جستجوی استاد راهنمای دانشگاهی

Julie Haasمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Electrophysiology
• +۳ مورد دیگر

Julie Haas is a Professor at Lehigh University investigating neural attention mechanisms through electrical synapses in the thalamic reticular nucleus (TRN). Her research integrates electrophysiology, optogenetics, and computational modeling to decode how inhibitory circuits filter sensory information, with implications for attention disorders. Her educational journey includes a B.A. in Music and Mathematics from Indiana University, a Ph.D. in Biomedical Engineering from Boston University, and postdoctoral training at Harvard University and UC San Diego, supplemented by Computational Neuroscience studies at the Marine Biological Laboratory. Research focuses on electrical synapse plasticity as the core mechanism for attentional selection. The Haas lab examines how dopamine, GABA receptors, and amygdala inputs modulate TRN circuitry using in vitro brain slices and optogenetic tools. Key discoveries include activity-dependent long-term potentiation at electrical synapses and their role in sensory gating, bridging molecular dynamics to circuit-level functions through innovative computational models. Publication analysis reveals a decade-long trajectory from foundational electrical synapse characterization (2012-2015) to neuromodulatory mechanisms (2016-2021) and recent dopamine receptor investigations (2022-2024). This evolution demonstrates increasing complexity in understanding how electrical synapses integrate neuromodulatory signals for attentional control. Funding from NIH, NSF, Whitehall Foundation, and Brain and Behavior Foundation supports her lab's work. She teaches advanced courses including Synapses, Plasticity and Learning (Bios 385/415) and Neurophysiology Laboratory (Bios 278), training next-generation neuroscientists in cutting-edge techniques. The Haas lab operates within Lehigh's neuroscience facilities with specialized electrophysiology rigs, optogenetic systems, and computational resources for multi-scale analysis of thalamic circuitry, currently exploring electrical synapse dysfunction in neurodevelopmental disorders.

Daniel A. Abramsمشاهده پروفایل

دانشیار بالینی

• Autism Spectrum Disorders
• Cognitive Neuroscience
• Brain Imaging
• +۵ مورد دیگر

Dr. Daniel A. Abrams is a Clinical Associate Professor in the Department of Psychiatry and Behavioral Sciences at Stanford University. His research focuses on the neurobiological basis of social communication impairments in children with autism spectrum disorders (ASD), particularly how atypical reward attribution to social stimuli like voices and faces impacts development. He employs techniques such as functional MRI, psychophysics, and cognitive assessments to identify neural markers of social and memory deficits in ASD. Dr. Abrams received his Ph.D. in Auditory Cognitive Neuroscience from Northwestern University (2008) and a B.F.A. from the University of Arizona (1994). His work has been funded by NIH, NARSAD, and the National Organization for Hearing Research Foundation. He leads the Speech and Social Neuroscience Lab at Stanford Cognitive and Systems Neuroscience Laboratory. Scientific awards include the K01 Research Scientist Development Award (NIH/NIMH, 2014-2017), NARSAD grant (2019-2021), and a 2017 CHRI Pilot Early Career Award. His recent publications highlight neural circuitry in voice processing, memory impairments linked to hyperconnectivity, and efficacy of clinic-home-school collaborative interventions for autistic adolescents. Key research trends: Autism neurodevelopment, reward/salience circuit dysfunction, social communication biomarkers, voice prosody decoding, and memory plasticity. Dr. Abrams’ full description below details his academic trajectory, grants, student collaborations, and clinical trial leadership.

