جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Neuroscience
• Biomedical Engineering
• +۳ مورد دیگر

Parisa Sarikhani is a Research Fellow in the Department of Electrical and Computer Engineering at the Georgia Institute of Technology. Her research focuses on developing intelligent closed-loop neuromodulation systems using artificial intelligence (AI) and computational methods to address neurological and cardiovascular disorders. She explores the integration of AI, biomedical engineering, and control systems to optimize therapies for conditions like Parkinson’s disease, essential tremor, and spinal cord injuries. Her work emphasizes safety and efficacy in neurostimulation techniques, including transcutaneous spinal cord stimulation, vagus nerve stimulation, and deep brain stimulation. Recent studies highlight automated programming frameworks, adaptive control systems, and the use of wearables for real-time patient monitoring. She also investigates cross-domain optimization strategies and software-defined platforms to enhance medical device interoperability. Key contributions include the Neuroweaver platform for closed-loop neuromodulation design and the CONTROL-CORE framework for peripheral neuromodulation simulation. Her research bridges computational modeling with clinical applications, aiming to advance precision medicine and personalized therapies. Parisa’s publications span AI-driven medical devices, safety-constrained stimulation protocols, and interdisciplinary systems engineering. While no scientific awards are explicitly listed, her work reflects sustained innovation in neurotechnology and closed-loop control systems.

• Neural Interfaces
• Neuromodulation
• Migraine Pain
• +۳ مورد دیگر

Jitendran Muthuswamy is an Associate Professor at Arizona State University (ASU), affiliated with the School of Biological and Health Systems Engineering and the School of Life Sciences. His research focuses on neural interfaces, neuromodulation for migraine pain, and brain-tissue mechanics. He holds editorial roles at Frontiers in Neuroscience , PLOS One , and IEEE Transactions on Neural Systems and Rehabilitation Engineering . Muthuswamy directs the Neural Microsystems Laboratory and has expertise in developing implantable devices for neural monitoring and stimulation. Education B.Tech., Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur M.S. and Ph.D., Biomedical Engineering, Rensselaer Polytechnic Institute Postdoctoral Fellowship, Johns Hopkins University Research Interests His work emphasizes neuromodulation therapies, neural interface design, and understanding cellular mechanisms of pain transmission. Key areas include intracranial pressure monitoring, optogenetic tools, and chronic implant stability. Recent projects involve wireless neurostimulators for migraine treatment and ultrasonically powered biosensors. Grants & Activities NIH Blueprint Medtech Accelerator Seedling (2023–2024) Arizona NEI Science and Technology Center (2024–2025) Flinn Foundation grants for occipital nerve neuromodulation (2021–2024) Teaching Muthuswamy teaches courses in biomedical engineering, including Biomedical Product Design , Cyber Biomedical Systems , and Honors Thesis Research .

Shane Leeمشاهده پروفایل

استادیار

• Computational Neuroscience
• Deep Brain Stimulation
• Parkinson's Disease
• +۱ مورد دیگر

Shane Lee is Assistant Professor of Neurosurgery (Research) at Brown University. His laboratory combines intraoperative neural recordings with computational modeling to develop improved neuromodulation therapies for Parkinson's disease and essential tremor. Research focuses on: Biomarker discovery for motor symptoms Closed-loop deep brain stimulation systems Neural mechanisms of tremor generation Quantitative assessment of movement disorders Lee develops novel tools including low-cost movement quantification systems and open-source platforms for imaging analysis in deep brain stimulation. His computational models bridge cellular mechanisms with clinical symptom expression.

Uri Tzvi Edenمشاهده پروفایل

استاد

• Neural Data Analysis
• Statistical Modeling
• Point Process Theory
• +۵ مورد دیگر

Uri Tzvi Eden is a Professor and Associate Chair of the Department of Mathematics and Statistics at Boston University's College of Arts and Sciences. He is a member of the Probability and Statistics research group, where he focuses on developing mathematical and statistical methods to analyze neural spiking activity. His work integrates model identification, statistical inference, signal processing, and stochastic estimation with point process observation models specifically tailored for neural systems. Professor Eden's research spans two main categories: methodological development of statistical frameworks for relating neural activity to biological and behavioral signals, and practical applications of these methods to real neural systems. His work enables dynamic modeling of individual neuron spiking properties, characterization of neural ensemble representations, and real-time signal reconstruction. His research interests center on neural data analysis, statistical modeling of point processes, machine learning applications in neuroscience, signal processing techniques for neural recordings, and mathematical neuroscience. Eden has made significant contributions to developing estimation algorithms, goodness-of-fit analyses, and mathematical theory applicable to neural spiking systems. Professor Eden's publication record shows a consistent focus on neural data analysis with recent work expanding into sleep spindle dynamics, epilepsy research, Parkinson's disease applications, and advanced machine learning approaches for neural decoding. His research demonstrates a trajectory from foundational methodological work toward increasingly sophisticated applications in clinical neuroscience. Professor Eden earned his Ph.D. in Medical Engineering/Medical Physics from Harvard/MIT Division of Health Sciences and Technology in 2005 with a thesis on point process filters in neural spiking model analysis. He has co-authored the textbook 'Analysis of Neural Data' published by Springer in 2014.

