Find Academic Supervisors

• Speech Perception
• Aging
• Cochlear Implants
• +2 more

Zilong Xie is an Assistant Professor in the School of Communication at Florida State University. His research focuses on the intersection of auditory, cognitive, and linguistic processes in speech recognition, with emphasis on aging, hearing loss, and cochlear implants. He employs interdisciplinary and computational methods to translate findings into clinical strategies for older adults, hearing-impaired individuals, and language learners. Ph.D. in Communication Sciences and Disorders (University of Texas at Austin, 2018) M.S. in Psychology (Peking University, 2013) B.S. in Psychology (Southwest University, 2009) Key research areas include: Speech Perception Aging Effects on Auditory Processing Cochlear Implant Efficacy Neuroplasticity in Speech Recognition Electroencephalographic Responses Recent publications highlight advancements in cortical tracking of speech under divided attention, age-related auditory changes, and machine learning applications for neurophysiological data analysis. His work bridges computational models with clinical audiology. 2023 FYAP Program Grant ($20,000) Institute for Successful Longevity Grant ($25,000) University of Kansas Medical Center Grant ($32,010) Dr. Xie's research aims to optimize speech understanding through hearing rehabilitation, exploring neural mechanisms in both native and non-native listeners with impaired hearing.

Leonardo S. BarbosaView profile

Academic

• Cognitive Neuroscience
• Information Theory
• Machine Learning
• +6 more

Leonardo S. Barbosa, Ph.D., is a Research Assistant Professor at the Fralin Biomedical Research Institute at Virginia Tech Carilion (VTC), where he is an active member of the Montague Lab. His work bridges cognitive neuroscience and computational modeling, leveraging advanced analytical frameworks to decode complex brain signals. His research interests lie in understanding human phenomenology through the lens of information theory and machine learning. He specializes in analyzing multimodal brain data, including M/EEG and fast cyclic voltammetry, to uncover the neural correlates of cognition and behavior. His interdisciplinary background in physics and cognitive neuroscience enables innovative approaches to modeling brain dynamics. Dr. Barbosa's scholarly contributions are accessible through his Google Scholar profile, indicating active engagement in publishing high-impact research. While specific articles are not listed, his methodological focus suggests strong contributions to computational and cognitive neuroscience. Ph.D. in Cognitive Neuroscience, École Normale Supérieure M.S. in Physics, University of São Paulo B.A. in Physics, University of São Paulo He has no listed scientific awards or formal advisees in the provided text. His research is conducted within the Montague Lab, a prominent interdisciplinary research group focused on neuroeconomics and neural computation.

Müjdat ÇetinView profile

Professor

• Signal Processing
• Image Processing
• Data Science
• +12 more

Müjdat Çetin is a Professor of Electrical and Computer Engineering and serves as the Robin and Tim Wentworth Director of the Goergen Institute for Data Science and Director of the New York State Center of Excellence in Data Science at the University of Rochester. He previously held faculty positions at Sabancı University and was a Research Scientist at MIT, with visiting roles at Boston University, Northeastern University, and MIT. Education: PhD in Electrical Engineering, Boston University, 2001 MS in Electrical Engineering, University of Salford, 1995 BS in Electrical Engineering, Boğaziçi University, 1993 His research lies at the intersection of signal processing, machine learning, and data science, with applications in biomedical imaging, radar, and brain-computer interfaces. He develops probabilistic and deep learning models for robust information extraction from noisy and complex data. His work emphasizes computational imaging, sparse representations, and multimodal data fusion. The recent publications reflect a strong trend toward integrating Bayesian methods and deep learning in imaging sciences, particularly in medical image reconstruction, neuroimaging analysis, and radar systems. His group actively explores transformer architectures, federated learning, and model-based deep learning for solving inverse problems in imaging. Scientific Awards and Honors: IEEE Fellow IEEE Signal Processing Society Best Paper Award IET Radar, Sonar and Navigation Premium Award Elsevier Signal Processing Best Paper Award Turkish Academy of Sciences Distinguished Young Scientist Award (GEBİP) ODTÜ Mustafa Parlar Foundation Research Incentive Award TÜBİTAK Career Award Boston University Best Engineering Research Award Professor Cetin has advised numerous PhD and Master’s students and led significant research grants in data science and imaging. He has served as a Senior Area Editor for IEEE Transactions on Image Processing and IEEE Transactions on Computational Imaging, and held editorial roles in several top journals. He has chaired major conferences including ICASSP, ICIP, and IVMSP workshops. He leads a multidisciplinary research group focused on data science and imaging, collaborating with neuroscientists and medical researchers. The team develops novel algorithms for brain-computer interfaces, medical image analysis, and remote sensing systems, often integrating machine learning with physical models of data acquisition.