Kaare Mikkelsenمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• Sleep Science
• Wearable EEG Systems
• +۳ مورد دیگر

Kaare Mikkelsen is an Associate Professor in the Department of Electrical and Computer Engineering at the Faculty of Technical Sciences, Aarhus University, specializing in biomedical engineering with a focus on ear-EEG technology for sleep and auditory neuroscience applications. His research pioneers non-invasive monitoring systems using ear-centered EEG sensors, developing machine learning frameworks for automatic sleep staging and auditory attention decoding. Key contributions include personalized sleep scoring algorithms that adapt to individual physiological variations and deep learning models for decoding brain responses to natural speech, enabling applications in hearing assistance and brain-computer interfaces. Recent publications (2022-2025) demonstrate a cohesive research trajectory centered on overcoming real-world challenges in wearable EEG: improving long-term reliability through electrode configuration studies, developing standardized data pipelines, and validating ear-EEG against clinical polysomnography. His work bridges engineering innovation with clinical sleep medicine, emphasizing at-home deployment and user-specific adaptation. Dr. Mikkelsen leads significant research projects including: Event based attention detection (2021-2024) Ear-EEG sleep monitoring (2015-present) His work receives funding from Danish research councils and involves collaborations with clinical partners for validation studies in naturalistic settings. As part of Aarhus University's Biomedical Engineering group, he utilizes advanced laboratories for sensor development and signal processing, contributing to the department's strategic focus on healthcare technology innovation.

• Auditory Attention
• EEG Decoding
• Cochlear Implants
• +۵ مورد دیگر

Tom Francart is a Professor at the Department of Neuroscience, Faculty of Medicine, KU Leuven. He leads research in auditory attention, EEG decoding, and speech processing, with affiliations to iSi Health, LBI (KU Leuven Brain Institute), and Leuven.AI. His research focuses on: Neural decoding of auditory attention using EEG Objective measures for aphasia and hearing impairment Brain-computer interfaces for hearing aids Audiovisual integration in speech perception Machine learning applications in auditory neuroscience Recent publications analyze neural tracking of speech in aphasia, transformer-based EEG decoding, and audiovisual context integration. He supervises projects on cochlear implants, attention decoding algorithms, and post-stroke recovery.

James Magnusonمشاهده پروفایل

استاد

• Neurobiology of Language
• Spoken Word Recognition
• Computational Modeling
• +۵ مورد دیگر

James Magnuson is a Professor in the Department of Psychological Sciences at the University of Connecticut, where he leads the Computational Cognitive Neuroscience of Language Lab (CCNLL). He serves as Principal Investigator for multiple NSF-funded initiatives, including the Neurobiology of Language graduate training program and the Science of Learning and Art of Communication research training program, and co-directs the Cognitive Science Shared Electrophysiology Resource Lab (CSSERL). His research investigates the neurobiological and psychological foundations of language processing, development, and disorders using computational modeling, eye-tracking, and neuroimaging techniques. Key interests include neural-network models of spoken word recognition (e.g., EARSHOT and TISK frameworks), real-time language processing across the lifespan, prosody in Specific Language Impairment, phonological competition dynamics, and cross-modal integration of linguistic information. Educational background includes a Ph.D. in Brain & Cognitive Sciences from the University of Rochester (2001). Current teaching responsibilities include undergraduate Cognitive Psychology (PSYC 2501) and graduate seminars on language plasticity and time-course methodologies (EEG, eye-tracking). Magnuson actively advises doctoral students in projects spanning computational modeling of speech recognition, neural bases of language processing, and individual differences in language acquisition. He has secured significant NSF funding for interdisciplinary training programs bridging cognitive science, neuroscience, and genetics.

• Neuroscience
• Consciousness Studies
• Neuroimaging
• +۴ مورد دیگر

Aya Khalaf is an Associate Research Scientist at the Yale School of Medicine, affiliated with the Blumenfeld Lab and the Janeway Society. Her research focuses on understanding neural mechanisms of consciousness, brain-computer interfaces, and machine learning applications in healthcare. She collaborates with leading institutions and researchers globally on studies involving EEG, fTCD, and neuroimaging techniques. Key research interests include auditory and visual perception networks, impaired consciousness in epilepsy, and hybrid BCI systems. Her work bridges cognitive neuroscience with engineering, aiming to translate findings into clinical tools. Notable collaborations include projects with Hal Blumenfeld, Dennis Spencer, and international teams testing consciousness theories. Publications highlight advancements in neural activity analysis, BCI calibration optimization, and multimodal signal processing. Her contributions span theoretical neuroscience frameworks and applied biomedical engineering solutions.