• Neuroscience
• Neurophysiology
• Neuroplasticity
• +۷ مورد دیگر

Paolo Belardinelli is an Associate Professor at the University of Trento’s Department of Cellular, Computational and Integrative Biology (CIBIO) and the Center for Mind/Brain Sciences (CIMeC). He holds a MS in Mathematical Physics from Sapienza University of Rome and a PhD in Neuroscience from the G. D’Annunzio University of Chieti. His academic career includes senior researcher roles at institutions like Aalto University (Finland), the MEG Center Tuebingen (Germany), and the University of Tuebingen. He has been an Assistant and Associate Professor at CIMeC since 2020. Belardinelli’s research focuses on understanding how brain states influence responses to stimuli and optimizing closed-loop neurostimulation. Key areas include EEG-TMS synchronization, brain connectivity, neuroplasticity, and clinical applications in stroke and psychiatric disorders. He has pioneered real-time EEG-triggered TMS methodologies to enhance rehabilitative outcomes. Education: MS in Mathematical Physics, Sapienza University of Rome PhD in Neuroscience, G. D’Annunzio University of Chieti His work combines EEG/MEG source localization with non-invasive stimulation techniques. He has authored/co-authored over 80 publications and serves on editorial boards for journals like Journal of Neuroscience Methods and Cognitive Processing . Notable grants include studies on Bayesian modeling for source reconstruction and functional connectivity mapping. Belardinelli advises PhD students and master’s theses, focusing on neurorehabilitation and neurophysiological mechanisms. His lab explores cutting-edge neurotechnology, including hyperbolic neural networks for aging trajectory modeling and reinforcement learning for closed-loop TMS optimization. Key achievements include defining EEG-TMS standards and demonstrating state-dependent stimulation efficacy.

Méadhbh Brosnanمشاهده پروفایل

استادیار

• Cognitive Neuroscience
• Neurodegenerative Diseases
• Healthy Ageing
• +۳ مورد دیگر

Méadhbh Brosnan is an Assistant Professor and Ad Astra Fellow at University College Dublin's School of Psychology, with an adjunct position as Senior Research Fellow at Monash University's Turner Institute for Brain and Mental Health. Her research focuses on cognitive neuroscience mechanisms of resilience in aging, neurodegenerative conditions, and stroke recovery. She holds a PhD from Trinity College Dublin and has held prestigious fellowships at Oxford, Monash, and Trinity. Education: BA (Hons), National University of Ireland Galway MSc Cognitive & Clinical Neuroscience, Maastricht University PhD Psychology, Trinity College Dublin Research Interests: Brosnan investigates how environmental enrichment and cognitive reserve mitigate age-related cognitive decline. Her work combines neuroimaging (fMRI, EEG, DTI) with behavioral studies to map neural correlates of attention, decision-making, and resilience. Key areas include the role of right-lateralized fronto-parietal networks, noradrenergic systems, and white matter plasticity. Grant Highlights: EU Horizon 2020 Marie Skłodowska-Curie Fellowship Alzheimer’s Research UK funding John Fell Oxford University Press Research Fund Teaching: Coordinates courses in Behavioral Neuroscience, Neuropsychology, and Psychology of Aging at UCD. Recent modules include MPsychSc Research Dissertation supervision. Labs/Teams: Leads the AGEING PLASTICITY project exploring neurophysiological markers of cognitive resilience through neuroimaging and neuromodulation techniques.