Nathan WeiszView profile

Professor

• Cognitive Neuroscience
• Auditory Perception
• Neural Oscillations
• +7 more

Nathan Weisz is a Professor of Physiological Psychology in the Department of Psychology at the University of Salzburg , where he leads the Salzburg Brain Dynamics Lab and the MEG Laboratory at the Center for Cognitive Neuroscience Salzburg (CCNS). He has previously served as Associate Professor at the University of Trento’s CIMeC and as an Emmy Noether Group Leader at the University of Konstanz. . Doctorate: Department of Psychology, University of Konstanz (2001–2004) Diploma in Psychology: Catholic University of Eichstätt (1996–2001) His research focuses on understanding how the brain processes acoustic stimuli, particularly speech, using electrophysiological methods such as MEG and EEG. He investigates neural mechanisms in challenging listening environments (e.g., the 'cocktail party' problem), the role of neural oscillations in perception and memory, multisensory integration (e.g., audio-visual speech), and the development of tinnitus following hearing loss. His work has direct implications for hearing rehabilitation and cochlear implantation. The recent publications highlight advanced applications of M/EEG in decoding conscious perception across modalities, the role of alpha desynchronization in auditory working memory, and predictive neural activity in the auditory system. These reflect a strong trend in cognitive electrophysiology , predictive coding , and neural dynamics of perception . Nathan Weisz has not been mentioned as receiving specific scientific awards in the provided text. He advises graduate students in psychology and neuroscience, though specific names are not listed. His research is supported by institutional and likely external funding given his leadership roles and publication record. He leads a prominent research group focused on non-invasive brain imaging and cognitive dynamics. He leads the Salzburg Brain Dynamics Lab , which operates within the Center for Cognitive Neuroscience Salzburg (CCNS), utilizing MEG and EEG to study brain dynamics in perception and cognition.

• Cognitive Neuroscience
• Auditory Perception
• Electrophysiology
• +7 more

Dr. Thomas Hartmann is a Senior Scientist at the Centre for Cognitive Neuroscience (CCNS), Department of Psychology, Faculty of Natural and Life Sciences, University of Salzburg. His research focuses on cognitive neuroscience, auditory perception, and electrophysiological methods (M/EEG), particularly in the context of tinnitus and cochlear implants. He leads the Salzburg Brain Dynamics Lab and is a key contributor to open-source neuroscience software development. PhD in Psychology, University of Konstanz (2006–2012) Diploma in Psychology, University of Konstanz (2001–2006) His primary research interests include auditory perception, neural oscillations, neurofeedback, cognitive neuroscience, and psychophysics . He investigates how the brain processes sound, especially under conditions of hearing impairment, and develops computational tools to improve experimental rigor and reproducibility. Dr. Hartmann’s recent publications demonstrate a strong trend in multisensory integration, neural speech tracking, attention modulation, and open-source methodological development . His work often combines EEG/MEG with behavioral paradigms to uncover the neural mechanisms of perception and attention. A major contribution is the Objective Psychophysics Toolbox (o_ptb), which enhances experimental design in MATLAB by abstracting hardware complexity. Active contributor to open-source neuroscience tools Focus on reproducible and robust experimental paradigms Collaborative research with leading institutions in Europe He has co-authored numerous peer-reviewed studies in journals such as Frontiers in Psychology , NeuroImage , and Cortex , and has been involved in research on tinnitus, cochlear implants, and neural correlates of attention. Although no direct students are listed, his role as a senior scientist and publication mentor suggests advisory responsibilities. He is affiliated with the Salzburg Brain Dynamics Lab, where his team conducts cutting-edge research on brain dynamics using advanced electrophysiological techniques and computational modeling.