Slim Essidمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۵ مورد دیگر

Slim Essid is a Full Professor at Télécom Paris and coordinator of the Audio Data Analysis and Signal Processing (ADASP) group. He holds a PhD and HDR from Université Pierre et Marie Curie (UPMC). His research focuses on machine learning, artificial intelligence, and signal processing applied to temporal data analysis, including multiview learning, representation learning, and structured prediction. Applications span music content analysis (MIR), multimodal perception (e.g., EEG data analysis), and human behavior analysis. He has advised 15 PhD students and collaborated on over 14 post-doctoral projects. Education: PhD in Signal Processing, Université Pierre et Marie Curie (2005) Habilitation (HDR), Université Pierre et Marie Curie (2015) M.Sc. in Digital Communication Systems, Télécom ParisTech (2002) Engineer Degree, École Nationale d’Ingénieurs de Tunis (2001) Research interests emphasize multimodal learning, self-supervised representation learning, and audio-visual fusion. Key projects include sound-prompted segmentation, zero-shot audio captioning, and EEG-based auditory attention decoding. Over 150 peer-reviewed publications exist across conferences like NeurIPS, ICML, and journals like IEEE Transactions. Active in reviewing for top-tier venues and advising French/EU research projects. Labs/Teams: Member of the Signal, Statistics and Learning (S2A) research team and the Information Processing and Communication Laboratory (LTCI).

• Cognitive Neuroscience
• Neuroimaging
• Emotion Processing
• +۴ مورد دیگر

Svetlana Shinkareva is a Professor in the Department of Psychology at the University of South Carolina, affiliated with the McCausland College of Arts and Sciences. She holds a Ph.D. in quantitative psychology from the University of Illinois at Urbana-Champaign and completed post-doctoral training at Carnegie Mellon University. Her work focuses on understanding the neural mechanisms underlying affective processing and conceptual representation across sensory modalities. Dr. Shinkareva's research investigates how hedonic valence (pleasantness/unpleasantness) is represented in the brain through audiovisual stimuli, as well as how abstract concepts are encoded neurally. Her studies use neuroimaging techniques like fMRI to decode brain activity patterns associated with emotion perception, cross-modal integration, and semantic knowledge. Key areas include evaluating valence processing in naturalistic settings, exploring autism-related neural connectivity differences, and advancing multivariate pattern analysis methods for cognitive neuroscience. Her recent publications (2021–2025) reveal a focus on audiovisual integration, affective valence dynamics, and translational applications of neuroimaging. Notable trends include cross-modal studies of emotional responses, computational modeling of brain representations, and methodological advancements in FMRI decoding. Her work bridges basic cognitive science with clinical applications, such as predictive modeling for stroke patients and understanding neurodevelopmental disorders. While no scientific awards are explicitly listed, her prolific publication record demonstrates sustained impact in cognitive neuroscience. She advises no listed graduate students but collaborates on interdisciplinary projects involving psychology, computer science, and clinical research. Her lab contributes to the SC Psycholinguistic Metabase and explores translational neuroimaging applications in health and behavior.

• Cognitive Neuroscience
• Interpersonal Coordination
• Joint Action
• +۴ مورد دیگر

Janeen Loehr is a Professor in Psychology and Health Studies at the University of Saskatchewan's College of Arts and Science. Her research focuses on the cognitive and neural mechanisms underlying interpersonal coordination, joint action, and ensemble music performance, employing EEG techniques to study action monitoring and self-other distinctions. She holds a B.A. (Honors) and Ph.D. in Psychology. Her work investigates how individuals synchronize actions in contexts like musical ensembles or conversations, emphasizing sensory attenuation, neural correlates of feedback processing, and shared agency. Key themes include temporal coordination, motor control, and the social neuroscience of collaborative behavior. Loehr has taught courses such as Advanced Human Neuropsychology and Neuroscience Seminars, contributing to both undergraduate and graduate education. Her publications span topics from EEG hyperscanning methodologies to therapeutic applications of music in dementia care, reflecting her interdisciplinary approach to understanding human interaction. Ongoing research explores the neural basis of joint agency and the development of assistive technologies for neurological conditions.