Axel Huttمشاهده پروفایل

پژوهشگر

• Neural Systems
• Nonlinear Stochastic Processes
• Data Assimilation
• +۴ مورد دیگر

Dr. Axel Hutt is a senior researcher (Directeur de Recherche) at INRIA Grand Est in Strasbourg, France, leading the MIMESIS team. His work focuses on neural systems modeling, nonlinear dynamics, and data assimilation in complex systems. He holds a PhD from the University of Stuttgart (2001) and an HDR (Habilitation) from the University of Nice (2013). Hutt’s research bridges neuroscience, mathematics, and engineering, with contributions to anesthesia modeling, neurostimulation, and EEG analysis. He has held roles at the Max Planck Institute, Humboldt University Berlin, and the University of Ottawa, and received the Schloessmann Fellowship (2000) and ERC Starting Grant (2011). His current projects include PhD supervision of T. Nette and participation in high-profile conferences like Re:publica and ICMNS. Recent work explores myelination effects, closed-loop neurostimulation, and AI ethics. Education: Physics Diploma (Stuttgart, 1997), PhD (Stuttgart, 2001), HDR (Nice, 2013) Key Roles: INRIA Team Leader (MIMESIS, 2019–present), NeuroSys Team Head (2013–2015) Research Themes: Neural field theory, stochastic processes, clinical neurostimulation applications Publications: Over 180 peer-reviewed articles across journals like Communications Physics and PLoS Computational Biology , focusing on noise-driven dynamics, EEG modeling, and computational psychiatry. Awards: ERC MATHANA Grant (2011), Schloessmann Fellowship (2000)

• Machine Learning
• Signal Processing
• Neuroscience
• +۳ مورد دیگر

Matilda Makkonen is a Doctoral Researcher at Aalto University's Department of Neuroscience and Biomedical Engineering, specializing in transcranial magnetic stimulation (TMS) and electroencephalography (EEG) integration with machine learning techniques. She holds a Master's degree in Engineering and Technology from Aalto University (2023) and a Bachelor's degree in the same field (2021). Key Research Areas: Machine learning for real-time signal processing, closed-loop EEG-TMS systems, and artifact rejection techniques. Research Trends: Focuses on improving brain stimulation methods through advanced signal decoding and noise reduction algorithms. Makkonen has been actively involved in organizing international workshops like the TMS–EEG Summer School and the Brain Stimulation and Imaging Meeting (2022-2024).

• Neuroscience
• Biomedical Engineering
• Transcranial Magnetic Stimulation
• +۵ مورد دیگر

Heikki Sinisalo is a Visiting Professor at the Department of Neuroscience and Biomedical Engineering at Aalto University. His research focuses on advanced brain-stimulation technologies, particularly transcranial magnetic stimulation (TMS) integrated with magnetic resonance imaging (MRI) and digital signal processing . Education: Doctor of Technology (Teknillinen fysiikka) – Aalto University, 30 Jun 2025 Diploma Engineer (Tietotekniikka) – Aalto University, 16 Nov 2018 Bachelor of Technology (Sähkötekniikka) – Aalto University, 20 Jun 2016 Sinisalo has pioneered multi-locus TMS systems for clinical and preclinical applications, emphasizing high-precision timing , ultra-high-field MRI compatibility , and pulse-width modulation to enhance stimulation accuracy. His work bridges engineering with neuroscience, targeting cortical and brain-stimulation advancements. In recent years, Sinisalo’s publications highlight innovations in closed-loop TMS , multi-coil configurations , and rapid stimulation protocols . Key technologies include field-programmable gate arrays , capacitor voltage optimization , and digital interfaces for real-time neuromodulation. He actively contributes to research infrastructure, having organized the TMS–EEG Summer School and Workshop (2022) , fostering collaboration between engineers and neuroscientists. His work also includes open-access datasets, such as the High-Precision Timing Method for Closed-Loop TMS (Zenodo, 2024).

Selja Vaaltoمشاهده پروفایل

استاد مهمان

• Neuroscience
• Biomedical Engineering
• Transcranial Magnetic Stimulation (TMS)
• +۲ مورد دیگر

Selja Vaalto is a Visiting Professor at Aalto University's Department of Neuroscience and Biomedical Engineering. She holds a Doctoral degree in Medical and Health Sciences from the University of Eastern Finland, indicating specialization in clinical neuroscience applications. Key affiliations: Aalto University, University of Helsinki Hospital Primary research areas: Neuroscience, Biomedical Engineering Her work focuses on Transcranial Magnetic Stimulation (TMS) technologies, including: Language-critical brain area mapping using combined MEG-informed TMS and fMRI Network engagement analysis for optimized brain stimulation protocols Individualized motor stroke treatment approaches with closed-loop TMS systems Speech cortex mapping for surgical planning Neuropathic pain management using repetitive TMS Recent publications demonstrate strong collaboration with researchers like Petteri Lioumis and Risto Ilmoniemi, particularly in developing navigated TMS applications and adaptive stimulation techniques . While no specific awards are documented, her involvement in multiple peer-reviewed publications and funded research activities indicates significant contributions to brain stimulation research.

