جستجوی استاد راهنمای دانشگاهی

Dr. Stefon van Noordt is an Assistant Professor in the Department of Psychology at Mount Saint Vincent University. His research focuses on understanding brain-based markers of attention control and performance monitoring, particularly in developmental and individual differences contexts. He integrates behavioral, imaging, and computational tools to study neural dynamics in normative and pathological states like anxiety. His work is supported by NSERC, CIHR, and other institutions. He holds postdoctoral training from McGill University and Yale University. Education includes a Ph.D. in Psychology (2016) from Brock University, with postdoctoral fellowships at the Montreal Neurological Institute and Yale Child Study Centre. His research emphasizes EEG analysis, autism risk prediction, and medial frontal theta oscillations. Research interests span cognitive neuroscience, developmental psychology, and neuroimaging, with a focus on EEG methodologies and their application to autism spectrum disorder. Recent work includes studies on infant EEG connectivity, social exclusion effects, and neural responses to peer feedback. Dr. van Noordt collaborates on projects like the EEG-IP platform for international infant data integration and has published extensively on EEG biomarkers and neurodevelopmental trajectories. He mentors students in Honours thesis projects and postdoctoral training.

• Cognitive Neuroscience
• Developmental Psychology
• Neuroimaging
• +۳ مورد دیگر

Patrick Bennett is a Professor in the Department of Psychology, Neuroscience & Behaviour at McMaster University , focusing on visual perception, aging, and cognitive neuroscience. His research explores how aging affects visual processing mechanisms, face identification, and perceptual learning. Key Research Areas : Aging effects on vision, Face processing, EEG dynamics, Biological motion perception, Perceptual learning Recent Publications : Analyzed visual crowding in older adults (2025), Investigated 3D target detection mechanisms (2024), Studied face mask impacts on emotion recognition (2023) Teaching : Instructs advanced statistics and research design courses for psychology students. His work combines computational methods with behavioral experiments to understand neural processing changes across the lifespan.

• Visual Perception
• Aging and Cognitive Neuroscience
• Face Identification
• +۳ مورد دیگر

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

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

Rosario B. Jaime-Lara is an Assistant Professor at the University of California, Los Angeles (UCLA) School of Nursing. She holds advanced degrees including a PhD in Nursing from the University of Pennsylvania, MSN from Columbia University, and dual BS degrees in Nursing and Biological Sciences from University of Pennsylvania and UC Davis. Her research focuses on neurophysiological mechanisms of eating behavior, nutritional disparities in Mexican-American communities, and chemosensory science. Key themes include obesity research, sensory neuroscience, and translational clinical studies. She employs rodent models and clinical research methodologies to explore taste/smell physiology and metabolic health. Jaime-Lara has received over 17 honors including the 2022 Hommer Memorial Award and multiple diversity fellowships. Her work spans interdisciplinary collaborations in genomics, microbiome research, and health IT interventions for chronic disease management. Recent publications emphasize olfactory dysfunction in coronaviruses, fat taste mediators, and metabolic profiling. Her research bridges basic science and clinical practice, with particular attention to underserved populations.

• Nutrition
• Neurophysiology
• Obesity Research
• +۵ مورد دیگر

Syed Ahmar Shah is a Senior Research Fellow (Associate Professor) and the Director of Innovation at the Usher Institute within the College of Medicine and Veterinary Medicine at the University of Edinburgh. He holds a tenured academic position and leads the DIME group (Data-driven Innovation in MEdicine). His work bridges biomedical engineering, data science, and clinical medicine, with a focus on improving healthcare through technological innovation. Dr. Shah completed his educational journey with a BEng in Electronics Engineering from GIK Institute of Engineering Sciences and Technology in Pakistan, followed by an MSc and DPhil (PhD) in Biomedical Engineering and Biomedical Signal Processing and Machine Learning, respectively, from the University of Oxford. His academic credentials reflect his interdisciplinary expertise spanning engineering, data science, and medicine. His research interests center around the application of advanced data analytics to healthcare challenges. Specifically, he focuses on signal processing for time-series analysis and filtering, machine learning for classification, regression, and clustering tasks, and the development of digital health systems for chronic disease management. His work particularly targets chronic respiratory conditions like COPD and asthma, where he applies data mining techniques to electronic health records to identify patterns and develop predictive models. Dr. Shah's publication portfolio includes over 60 peer-reviewed articles in prestigious journals such as The Lancet, Brain, BMJ Open, Thorax, IEEE Transactions, JMIR, and JACI. His recent work demonstrates a strong trajectory in applying artificial intelligence to predict asthma attacks, analyze long COVID outcomes, and develop tools for personalized COPD care, particularly for women. His research often involves large-scale data analysis from national healthcare databases across the UK, Brazil, and Scotland, enabling cross-country comparisons of disease patterns and healthcare system responses. Florence Nightingale Award for Excellence in Healthcare Data Analytics (2023) As an active supervisor, Dr. Shah is open to PhD supervision enquiries and has contributed to training the next generation of researchers at the intersection of data science and healthcare. His DIME research group serves as a hub for innovative projects that combine engineering approaches with clinical medicine to address pressing healthcare challenges. Dr. Shah also engages with industry through data science consulting, offering expertise in developing intelligent algorithms for businesses with large datasets, particularly in healthcare but extending to other domains as well.

