جست‌وجوی استاد و پژوهشگر

Matthew Holdenمشاهده پروفایل

دانشیار

• Surgical Data Science
• Machine Learning
• Computer-Assisted Surgery
• +۳ مورد دیگر

Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.

Hak-Keung Lamمشاهده پروفایل

استاد

• Fuzzy Control Systems
• Neural Networks
• Control Theory
• +۴ مورد دیگر

Dr. Hak-Keung Lam is a Reader in the Department of Engineering at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. He holds an IEEE Fellowship and has been a Clarivate Web of Science Highly Cited Researcher since 2018. His research focuses on fuzzy control systems, neural networks, stability analysis, and their applications in biomedical and engineering domains. Education: Dr. Eng. (2000), B. Eng. (1995), both from Hong Kong Polytechnic University. Research Interests: Fuzzy modeling, neural network-based control, computational intelligence, machine learning, and biomedical applications such as ECG/EEG signal classification. His work bridges theoretical advancements with practical implementations in robotics, autonomous systems, and healthcare technology. Publications: Over 480 publications (as of 2023) in top-tier journals and conferences, with a focus on control systems, fuzzy logic, and intelligent systems. Recent work includes fault-tolerant control, cyber-physical systems, and explainable AI. Awards: IEEE Fellow (2019), 1st Place in PhysioNet Computing in Cardiology Challenge (2022). Grants/Projects: Active projects include fuzzy control system stabilization, autonomous robots in healthcare environments, and networked control of robotic systems. Labs/Teams: Center for Robotics Research, contributing to solutions for societal challenges through robot-centric approaches.

• Epilepsy
• Neurology
• Neural Networks
• +۵ مورد دیگر

Aline Herlopian, MD, is an Associate Professor of Neurology at Yale School of Medicine, specializing in epilepsy and treatment-resistant seizures. She serves as Medical Director of the Clinical Neurophysiology/EEG Lab and Director of Clinical Trials in Epilepsy. Her clinical focus includes catastrophic epilepsies and innovative treatments like brain stimulation, laser therapy, and surgical resection. Clinical Epilepsy Fellowship, Massachusetts General Hospital (2017) Clinical Neurophysiology Fellowship, Massachusetts General Hospital (2016) Neurology Residency, University of Arkansas for Medical Sciences (2015) MD, American University of Beirut (2008) Her research explores neural network dynamics in epilepsy, EEG pattern classification via machine learning, and global health applications in neurological care. She has authored 15+ publications in 2024-2025, including peer-reviewed articles and book chapters on epilepsy subtypes and surgical approaches. Inaugural Global Health Education Innovation Award (2025) Dr. Herlopian leads clinical trials in epilepsy and collaborates extensively with Yale Institute for Global Health. She is a renowned advisor for patient education, emphasizing seizure demystification through analogies, and contributes to Yale Co-Authors networks in neurology research.

• Engineering Education
• Engineering Practice
• Engineering Management
• +۱ مورد دیگر

A/Pr Steven Goh is an Associate Professor in Mechanical and Mechatronic Engineering at the University of Southern Queensland (USQ), affiliated with the School of Engineering. He holds advanced degrees including a DEng from USQ and is a Fellow of Engineers Australia. His research focuses on engineering education, practice, management, and biomedical engineering. He has received notable awards such as the 2015 Australian Government OLT Citation for Outstanding Contribution to Student Learning and multiple USQ accolades. Education: BEng(Hons) in Manufacturing & Materials (UQ), MBA (Deakin), MProfAcc (USQ), DEng (USQ), and a Diploma in Company Directorship (AICD). Research Interests: Engineering education innovation, sustainable energy systems, and biomedical applications. He actively contributes to professional bodies like the Australasian Association of Engineering Education and serves as Editor (Strategic) for the Australian Journal of Mechanical Engineering. Awards: Multiple teaching excellence awards from USQ (2008-2010) and the 2015 national OLT Citation. Advising/Grants: Not explicitly detailed in text; his roles include supervising students and leading research projects on engineering education and asset management. Labs/Teams: Associated with the Centre for Future Materials and Centre for Health Research at USQ.