• Artificial Intelligence
• Machine Learning
• Human-Computer Interaction
• +16 more

Professor Damien Coyle is the Director of the Bath Institute for the Augmented Human at the University of Bath, where he leads cutting-edge research in artificial intelligence, machine learning, and human-computer interaction. His work is centered on brain-computer interfaces (BCI), neurotechnology, and cognitive computing, with applications in healthcare, rehabilitation, and intelligent systems. His research interests include Brain-Computer Interfaces , Spiking Neural Networks , Deep Learning , Emotion Recognition , Virtual and Extended Reality , and Neurofeedback Systems . He applies advanced AI techniques to decode neural signals, enabling real-time interaction between the brain and external devices. His interdisciplinary approach integrates neuroscience, computer science, and engineering. His recent publications from 2023–2024 reveal a strong focus on anomaly detection in industrial systems , EEG-fMRI data fusion for inner speech decoding , multi-level IT incident prediction using AI , and enhancing neurofeedback with immersive VR . These works demonstrate a trend toward multimodal data integration, real-time BCI applications, and AI-driven decision support in both clinical and technological domains. He has supervised numerous students and collaborators, many of whom are co-authors on his publications. His research is frequently published in top-tier IEEE and Springer conferences. He actively contributes to the advancement of neuroadaptive technologies and is involved in high-performance computing for BCI calibration. His leadership in organizing major conferences further underscores his influence in the field.

• Computational Psychiatry
• Social Neuroscience
• Systems Biology
• +5 more

Guillaume Dumas is a Professor at the Faculté de médecine of Université de Montréal, specializing in computational psychiatry and social neuroscience. He leads the Laboratoire de Psychiatrie de Précision et de Physiologie Sociale at the CHU Sainte-Justine Research Center. His work bridges neuroscience, AI, and clinical practice, focusing on social cognition, brain synchronization, and neurodevelopmental disorders. Education includes a PhD in Cognitive Neuroscience from Sorbonne Université and an HDR in Clinical Neurosciences from Université de Paris. He has held roles at Institut Pasteur and Florida Atlantic University. Research interests span computational psychiatry, interbrain connectivity, and AI-driven diagnostic tools. Notable projects include the SCALE initiative studying autism across levels and InterBrain Synchronization mechanisms. His lab develops serious games for clinical assessment and biomarker discovery. Recipient of FRQS J1/J2 awards, IVADO affiliation, and grants from CIHR and FRQNT. Supervised 7 Master’s students recently, focusing on EEG, genomic variants, and AI models.

Thomas SpragueView profile

Assistant Professor

• Neuroscience
• Cognitive Neuroscience
• Visual Cognition
• +3 more

Thomas Sprague is an Assistant Professor in the Department of Psychological & Brain Sciences at the University of California, Santa Barbara (UCSB), and a member of the UCSB Neuroscience program. He holds a BA in Cognitive Sciences from Rice University (2010) and a PhD in Neurosciences from UC San Diego (2016). His research focuses on understanding how neural systems encode and manipulate information to support goal-directed behavior, leveraging computational neuroimaging (fMRI, EEG) and model-based analysis techniques. Key research interests include visual cognition, working memory, attention, and the neural mechanisms underlying prioritization in dynamic behavioral contexts. His lab, the Perception, Cognition, and Action Lab (PCA Lab), investigates how task goals modulate neural representations of visual information across brain regions. He has received prestigious awards such as the Alfred P. Sloan Research Fellowship and NIH grants. Dr. Sprague’s work is supported by grants from the National Institutes of Health, Army Research Labs, and NVIDIA. He emphasizes open science practices, making data and code publicly available through his lab website and GitHub profile. His contributions include developing methods for multivariate analysis and inverted encoding models to decode neural representations of visual features and behavioral relevance.

• Generative Artificial Intelligence
• Visual Working Memory
• EEG-based Brain-Computer Interfaces
• +3 more

Gerard Bell is an academic researcher specializing in the intersection of generative artificial intelligence, cognitive neuroscience, and product design engineering. His work explores how AI-generated stimuli can enhance visual working memory research, the neural mechanisms underlying creative ideation in designers, and the development of intuitive gesture-based interfaces for 3D object manipulation in conceptual design. Research Interests Generative AI for stimulus creation Neuroscience of creative design and ideation EEG-based brain-computer interfaces Human-computer interaction in design contexts Gesture vocabulary for 3D modeling The 15 most recent publications reveal a consistent focus on bridging artificial intelligence with cognitive processes in design engineering. Key trends include real-time neural feedback systems (2024), structural alignment models for design concepts (2022), and systematic analysis of gesture interfaces in HCI (2019). The research spans experimental validation of AI-generated stimuli (2025) to neuroimaging studies of professional designers (2020-2019).