• Signal Processing
• Machine Learning
• Biomedical Engineering
• +۵ مورد دیگر

Neil Lin is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of California, Los Angeles (UCLA), with a joint appointment in Bioengineering. His research focuses on developing 3D-printed tissues that replicate the structure, mechanics, and functionality of human organs, with applications in drug screening and regenerative medicine. Lin leads the Lin Lab - Living Soft Material Engineering , advancing soft and living material engineering through interdisciplinary approaches. Lin holds a Ph.D. and M.S. in Engineering from Cornell University (2016 and 2013) and a B.S. in Engineering from National Tsing Hua University, Taiwan (2008). His work spans biomaterials mechanics, quantitative imaging, and AI-driven biological analysis. Research interests include the structure and dynamics of soft biomaterials, image-based force measurements, and quantitative imaging techniques for material characterization. Recent work explores cell morphology regulation, AI applications in microscopy, and mechanical heterogeneity in live tissues. Lin’s publications highlight advancements in cell trapping, AI-based image translation, and prostate cancer metabolism modeling. He has received prestigious awards, including the Young Investigator Award (2022), Hellman Fellowship, and NIH Maximizing Investigators' Research Award (2022). His lab integrates engineering and biology to address challenges in tissue engineering, with ongoing projects on 3D kidney models, drug response assays, and AI-driven phenotyping of senescent cells.

• Biomedical Engineering
• Tissue Engineering
• 3D Printing
• +۵ مورد دیگر

Professor David Bannerman is a leading academic in Behavioral Neuroscience at the University of Oxford's Department of Psychiatry. He heads the Behavioural Neuroscience Unit, focusing on neural mechanisms underlying anxiety, learning, and memory. His research integrates neurophysiological, genetic, and pharmacological approaches to study neuropsychiatric disorders such as Alzheimer’s, schizophrenia, and sleep disturbances. Education: BSc (Hons) and PhD qualifications are listed. His research interests span synaptic plasticity, neurodegenerative processes, and the neurobiology of fear/anxiety. Notable areas include the role of orexin pathways in anxiety, tau protein effects on spatial cognition, and the impact of psychedelics like 5-MeO-DMT on neural states. He also investigates sleep spindles’ role in brain state regulation and the consequences of SSRI discontinuation on serotonergic systems. Recent work emphasizes Alzheimer’s disease mechanisms (e.g., amyloid β’s synaptic effects), schizophrenia models (NMDA receptor dysfunction), and the neuroplasticity linked to working memory training. His lab employs cutting-edge techniques, including optogenetics and in vivo neural recordings, to dissect complex behaviors and circuit-level dysfunction. His publications highlight interdisciplinary approaches, bridging basic neuroscience with translational research. Key themes include: synaptic dysfunction in neurodegeneration, environmental influences on neurobehavioral outcomes (e.g., magnetic fields), and the neurobiological basis of psychiatric symptomatology.

• Behavioral Neuroscience
• Neurobiology of Anxiety
• Synaptic Plasticity
• +۴ مورد دیگر

Nilanjan Sarkar is the Vice Dean and Senior Associate Dean for Faculty Affairs at Vanderbilt University's School of Engineering, holding the David K. Wilson Professorship in Engineering. He is a Professor in Mechanical Engineering, Computer Engineering, and Computer Science. His research focuses on intelligent systems for human interaction, including robotics, virtual/augmented reality, and assistive technologies for neurodevelopmental disorders and aging populations. Education: PhD (Mechanical Engineering, University of Pennsylvania), ME (Indian Institute of Science), BE (Indian Institute of Engineering Science and Technology, Shibpur). Research interests span human-robot interaction, sensor fusion, and rehabilitation engineering. His lab develops systems for autism intervention, stroke rehabilitation, and elderly engagement through socially assistive robotics and VR/AR. Notable projects include robot-mediated therapy for children and AR telepresence systems for long-term care facilities. Labs/Teams: Robotics and Autonomous Systems Laboratory. Key contributions include CoMove, RASSLE, and the Career Interview Readiness in VR platform. His work emphasizes participatory design with end-users for ethical and inclusive technology.