Louis Collinsمشاهده پروفایل

استاد

• Neuroimaging
• Medical Image Processing
• Magnetic Resonance Imaging (MRI)
• +۷ مورد دیگر

Louis Collins is a Professor in the Department of Biomedical Engineering and Department of Neurology and Neurosurgery at McGill University, with associate membership at the Center for Intelligent Machines. His work focuses on advanced medical imaging techniques for neurological applications. Key Expertise: Non-linear image registration, model-based segmentation, neuroimaging, MRI analysis Applications: Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, schizophrenia Methodology: Development of computer vision algorithms for image-guided neurosurgery (IGNS), automated atlasing, and biomarker quantification Collins' research combines computational neuroanatomy with clinical translation, particularly in: Quantifying brain atrophy and anatomical variability across populations Optimizing MRI templates for improved diagnostic accuracy Developing tools like SEEGAtlas for surgical electrode classification Exploring neurophysiological fingerprints of neurodegenerative diseases His lab (NIST-Lab) actively pursues CIHR-funded projects on ultrasound-based image-guided neurosurgery and machine learning applications in clinical trials.

Steven Livingstoneمشاهده پروفایل

دانشیار

• Data Science
• Affective Computing
• Human Computer Interaction
• +۴ مورد دیگر

Steven Livingstone is an Associate Professor in the Department of Computer Science at Ontario Tech University, Faculty of Science. His research focuses on affective data science, applying machine learning and statistical modeling to understand emotion and its disorders. He leads the Affective Data Science Lab (ADSL), specializing in emotion recognition technologies using physiological data like EEG and motion capture. Livingstone holds a PhD from The University of Queensland (2008) and has published over 70 peer-reviewed papers, with over 2,400 citations. His RAVDESS dataset is widely used in speech emotion recognition research. His research interests span data science, affective computing, and music's role in emotion. Recent work emphasizes data provisioning for deep learning applications. Livingstone teaches courses including Scientific Data Analysis and Information Visualization. He actively mentors undergraduate and graduate students in his lab, focusing on research assistantships in emotion technology development. Key contributions include studies on musical tempo’s physiological effects, Parkinson’s disease facial mimicry deficits, and ensemble performance dynamics. His work has been featured in The Atlantic, NBC Today, and on the cover of Informatik Spektrum. The ADSL lab collaborates on projects combining data analytics with human-computer interaction to advance emotion-aware systems.

• Machine Learning
• Educational Data Mining
• Time Series Forecasting
• +۵ مورد دیگر

Irena Koprinska is a prominent researcher at the University of Sydney with over 150 publications from 1996 to 2025. Her work spans multiple interdisciplinary domains with significant contributions to machine learning applications in educational technology, time series forecasting, and health informatics. She maintains strong research collaborations, particularly with Kalina Yacef (38 joint publications), Mashud Rana (26 papers), and Bryn Jeffries (22 papers), indicating leadership in her research group. Her research interests focus on practical applications of machine learning across diverse domains. In educational data mining, she has pioneered methods for predicting student performance in programming courses, analyzing syntax errors, and developing automated hint generation systems. Her work in time series forecasting has made significant contributions to solar power prediction using advanced neural network architectures. Additionally, she has applied machine learning techniques to medical domains, particularly in sleep disorder detection and analysis. The analysis of her 15 most recent publications (2022-2025) reveals a continued focus on educational technology and time series analysis, with increasing attention to interpretable methods and health applications. Her work demonstrates a consistent trajectory of applying sophisticated machine learning techniques to solve real-world problems across multiple domains, with particular emphasis on creating practical tools for education and renewable energy management. Notable Research Contributions: Development of the HINTS framework for automated programming hint generation Innovative approaches to multistep-ahead time series forecasting Applications of deep learning to sleep disorder detection Methods for predicting student performance in programming education Her publication record in top venues including Machine Learning journal, AIED, EDM, and IJCNN demonstrates significant impact in both machine learning and educational technology communities. The consistent output of high-quality research over nearly three decades indicates sustained scholarly productivity and leadership in her fields of expertise.

