping and advanced haptic feedback systems for prosthetics. He participates in the Global Futures initiative as a Senior Global Futures Scientist, addressing long-term societal impacts of neuroscience research. His work bridges technical innovation with ethical responsibility, ensuring technologies benefit broader communities.

Joseph Makinمشاهده پروفایل

استادیار

• Mathematical Neuroscience
• Neural Computation
• Brain-Machine Interfaces
• +۷ مورد دیگر

Joseph Makin is an Assistant Professor of Electrical and Computer Engineering at Purdue University, affiliated with the Elmore Family School of Electrical and Computer Engineering. His research focuses on mathematical neuroscience, neural computation, and brain-machine interfaces (BMIs), with a strong emphasis on machine learning applications in neuroscience. He leads the Makin Lab, which develops algorithms for BMI/BCI systems, investigates theoretical aspects of neural computation, and explores pure machine learning principles inspired by biological systems. Education: Not explicitly listed in provided texts. His work bridges computational neuroscience and engineering, particularly in decoding speech and movement from neural activity using ECoG arrays. Notable projects include real-time speech decoding in paralyzed individuals and developing encoder-decoder frameworks for cortical activity translation. The lab also focuses on advancing recurrent neural network architectures and probabilistic graphical models, as evidenced by their textbook An Introduction to Modern Statistical Learning . Recent research trends emphasize self-supervised learning for speech decoding, hybrid system modeling of biological processes, and translational applications in neuroprosthetics. His team collaborates with neurosurgeons and biomedical engineers to translate theoretical insights into clinical tools. Scientific Awards: No explicit awards listed, though his BCI project reached finalist status in a major competition. Makin advises graduate students in machine learning and BMI projects, prioritizing strong backgrounds in neural networks and statistical methods. The Makin Lab is part of Purdue's broader efforts in computational neuroscience and biomedical engineering.

• Brain-Machine Interfaces
• Electroencephalography
• Neuroprosthetics
• +۳ مورد دیگر

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)

Payam Heydariمشاهده پروفایل

استاد

• Integrated Circuits Design
• RFIC (Radio-Frequency Integrated Circuits)
• Millimeter-Wave Systems
• +۶ مورد دیگر

Payam Heydari is the Henry Samueli Faculty Excellence Professor and University Chancellor’s Professor at the University of California, Irvine (UCI), holding joint appointments in Electrical Engineering and Computer Science (EECS) and Biomedical Engineering. He leads the Nanoscale Communication IC (NCIC) Labs, focusing on advanced RFIC, millimeter-wave, and biomedical circuit designs. His academic career includes distinguished roles such as IEEE SSCS and MTT-S Distinguished Lecturer (2014-2016 and 2019-2022). Education: B.S. and M.S. (Honors) from Sharif University of Technology (1992, 1995), Ph.D. from University of Southern California (2001). Professional experience includes research internships at Nokia Bell Labs and IBM T.J. Watson Research Center. Research Interests: Design of high-frequency integrated circuits for communication and biomedical applications, low-power RF front-ends, and brain-machine interfaces. Over 190 publications, including co-authoring two books and multiple invited keynotes at IEEE conferences like ISSCC and IMS. Awards: 2024 IEEE Darlington Award, 2023 MTT-S Distinguished Educator Award, 2021 SSCS Innovative Education Award, and NSF CAREER Award. Recognized as a Fellow of the National Academy of Inventors and IEEE. Service: Editor roles at IEEE JSSC, OJ-SSCS, and SSC-L. Member of ISSCC Technical Program Committee (2014-2019), ESSCIRC, and IMS 2021 committees. Director of NCIC Labs.