• Cognitive Ergonomics
• Electrophysiology
• Mobile EEG
• +4 more

Dr. Stefan Arnau is a Researcher at the Leibniz Institute for Work Research Dortmund (IfADo) since 2019, specializing in Experimental Ergonomics and Information Processing . His work focuses on cognitive fatigue, aging, and neurophysiological markers in applied settings. Key Techniques: Mobile EEG, eye blink analysis, ERP Collaborators: Prof. Edmund Wascher, Dr. Sarah Getzmann, Dr. Jonathan Reiser His research interests span cognitive ergonomics, neurophysiological correlates of mental load, and real-world applications of EEG technology. Recent publications emphasize aging-related cognitive decline, task-switching efficiency, and stress-induced neural changes. Analysis of his 15 most recent articles reveals a consistent focus on: Decoding cognitive states via EEG and eye blink activity Age-related differences in attentional resource allocation Neuroergonomic applications in driving and workplace environments Theta and alpha oscillations as markers of cognitive effort Machine learning for artifact removal in real-world EEG data Temporal perception under distraction He contributes to third-party funded projects and participates in the Dortmunder Vital-Studie for health assessment in occupational settings.

Yuling YanView profile

Professor

• Biomedical Imaging
• Machine Learning for Medical Applications
• Biosignal Analysis
• +7 more

Yuling Yan serves as Professor and Founding Chair of the Department of Bioengineering at Santa Clara University's School of Engineering, holding the Phil and Bobbie Sanfilippo Professorship. She maintains an affiliation as Consulting Faculty at Stanford University's Department of Otolaryngology. Her academic journey includes faculty positions at the University of Hawaii-Manoa (2002-2005), University of Wisconsin-Madison, and University of the Ryukyus (1997-1999), with postdoctoral training at McGill University and Max Planck Institute for Biochemistry. Professor Yan's research focuses on AI-driven medical diagnostics, specializing in machine learning applications for early disease detection including vocal pathologies, cardiac arrhythmia, skin cancer, brain aneurysms, and breast cancer. Her work integrates biomedical imaging (MRA, ultrasound, CT), biosignal analysis, and fluorescence microscopy with molecular switch probes. Current projects involve collaboration with radiologists at Santa Clara Valley Hospital to develop deep learning systems for medical image analysis. Her publication trends reveal an evolution from foundational work in vocal fold dynamics and optical probe development (2011-2015) toward AI/ML applications in medical diagnostics (2017-present), with increasing emphasis on deep neural networks for cancer detection and cardiac monitoring. Key contributions include novel methodologies for high-speed laryngeal imaging analysis and optical lock-in detection microscopy. Active research funding includes NIH Multi-Investigator Awards for high-contrast single-molecule imaging and NSF grants for laryngeal imaging tools development. She teaches graduate courses including Machine Learning for Biomedical Applications and Deep Learning for Medical Image Analysis, alongside undergraduate bioengineering fundamentals.

Christian KlaesView profile

Professor

• Neurotechnology
• Brain-Computer Interfaces
• Neuroscience
• +7 more

Prof. Dr. Christian Klaes leads the KlaesLab at the Ruhr-University Bochum 's Department of Neurotechnology , focusing on advanced neuroprostheses and assistive devices for paralysis rehabilitation. His work integrates neuroscience , machine learning , and virtual reality to decode brain signals for controlling exoskeletons and BCI systems . Key research areas: Brain-Computer Interfaces , Neural Implants , VR-based Neurorehabilitation , and AI-driven Medical Diagnostics Collaborations with Caltech , German Primate Center , and University of Madeira expand his interdisciplinary impact. Recent publications highlight advancements in EEG decoding accuracy , spike sorting algorithms , and somatosensory feedback systems , demonstrating his lab's contributions to neural signal processing and embedded AI platforms for future implants. Current projects include developing smart upper-limb exoskeletons , exploring terahertz brain imaging , and pioneering phantom touch illusions for sensory augmentation in VR environments.