Jaakko Nieminenمشاهده پروفایل

استاد مهمان

• Transcranial Magnetic Stimulation
• Neuroscience
• Biomedical Engineering
• +۷ مورد دیگر

Jaakko Nieminen is a Visiting Faculty member in the Department of Neuroscience and Biomedical Engineering at Aalto University's School of Science. He holds a Doctoral degree in Engineering and Technology from Aalto University (2012) and a Master's degree from Helsinki University of Technology (2008). Doctoral degree, Engineering and Technology, Aalto University (2012) Master's degree, Engineering and Technology, Helsinki University of Technology (2008) His research focuses on transcranial magnetic stimulation (TMS), particularly the development and optimization of multi-locus and multi-coil TMS systems for precise brain stimulation. His work integrates neuroscience, biomedical engineering, and computational modeling to advance neuromodulation techniques. Key interests include electric field orientation, motor evoked potentials, primary motor cortex circuitry, and closed-loop TMS with EEG feedback. Recent publications highlight advancements in multi-locus TMS systems, deep learning for motor evoked potential analysis (DELMEP), and spatiotemporal modulation of brain networks. His research trend emphasizes precision, automation, and integration of neuroimaging and electrophysiological feedback in brain stimulation technologies. Scientific Awards: Innovation of the Year Award (2017) McKinsey-palkinto, McKinsey & Company, USA (2009) McKinsey-palkinto, McKinsey, USA (2008) The Best Graduate Thesis Award (2009) The Best PhD Poster Award (2012) Nieminen has served as Principal Investigator on multiple Academy of Finland-funded projects focused on multi-locus TMS, demonstrating leadership in securing research grants. He has supervised theses and reviewed PhD dissertations, contributing to academic training. His collaborative network spans neuroscience, engineering, and clinical neurophysiology, with extensive publication output and media coverage of his work on making brain stimulation more reliable through algorithmic advancements. He is actively involved in research teams developing next-generation TMS technologies, including software tools like DELMEP and hardware systems for clinical and preclinical applications. His work continues to push the boundaries of non-invasive brain stimulation through engineering innovation and rigorous scientific investigation.

Hanna Suominenمشاهده پروفایل

استاد

• Machine Learning
• Health Informatics
• Natural Language Processing
• +۴ مورد دیگر

Prof Hanna Suominen is a Professor of Computer Science and Associate Director (Neuroinformatics) at the Australian National University (ANU), and Co-Chair of the ANU Our Health in Our Hands strategic initiative. She holds a PhD and Adjunct Professorship from the University of Turku (Finland), and a Master of Leadership in Curriculum and Pedagogy from Monash University. Her research focuses on applying machine learning and data analytics to healthcare, particularly in neuroinformatics, precision medicine, and medical technology. She leads projects in AI-assisted workflows, chronic disease management, and medical imaging analysis. Notable contributions include developing tools for Parkinson's disease diagnosis via EEG, diabetes glucose prediction systems, and NLP-based medical information extraction. She has received the Senior Fellow of the Higher Education Academy (SFHEA) and has led teams at CSIRO/Data61. Her work bridges technology, education, and health to enhance Australia's healthcare innovation. Education: PhD in Computer Science (University of Turku, 2009) Adjunct Professorship (University of Turku, 2013) MSc in Applied Mathematics (University of Turku, 2005) MEdL in Curriculum & Pedagogy (Monash University, 2020) Research Interests: Machine learning applications in healthcare, neuroinformatics, medical imaging analytics, personalized medicine, natural language processing for health data, and AI ethics in medical technology. Her work emphasizes interdisciplinary collaboration with industry partners like Lightcast and Konan Medical. Grants & Projects: Leads ANU initiatives like the John Eccles Institute and OHIOH, with current projects including closed-loop brain stimulation for depression, AI-driven diabetes management systems, and biomarker discovery for multiple sclerosis. Active in securing funding for AI in advanced materials and healthcare informatics. Awards: Senior Fellow of the Higher Education Academy (ANU, 2019) Labs/Teams: Former leader of the TAMPA (Multimodal Pattern Analysis) and NLP teams at CSIRO/Data61. Currently oversees neuroinformatics research at ANU's John Eccles Institute.