• Human-Robot Interaction
• Human-Computer Interaction
• Robotics
• +۶ مورد دیگر

Associate Professor Yan Wong is a faculty member at Monash University's Department of Electrical and Computer Systems Engineering and Monash Biomedicine Discovery Institute. His research focuses on neural prosthetics, brain-machine interfaces, and restoring sensory functions through cutting-edge neural recording and stimulation techniques. He leads the Neurobionics Laboratory, advancing technologies like the Gennaris vision system, a wireless cortical implant for restoring vision in the blind. Yan holds a PhD in Biomedical Engineering from UNSW and has conducted postdoctoral research at New York University and the University of Melbourne. His work bridges engineering and medicine, addressing critical healthcare challenges such as quadriplegia, epilepsy, and hearing loss. He actively supervises students and contributes to teaching biomedical engineering courses at Monash, fostering the next generation of researchers. Key research interests include visual cortex stimulation, cochlear implants, spike-LFP interactions in movement planning, and endovascular neural interfaces. He has led or collaborated on multiple ARC-funded projects, including studies on neural prosthetic efficacy, chronic pain modulation, and visual attention mechanisms. His interdisciplinary approach integrates neuroscience, biomedical engineering, and clinical translation, aiming to deliver impactful medical technologies. Yan’s contributions have advanced neural prosthetics toward commercialization, with a focus on improving healthcare outcomes through innovative biomedical solutions.

• Neural Prosthetics
• Brain-Machine Interfaces
• Biomedical Engineering
• +۴ مورد دیگر

Diogo Almeida is an Associate Professor of Psychology and Global Network Associate Professor of Psychology at New York University Abu Dhabi. His research focuses on neurolinguistics and language processing, employing methodologies such as MEG/EEG, fMRI, and cross-linguistic studies. He holds an MSc in Cognitive Science from the Ecole des Hautes Etudes en Sciences Sociales and an PhD in Linguistics from the University of Maryland, College Park. Prior to NYU Abu Dhabi, he held positions at the University of California, Irvine, and Michigan State University. Almeida’s research explores perceptual processes underlying language understanding, including syntactic structure processing, agreement phenomena, and the neural bases of lexical access. His work integrates behavioral experiments with neuroimaging techniques to investigate how linguistic structures are represented in the brain. Key areas include sentence processing selectivity in Broca’s area, cross-linguistic differences in agreement encoding, and the role of phonological and morphological features in real-time language comprehension. His publications span topics like masked priming effects, sociolinguistic influences on speech perception, and the reliability of experimental syntax methods. He leads the Language, Mind and Brain research group at NYU Abu Dhabi, focusing on interdisciplinary approaches to language cognition. Almeida has contributed to debates on empirical rigor in syntax, advocating for robust experimental designs and large-scale data collection to validate theoretical claims.

• Neurolinguistics
• Language Processing
• Cognitive Science
• +۳ مورد دیگر

Karin Pfennig is a Professor in the Department of Biology at the University of North Carolina at Chapel Hill. Her research focuses on understanding how behavior drives biodiversity, particularly through the evolution of mating behavior and its role in speciation, hybridization, and species distributions. She uses spadefoot toads as model organisms to study adaptive hybridization, behavioral plasticity, and the ecological consequences of species interactions. Her work combines field studies, laboratory experiments, and genetic analyses. Key research themes include the evolution of mate choice plasticity, the role of hybridization in range expansions, and the mechanisms underlying reproductive character displacement. Pfennig’s lab is located in Wilson Hall, and she actively mentors graduate students such as Catherine Chen, Patrick Kelly, and Gina Calabrese. Notable awards include winning the NSF’s 2026 Idea Machine competition for her proposal on community-driven carbon capture solutions. Her findings have been published in prestigious journals like *Science*, *Proceedings of the Royal Society B*, and *Journal of Heredity*. Pfennig’s research bridges ecology and evolution, addressing critical questions about biodiversity maintenance and adaptation in human-impacted environments. Her lab emphasizes interdisciplinary approaches, integrating behavioral, ecological, and genetic perspectives to unravel complex evolutionary processes.

• Behavioral Ecology
• Evolutionary Biology
• Speciation
• +۵ مورد دیگر

Jonas Beskow is a Professor and Head of Division at the Division of Speech, Music and Hearing at KTH Royal Institute of Technology. His research focuses on multimodal interaction, speech synthesis, robotics, and human-robot interaction. He leads the Learning style variation in nonverbal behaviour for social robots and agents project as part of Digital Futures, a cross-disciplinary research center. His work involves developing social robots like the Furhat head and advancing technologies for gesture synthesis, audio-driven motion, and adaptive intelligent systems. He holds roles as Co-PI for the Advanced Adaptive Intelligent Systems (AAIS) and Adaptive Intelligent Homes (AIH) projects. His research spans robotics, computer graphics, and clinical applications such as dementia detection through multimodal patient behavior analysis. Beskow also contributes to educational initiatives, supervising courses in computer science and engineering, including degree projects in machine learning and systems engineering. Publications highlight innovations in gesture generation, speech-driven animation, and socially-aware robotics. Collaborations with institutions like Stockholm University and RISE Research Institutes drive interdisciplinary solutions. His work bridges artificial intelligence, human-computer interaction, and assistive technologies, emphasizing ethical and societal impacts of emerging digital systems.