Xenia KobelevaView profile

Professor

• Computational Neuroscience
• Neuropsychiatry
• Machine Learning in Neuroimaging
• +5 more

Prof. Dr. Xenia Kobeleva serves as Professor and head of the Department of Neurostimulation within the Faculty of Medicine at Ruhr University Bochum (RUB). She concurrently leads the Computational Neurology Research Group, directing interdisciplinary efforts at the intersection of computational science and clinical neuropsychiatry. Her research program focuses on developing and applying computational methodologies—including machine learning, artificial intelligence, and whole-brain network modeling—to analyze clinical and neuroimaging data. This dual approach of data-driven and theory-based modeling aims to decode pathophysiological mechanisms in neuropsychiatric disorders, with direct translational goals for neurostimulation-based diagnostics and therapies. Key application areas include Alzheimer's disease, Lewy body dementia, amyotrophic lateral sclerosis (ALS), and movement disorders. Analysis of her 2016-2024 publications reveals a consistent trajectory in computational neurology, characterized by innovative network-level analyses of neurodegenerative conditions. Her work demonstrates particular expertise in integrating multimodal neuroimaging with advanced modeling techniques to identify disease-specific biomarkers and network alterations, bridging computational neuroscience with clinical practice. Prof. Kobeleva directs both the Department of Neurostimulation and Computational Neurology Research Group at RUB, fostering collaboration between neurologists, psychiatrists, computer scientists, and engineers. These teams operate from room MB 6/189 at Universitätsstraße 150 in Bochum, developing computational tools and neurostimulation protocols for neuropsychiatric disorders.

Edward A. VesselView profile

Assistant Professor

• Cognitive Neuroscience
• Neuroaesthetics
• Computational Neuroscience
• +4 more

Edward A. Vessel serves as the Eugene Surowitz Assistant Professor in the Department of Psychology at The City College of New York (CCNY), with an additional appointment at the Colin Powell School for Civic and Global Leadership. His office is located in NAC 7/217c, contactable via 212-650-7150 or evessel@ccny.cuny.edu. His research spans: Cognitive Neuroscience Neuroaesthetics Computational Neuroscience Brain Imaging Vision Empirical Aesthetics Creativity Building At the Visual Neuroscience and Aesthetics Lab (VISNAlab), Vessel investigates neural mechanisms of aesthetic perception using fMRI and EEG. His work examines how the brain processes artistic and natural visual stimuli, integrating computational models to decode emotional and cognitive responses to beauty. This interdisciplinary approach bridges psychology, neuroscience, and art theory to understand creativity's biological foundations. Vessel actively contributes to neuroaesthetics through empirical studies on visual cognition, though specific grants or collaborative projects aren't detailed in the source text. His lab maintains a public research profile at visnalab.ccny.cuny.edu.

• Neuroscience
• Machine Learning
• Control Theory
• +4 more

Dr. LIU Quanying is an Associate Professor in the Department of Biomedical Engineering at the Southern University of Science and Technology (SUSTech), where she has been a faculty member since September 2019. She serves as the Principal Investigator of the Neural Computing and Control Laboratory (NCC lab) and is a doctoral supervisor. Prior to joining SUSTech, she earned her PhD in Biomedical Engineering from ETH Zurich and conducted postdoctoral research at Caltech. Education: PhD in Biomedical Engineering, ETH Zurich (2013-2017) Master in Computer Science, Lanzhou University (2010-2013) Bachelor in Electrical Engineering, Lanzhou University (2006-2010) Research Interests: Dr. Liu’s research integrates neuroscience, machine learning, and control theory. Her work focuses on multi-modal neural signal processing (EEG, sEEG, fMRI, DTI), explainable AI for neuroscience, and optimization techniques for neuromodulation (tES, TMS). She has developed high-density EEG source localization algorithms and data-driven brain network modeling frameworks, aiming to enhance precision in neural stimulation and control. Scientific Awards: The New Brain 30 (2023) AAIC Travel Award (2019) Estes Stars Award (2018) 深圳市孔雀人才计划C类 Laboratory and Team: As the PI of the NCC lab, Dr. Liu leads a team focused on machine learning algorithms, neurocomputational modeling, and neurofeedback control. The lab actively recruits graduate students, postdocs, and visiting researchers, emphasizing interdisciplinary collaboration in neuroscience and AI.