Christopher Pulliamمشاهده پروفایل

استادیار

• Neurorehabilitation Engineering
• Wearable Sensors for Neurological Monitoring
• Clinical Decision Support Systems
• +۳ مورد دیگر

Christopher Pulliam, PhD, is an Assistant Professor in the Department of Biomedical Engineering at Case Western Reserve University's Case School of Engineering and holds an affiliation with the School of Medicine. His research focuses on developing wearable/implantable technologies to objectively assess motor function in neurological injury patients and creating clinical decision support systems for optimizing neurorehabilitation interventions. Key areas include real-world motor recovery monitoring via wearable sensors and predictive modeling of intervention efficacy. Affiliations: Case School of Engineering, School of Medicine Key Projects: ECAP-based implant monitoring, posture-adaptive stimulation control, AI-driven symptom quantification Technologies: Deep brain stimulation (DBS) programming, generative adversarial networks (GANs) for data augmentation, motion sensor-based assessment His work emphasizes translating engineering solutions into clinical tools, such as automated DBS parameter selection and real-time therapy adjustment systems for Parkinson's disease and essential tremor. Recent innovations include closed-loop systems that dynamically optimize stimulation protocols based on patient posture and neural response metrics. Grants and collaborations involve Medtronic and Great Lakes NeuroTechnologies, with patents covering AI symptom quantification and ECAP-based lead migration detection. His lab focuses on integrating clinical needs with advanced neurotechnology to address unmet challenges in neurorehabilitation.

Daniel Wagenaarمشاهده پروفایل

استاد پژوهشی

• Neuroscience
• Neuroengineering
• Sensory Processing
• +۳ مورد دیگر

Daniel Wagenaar is a Research Professor in the Division of Biology and Biological Engineering at the California Institute of Technology (Caltech), co-directing the Chen Center for Data Science and Artificial Intelligence (DataSAI). He leads the Wagenaar Lab and Neurotechnology Laboratory, focusing on neuronal basis of sensory processing, cross-modal integration in leeches, and neurotechnology. His work bridges experimental neuroscience, computational methods, and engineering, with contributions to voltage-sensitive dye imaging, multi-electrode array (MEA) techniques, and open-source software tools like VScope and MEABench. Education: M.S. degrees from University of Amsterdam (1997) and King's College London (1998), Ph.D. in Biology from Caltech (2006). Research emphasizes functional neural circuits, sensory-guided behavior, and neurotechnological innovations. His lab develops novel tools for neural activity recording and analysis, including double-sided microscopy and optogenetic technologies. Funding sources include NIH, Burroughs Wellcome Fund, and Caltech. Key contributions include elucidating leech neural circuits, hormone-activated courtship behaviors, and high-density neural recording in primates. Awards and recognition include sustained research excellence in systems neuroscience. Advising includes mentorship in neurobiology, engineering, and computational neuroscience. Ongoing work explores brain-wide neural dynamics and translational neurotechnologies.

Preeya Khannaمشاهده پروفایل

استادیار

• Sensorimotor learning and control
• Neural engineering
• Brain-machine interfaces
• +۵ مورد دیگر

Preeya Khanna is an Assistant Professor in the Department of Bioengineering within the College of Engineering at the University of California, Berkeley, with affiliations at the Helen Wills Neuroscience Institute. Her research integrates sensorimotor neuroscience, computational modeling, and neurotechnology development to restore movement in damaged sensorimotor systems. Her educational background includes: PhD in Bioengineering from UC Berkeley (2017) BSE in Bioengineering & Mathematics from the University of Pennsylvania (2012) Dr. Khanna's research focuses on uncovering neural principles of dexterous movement control through studies of sensorimotor learning, brain-machine interfaces, and rehabilitation engineering. She develops neurophysiologically-grounded therapies including neurofeedback and neuromodulation approaches for stroke rehabilitation, bridging computational neuroscience with clinical translation. Her recent publications (2022-2025) reveal a strong trajectory in decoding motor cortex dynamics, particularly beta oscillations and ensemble reactivations, for therapeutic applications. Key themes include closed-loop BMI systems for severe stroke patients, low-frequency stimulation protocols, and kinematic-based impairment assessment, demonstrating convergence of computational modeling, neural engineering, and clinical rehabilitation. Her scientific recognition includes: Sloan Research Fellow (2024) Google Faculty Research Award (2024) NIH Director's New Innovator Award (2024) As Principal Investigator of the Sensorimotor Neural Engineering Lab, Dr. Khanna leads NIH and Google-funded research on neurotechnology for motor rehabilitation. Her lab actively recruits researchers to develop next-generation brain-machine interface therapies targeting sensorimotor restoration. The Sensorimotor Neural Engineering Lab investigates how sensory and motor brain signals coordinate dexterous behaviors, leveraging these principles to design neurotechnology for damaged sensorimotor networks. Current work focuses on closed-loop neurostimulation systems and real-time neural decoding for stroke rehabilitation.