Brett J. Borghettiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۱۲ مورد دیگر

Dr. Brett J. Borghetti is a Professor of Computer Science in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT), Graduate School of Engineering and Management, Wright-Patterson AFB, OH. He was promoted to Professor in July 2022, following prior appointments as Associate Professor (2017) and Assistant Professor (2008/2013). His expertise lies in artificial intelligence, machine learning, deep learning, cybersecurity, and human-machine teaming. Education: Ph.D. in Computer Science, University of Minnesota, Twin Cities (2008) M.S. in Computer Systems, Air Force Institute of Technology (1996) B.S. in Electrical Engineering, Worcester Polytechnic Institute (1992) Dr. Borghetti's research focuses on applying machine learning to physical science sensors (hyperspectral, seismic, RF), cybersecurity, and enhancing human-machine team performance. He teaches graduate courses in machine learning, AI, data security, and algorithm design, and advises numerous MS and PhD students in areas such as sensor exploitation, cognitive workload, and cyber situational awareness. His recent publications demonstrate strong trends in deep learning for multimodal sensor fusion, nuclear security, and neuroergonomics. Scientific Awards: AETC Educator of the Year (2021, Civilian) AFIT Ezra Kotcher Teaching Award (2021) AFIT Teaching Excellence Award (2019) AF STEM Outstanding Science and Educator Award (2015) Multiple Eta Kappa Nu Outstanding Instructor Awards Air Force Meritorious Service Medal and other military honors Dr. Borghetti has advised numerous graduate students and led research projects with significant funding and applications in defense and national security. He has directed research in AI-driven sensor analysis, cyber defense systems, and adaptive automation. His work often involves collaboration with national labs and DoD agencies. He has contributed to major research initiatives in human factors, cyber intruder detection, and machine learning for operational environments. Labs and Research Teams: His work is associated with AFIT's research in cyber security, sensor exploitation, and human-machine systems. He collaborates with teams working on the Cyber Intruder Alert Testbed (CIAT), neuroergonomic modeling, and machine learning for defense applications.

Robert J.K. Jacobمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• Brain-Computer Interfaces (BCI)
• User Interface Design
• +۵ مورد دیگر

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.

Tim Oatesمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۷ مورد دیگر

Dr. Tim Oates is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County . His research spans machine learning, artificial intelligence, and brain-machine interfaces, with a focus on weakly supervised methods, human-in-the-loop reinforcement learning, and grounded policy development for robotics. Ph.D., Computer Science, University of Massachusetts, Amherst, 2000 M.S., Computer Science, University of Massachusetts, Amherst, 1997 B.S., Computer Science and Electrical Engineering, 1989 Current research threads include: Developing non-invasive brain injury severity assessment via medical time series Modeling human brain development through computational frameworks Designing algorithms for autonomous robotic learning Recent publications highlight AI security mechanisms (backdoor detection via tensor decomposition, matrix factorization) Medical applications (3D artery reconstruction, skin lesion diagnosis, EEG denoising) Neuro-symbolic integration (holographic representations, language-guided reinforcement learning) Mathematical reasoning (schema-based problem solving, subitizing algorithms) Contact: oates@cs.umbc.edu | Office: 336 Information Technology and Engineering (ITE) Building

• Neuroscience
• Psychiatry
• Neurology
• +۵ مورد دیگر

Dr. Merve Fritsch is a Specialist in Psychiatry and Psychotherapy and a Senior Physician in ward 152A at Charité - Universitätsmedizin Berlin. Her affiliation is with the Department of Psychiatry and Neurosciences, part of the Neurology, Neurosurgery and Psychiatry division at Campus Charité Mitte. Dr. Fritsch's research focuses on neurological and psychiatric conditions, particularly thalamic aphasia, fronto-thalamic networks, and the role of the inferior frontal cortex in conscious perception. Her work integrates clinical neurology with cognitive neuroscience, addressing topics such as stroke recovery, perceptual decision-making, and NMDA receptor dysfunction. Her studies investigate ischemic lesions' effects on visual and language functions, stroke outcomes, and the mechanisms underlying consciousness. She employs advanced techniques like TMS-EEG to explore neural connectivity in conditions like autism spectrum disorder. Dr. Fritsch’s research also addresses clinical applications, such as cerebral embolism during cardiac interventions and the localization of aphasia in stroke patients. While no formal awards are listed, her contributions span peer-reviewed publications in high-impact journals, reflecting her expertise in neurology, psychiatry, and translational neuroscience. She actively engages in clinical practice and academic research, bridging theoretical insights with patient care.