Ramanathan Subramanianمشاهده پروفایل

دانشیار

• Human-centered computing
• AI systems
• Non-verbal behavioral analysis
• +۷ مورد دیگر

Ramanathan Subramanian is an Associate Professor in the Department of Information Technology & Systems, AI and Robotics at the University of Canberra. His past affiliations include IIT Ropar and UIUC, Singapore. He has received notable awards including the IEEE Transactions on Affective Computing Best Paper Award (2019) and Multimedia Rising Star nomination (2015). His research focuses on Human-centered computing, AI systems leveraging non-verbal behavioral cues, and applications in health analytics and virtual reality. He actively supervises PhD and Master’s students in areas like Human-Computer Interaction, Data Science, and VR for healthy aging. Research interests: Human perception modeling, intelligent interfaces, health-focused VR, and applied machine learning Key projects: Driver sentiment prediction, distraction awareness campaigns, and robotics for waste management His work contributes to UN SDGs related to health and well-being, particularly through technologies improving aging care and mental health monitoring. Recent projects include developing explainable AI for emotion inference and multimodal data fusion systems. Awards: IEEE Best Paper Award, Multimedia Rising Star Grants: Lead or Co-Investigator on three major grants totaling over AUD 4M Labs/Teams: Collaborates extensively with the University's robotics and health tech groups, contributing to interdisciplinary projects in AI ethics and medical signal processing.

Fan Gang Zengمشاهده پروفایل

استاد

• Cochlear Implants
• Auditory Neuroscience
• Speech Perception
• +۶ مورد دیگر

Fan Gang Zeng is a Professor in the Department of Otolaryngology at the University of California, Irvine (UCI), and Director of the Center for Hearing Research. His academic roles span multiple disciplines, including joint appointments in Anatomy & Neurobiology, Biomedical Engineering (BME), Cognitive Sciences, and SOM-Administration. His research focuses on auditory neuroscience, cochlear implants, tinnitus, and speech perception, with a particular emphasis on improving hearing restoration technologies and understanding neural mechanisms of hearing loss. Affiliations: Director, Center for Hearing Research Member, UCI Institute for Memory Impairments and Neurological Disorders (UCI MIND) NIH-funded investigator through grants like R01 DC015587 Research Interests: Professor Zeng’s work bridges engineering and neuroscience, addressing challenges in cochlear implant optimization, tinnitus biomarkers, and speech intelligibility. Key areas include: Noninvasive ear stimulation for tinnitus suppression Cochlear implant artifact attenuation techniques Cognitive correlates of auditory brainstem responses Speech processing algorithms for hearing aids and implants Grants & Funding: His research is supported by NIH grants and industry partnerships, including collaborations with companies like Axonics, Nurotron, and Syntiant. He holds patents related to cochlear implant design and neural stimulation technologies. Labs & Teams: The Center for Hearing Research at UCI integrates multidisciplinary teams to advance audiological innovations, with a focus on translating basic science into clinical applications.