• Multimodal Interaction
• Speech Synthesis
• Robotics
• +۳ مورد دیگر

Dr. Svetlana Yanushkevich is a Professor in the Department of Electrical and Software Engineering at the Schulich School of Engineering, University of Calgary. She is also a Full Member of the Hotchkiss Brain Institute and the Mathison Centre for Mental Health Research and Education. Her research focuses on biometric technologies, decision support systems, biomedical applications, and computational intelligence. She leads the Biometric Technologies Laboratory, developing strategies for risk assessment in biometric systems and healthcare monitoring through machine reasoning and signal processing. Education : BSc/MSc in Electrical Engineering (1989), State University of Informatics and Radioelectronics, Minsk PhD in Electrical Engineering (1992), same institution Dr. Habilitated in Technical Sciences (1999), Warsaw University of Technology Research Interests : Dr. Yanushkevich’s work spans biometric system design (e.g., gait analysis, facial attributes), decision support via probabilistic models (Bayesian networks, causal inference), biomedical applications (stroke rehabilitation, wearable sensors), and computational intelligence for data science. She emphasizes fairness, bias mitigation, and trustworthiness in AI systems, particularly in healthcare and accessibility contexts. Recent Research Trends : Her recent publications address causal modeling for accessibility barriers, UAV operator cognitive workload, and medical device optimization in radiation therapy. She explores AI ethics, stress contagion in human-robot teams, and cross-spectral biometric systems. Awards & Recognition : 2024 FEIC Fellow (Engineering Institute of Canada) 2019 Research Excellence Award (Schulich School of Engineering) 2001 Senior IEEE Membership Advising & Grants : She coordinates courses like ENCM 509 (Biometric Systems Design) and ENEL 610 (Biometric Technologies). Her research is supported by grants focusing on healthcare AI, accessibility technologies, and computational epidemiology. Labs & Collaborations : Her Biometric Technologies Lab collaborates with institutions like Hokkaido University and the IEEE Computational Intelligence Society. Projects include wearable health monitoring, decision support platforms, and AI-driven epidemiological modeling.

• Biometric Technologies
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Arthur Lim is a Professor of the Practice Education in the Mathematics Department at the University of Notre Dame. His primary affiliations include teaching roles in calculus and dynamical systems courses, alongside research in algebraic structures and pedagogical innovation. Ph.D., University of Utah (2001) M.S., National University of Singapore (1994) His research explores combinatorial matrix analysis applied to Lie algebras and pedagogical strategies emphasizing historical context and critical thinking in mathematics education. Recent work bridges operations research with market dynamics and telecommunications policy. Selected publications span 1999–2019, addressing topics from orbital integral approximations to net neutrality debates. Teaching innovations focus on integrating historical narratives with technical rigor in undergraduate instruction.

• Combinatorial Matrix Analysis
• Lie Algebras
• Mathematics Pedagogy
• +۳ مورد دیگر

Alan Lindsay is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. He holds a Ph.D. from the University of British Columbia (2010) and a B.S. from the University of Edinburgh (2005). His research focuses on computational and analytical methods for partial differential equations (PDEs) modeling physical and biological systems, including Micro-Electromechanical Systems (MEMS), mathematical ecology, imaging, and inverse problems. His email is a.lindsay@nd.edu, and he is based in Crowley Hall. Education: Ph.D., Applied Mathematics, University of British Columbia, 2010 B.S., Mathematics, University of Edinburgh, 2005 Research Interests: Applied Partial Differential Equations Numerical Methods for PDEs Mathematical Biology and Biophysics Scientific Computing and Simulation MEMS and Micro-Electromechanical Systems Mathematical Modeling of Biological Processes Recent Research Trends: Lindsay’s work emphasizes computational techniques like boundary integral methods, kinetic Monte Carlo simulations, and bifurcation analysis to study diffusion processes, first passage times, and pattern formation in biological and physical systems. His studies bridge theoretical analysis and practical applications, such as optimizing T cell antigen recognition and modeling moth mating strategies. Grants & Advising: While no students are listed, his research is supported by grants in computational mathematics and biological modeling. His work often involves interdisciplinary collaborations with biologists and engineers. Labs/Teams: His research is conducted within the ACMS department, leveraging Notre Dame’s computational infrastructure.

• Applied Partial Differential Equations
• Computational Mathematics
• Mathematical and Computational Biology
• +۳ مورد دیگر