Jörg Fachnerمشاهده پروفایل

استاد

• Music Therapy
• Social Neuroscience
• Neurorehabilitation
• +۳ مورد دیگر

Jörg Fachner is a Professor of Music, Health, and the Brain at Anglia Ruskin University (ARU), serving as Co-Director of the Cambridge Institute for Music Therapy Research. His research focuses on translational issues in interdisciplinary topics between medical, humanities, and music sciences. He holds a Dr. rerum medicinalium (Medical Sciences) from the University of Witten/Herdecke and a Diplom Pädagoge (Master in Education) from the University of Dortmund. **Research Interests:** Music therapy in neurorehabilitation, addiction treatment, dementia care, and mental health. Specializes in social neuroscience, EEG-based studies of therapeutic processes, and the neural dynamics of music-induced emotions. **Grants & Consultancy:** Major grants include the Josef Ressel Centre for Personalised Music Therapy Research in Neurorehabilitation (€1.1M), UKRI funding for RadioMe dementia technology (£35k), and collaborations with institutions like Aalborg University and Jyväskylä University. Consulted on projects such as depression and music therapy (NHS) and art therapies in addiction treatment (Germany). **Awards:** None explicitly listed, but recognized for contributions to music therapy research and clinical practice. **Labs/Teams:** Leads the Cambridge Institute for Music Therapy Research, collaborating on interdisciplinary projects like RadioMe and EEG hyperscanning studies. Supervises a global cohort of PhD students in music therapy and neuroscience.

Bradley V. Vaughnمشاهده پروفایل

استاد

• Sleep disorders
• Epilepsy
• Nocturnal events
• +۳ مورد دیگر

Dr. Bradley V. Vaughn is a Professor of Neurology at the University of North Carolina School of Medicine. His clinical and research focus spans sleep disorders, epilepsy, and neurological conditions affecting sleep physiology. Currently based in Chapel Hill, NC, he specializes in understanding the complex interactions between neurological diseases and sleep patterns. MD from Hershey Medical School Internship/Residency at Hershey Medical Center Chief Residency and Fellowship at UNC Research interests center on sleep medicine and neurological comorbidities , particularly exploring: Interactions between epilepsy and sleep cycles Polysomnography technical advancements Sleep disturbances in neurogenetic disorders Management of nocturnal neurological events His publications since 2004 demonstrate consistent contributions to understanding sleep architecture in neurological contexts and advancing precision medicine applications . Recent work (2022-2025) explores genetic correlations and cancer-related sleep disturbances. No specific awards or student advisement information appears in the provided materials.

Gabriele Grattonمشاهده پروفایل

استاد

• Brain Function
• Brain Activity
• Event-related Optical Signal
• +۷ مورد دیگر

Gabriele Gratton is a Professor in the Department of Psychology and Neuroscience Program at the University of Illinois Urbana-Champaign, and serves as a Theme Lead at the Beckman Institute for Advanced Science and Technology. With over 178 research outputs, Gratton has established a distinguished career in cognitive neuroscience with a focus on brain function, aging, and neuroimaging techniques. Gratton's research interests center around brain function and activity , particularly using Event-related Optical Signal (EROS) and other neuroimaging methods. Key areas include optical imaging , aging effects on cognition , event-related brain potentials , and cerebrovascular health . Much of their work investigates how vascular health, fitness, and aging interact to affect brain structure and cognitive performance across the lifespan. The research portfolio shows a strong trend toward multimodal neuroimaging approaches, with recent work focusing on trimodal brain imaging techniques that simultaneously investigate human brain function. There's also significant emphasis on understanding how physical activity and fitness impact cognitive aging, white matter integrity, and cerebrovascular health. Association for Psychological Science Fellow (2006) Foundation of Augmented Cognition Award (2005) President of Society for Psychophysiological Research (2009) SPR Award for Distinguished Contributions to Psychophysiology (2019) SPR Early Career Award (1997) Gratton has received extensive recognition for their work, with publications being picked up by news outlets and shared across social media platforms. Their research has been referenced by numerous scholars, with several papers accumulating significant reader attention on academic platforms like Mendeley. Much of this work is conducted through the Beckman Institute, where Gratton leads research themes focused on advanced brain imaging and cognitive neuroscience.

William F. Speierمشاهده پروفایل

دانشیار

• Medical Informatics
• Biomedical Engineering
• Neurology
• +۳ مورد دیگر

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.