• Cognitive Neuroscience
• Working Memory
• Temporal Integration
• +۴ مورد دیگر

Elkan Akyürek is a Professor of Cognitive Neuroscience and Chair of the Department of Experimental Psychology at the University of Groningen. He leads a research group focused on understanding the neural mechanisms underlying working memory, temporal integration, and attention. His work employs EEG, behavioral experiments, and computational modeling to explore how sensory information is integrated, maintained, and processed in the brain. He has been instrumental in advancing methodologies like impulse perturbation and concealed information testing, with applications in cognitive neuroscience and forensic psychology. Research interests center on cognitive neuroscience, particularly in the domains of working memory maintenance, temporal processing, and the neural correlates of conscious awareness. He investigates how attention modulates perception and memory, with a focus on adaptive mechanisms in visual and auditory systems. His lab also explores the effects of nutritional interventions (e.g., cocoa flavanols) on cognitive performance. Recent research highlights include studies on concealed information detection using EEG and eye-tracking, adaptive forgetting dynamics in working memory, and the neural basis of temporal integration. His work has been published in high-impact journals like *Nature Neuroscience*, *Psychological Review*, and *NeuroImage*, and he has secured grants from the Dutch Science Foundation (NWO) and the Police and Science program. Dr. Akyürek has supervised numerous PhD students and postdocs, fostering collaborations with institutions globally. He is actively involved in science outreach, including educational initiatives to demystify cognitive neuroscience for secondary students. His lab’s interdisciplinary approach integrates psychology, neuroscience, and computational modeling to address fundamental questions about human cognition.

Konrad Paul Kordingمشاهده پروفایل

استاد

• Neuroscience
• Machine Learning
• Computational Neuroscience
• +۶ مورد دیگر

Konrad Paul Kording is the Nathan Francis Mossell University Professor at the University of Pennsylvania's School of Engineering and Applied Science, holding primary appointments in Bioengineering and Neuroscience, and a secondary appointment in Computer and Information Science. His research focuses on computational neuroscience, machine learning, and their applications to understanding neural systems and human behavior. He leads a research group located in Room 404 Richards and maintains an active research website and personal academic page. His work explores topics such as neural decoding, causal inference, and the alignment of artificial neural networks with biological systems. He has pioneered methods for analyzing neural activity in macaques, developing large-scale facial analysis resources like PrimateFace. His interdisciplinary approach integrates computer vision, medical informatics, and robotics to address challenges in neurology and developmental disorders. Recent research highlights include advancing techniques for early prediction of cerebral palsy using motion tracking and automated segmentation of synchrotron-scanned fossils. His contributions to causal discovery methodologies and neural network interpretability have been influential in both neuroscience and machine learning communities.

Amy Finnمشاهده پروفایل

دانشیار

• Developmental Cognitive Neuroscience
• Learning and Memory Systems
• Child Development
• +۳ مورد دیگر

Amy Finn is an Associate Professor and Director of the Finn Learning and Neural Development Lab (LAND Lab) at the University of Toronto. Her research investigates how brain and cognitive development shape learning outcomes in children, focusing on memory systems, neural plasticity, and environmental influences. The LAND Lab explores developmental cognitive neuroscience, emphasizing age-related learning patterns, critical periods, and brain-environment interactions. Current research areas include implicit memory development, attentional modulation of learning, socioeconomic impacts on neural systems, and computational models of cognition. Notable publications span topics like attention-memory dynamics, scene perception in children, and socioeconomic status effects on hippocampal development. Collaborations involve institutions such as MIT, Emory University, and international labs. Developmental Cognitive Neuroscience Learning and Memory Systems Neural Plasticity Environmental Influences on Learning

• Wireless Sensor Networks
• Brain-Computer Interfaces (BCI)
• EEG Signal Processing
• +۴ مورد دیگر

Dr. Alexander Bertrand is a Professor at the Faculty of Engineering Sciences , KU Leuven, heading the Dynamic Systems, Signal Processing and Data Analysis (STADIUS) division. He leads the Department of Electrical Engineering (ESAT) and contributes to Leuven.AI institute, with expertise spanning wireless sensor networks, brain-computer interfaces (BCI), and biomedical signal processing. Research Focus : Wireless acoustic/EEG sensor networks, distributed signal enhancement, adaptive filtering, neural decoding of auditory/visual attention, and AI-driven time series analysis. Key Projects : EEG-Linx platform for modular brain recordings (2025-2027) Calibration-free BCI systems (2025-2029) AI quality assessment for time series data (2024-2028) Wireless EEG patches for hearing technology (2024) Publications (2023-2025) demonstrate leadership in distributed signal processing , auditory attention BCI , and scalable sensor architectures , with applications in education, healthcare, and wearable tech. Teaching includes courses on digital signal processing, biomedical data analysis, and medical technology design.