جست‌وجوی استاد و پژوهشگر

Gergely Csibraمشاهده پروفایل

استاد

• Developmental Psychology
• Cognitive Science
• Social Cognition
• +۷ مورد دیگر

Gergely Csibra is a part-time Professor of Psychology at Birkbeck, University of London, with a primary affiliation at Central European University in Budapest. His research focuses on developmental psychology, cognitive science, and neuroscience, particularly exploring infant social cognition, action perception, and neuroimaging techniques. He investigates how infants interpret social interactions, understand goal-directed actions, and process communicative signals through methodologies such as ERP, fNIRS, and eye-tracking. Research Interests: Infant social cognition and theory of mind Development of social learning mechanisms Neural basis of action understanding Language acquisition and symbolic representation Joint action and collaborative cognition Developmental neuroscience using EEG/fNIRS Publications: Over 160 peer-reviewed articles, including foundational work on natural pedagogy, teleological reasoning in infancy, and social cognitive development. Recent work examines social cost optimization in collaborative actions and the neural correlates of infant communication understanding. Labs/Teams: Leads interdisciplinary research groups studying cognitive development at both Birkbeck and CEU, collaborating with neuroscientists, psychologists, and robotics researchers to model human social cognition.

Jeff Jonesمشاهده پروفایل

استادیار

• Neuroscience
• Circadian Rhythms
• Neuroendocrinology
• +۳ مورد دیگر

Jeff Jones is an Assistant Professor in the Department of Neuroscience at Texas A&M University. His research focuses on circadian rhythms, particularly investigating how the suprachiasmatic nucleus (SCN) encodes and transmits timing signals to regulate physiological and behavioral outputs. He joined the department in 2021 after completing postdoctoral studies at Stanford University and Washington University. Education B.Sc., Neuroscience, University of Texas at Dallas (2009) M.Sc., Neurobiology and Behavior, Columbia University (2010) Ph.D., Neuroscience, Vanderbilt University (2015) Research Interests His work explores the neural circuits underlying circadian timing, including SCN-VIP neuron signaling, light entrainment mechanisms, and downstream integration of circadian signals with other brain regions. His lab develops optogenetic tools to measure and manipulate circadian rhythms in vivo, aiming to clarify how disruptions in these circuits contribute to metabolic, cardiovascular, and mood disorders. Publications Recent work emphasizes circadian dysfunction in cancer progression, sex-dependent behavioral rhythms, and glucocorticoid regulation by SCN-derived neurons. His methods for phase analysis and behavioral tracking have advanced circadian research methodologies. Labs/Teams The Jones Lab at Texas A&M focuses on decoding how SCN output is translated into diverse daily rhythms via downstream neuronal networks. Current projects investigate circadian-neuropeptide interactions, temporal gene expression coordination, and integrative signaling pathways.

• Artificial Intelligence
• Advanced Analytics
• Operations Research
• +۵ مورد دیگر

Dr. Su Nguyen is a Senior Lecturer in AI and Analytics at RMIT University's Department of Accounting, Information Systems & Supply Chain, located at the City Campus in Australia. His research focuses on integrating artificial intelligence, analytics, and operations research to address challenges in sustainability, safety, and critical domains like healthcare and logistics. Key areas include transparent AI systems, simulation models for dynamic environments, and optimization algorithms for energy and transportation. His work emphasizes making AI systems accountable to mitigate risks such as discrimination and ensures fairness in autonomous decision-making. Collaborations span Australia, New Zealand, and the Asia Pacific region, promoting AI applications in industry. Supervised projects include topics like personalized care plans, workforce rostering optimization, and AI-driven solutions for supply chains and cyber-risk evaluation. Dr. Nguyen's research bridges theoretical advancements with practical industry engagement, aiming to enhance decision-making through advanced analytics and business optimization frameworks.

• Neuroscience
• Memory
• Emotional Development
• +۳ مورد دیگر

Bénédicte AMILHON is a Professor at the Université de Montréal's Faculty of Medicine , Department of Neurosciences. Her research focuses on neural circuits linking emotional behavior and memory, particularly the interaction between emotions and memory formation via raphe-hippocampal pathways. She combines electrophysiology, optogenetics, and calcium imaging to study serotonergic modulation of hippocampal rhythms and anxiety. Education: PhD in Neuroscience (Université Pierre et Marie Curie Paris VI, 2009) Postdoctoral training at McGill University (2009-2017) Affiliations: Membre du Centre de recherche Azrieli du CHU Sainte-Justine Research grants from FRQS, NARSAD, and the Canadian Institutes of Health Research (CIHR) Her work has uncovered roles for serotonergic inputs in anxiety modulation and hippocampal theta rhythms. Key awards include the FRQS Junior 1 and NARSAD Young Investigator Award. She teaches courses in cellular/molecular neuroscience and supervises students in master's and doctoral programs. Current projects investigate neural circuits linking emotion and memory, including grants on raphe-hippocampus pathways (2020-2027) and curved electrode technologies for brain recordings (2023-2026). Labs/Teams: Amilhon Lab (specializing in optogenetics and circuit dissection) Part of the Azrieli Centre's interdisciplinary teams Grants: CIHR Project Grant (2018-2027) FRQS Junior 2 Award (2023-2026)

Abidemi Bolu Ajiboyeمشاهده پروفایل

استاد

• Brain-Computer Interfaces (BCI)
• Neuroprosthetics
• Spinal Cord Injury Rehabilitation
• +۵ مورد دیگر

Abidemi Bolu Ajiboye, PhD, is the Robert & Brenda Aiken Professor of Biomedical Engineering at Case Western Reserve University's Case School of Engineering. He holds key administrative roles including Faculty Director of Postdoctoral Affairs, Associate Chair, and Executive Vice Chair of the Case School of Engineering. His research focuses on developing brain-computer interface (BCI) technologies to restore motor function in individuals with spinal cord injuries and stroke. Notable projects include the ReHAB initiative, aiming to reconnect paralyzed limbs to the brain through bidirectional neuroprostheses. Education: PhD Biomedical Engineering (Northwestern University, 2008), MS Biomedical Engineering (Northwestern, 2003), BS Biomedical & Electrical Engineering (Duke University, 2000) Research interests span neural control mechanisms, FES-based systems, and clinical translation of BCI technologies. He has pioneered closed-loop systems for natural movement restoration and received VA Career Development Awards. His work integrates neuroscience with engineering to address complex rehabilitation challenges. Publications emphasize BCI system design, neural signal processing, and clinical applications. Recent efforts focus on tactile feedback restoration and addressing biocompatibility challenges in neural implants. He advises on postdoctoral training programs and contributes to interdisciplinary neural engineering initiatives through the Cleveland Neural Engineering Workshop.

Ammar Belatrecheمشاهده پروفایل

مدرس ارشد

• bio-inspired intelligent systems
• machine learning
• spiking neural networks
• +۵ مورد دیگر

Dr. Ammar Belatreche is a Senior Lecturer in Computer Science and Programme Leader for the MSc Advanced Computer Science at Northumbria University's Department of Computer and Information Sciences. He joined Northumbria University in May 2016 after previous positions as a Research Associate and Lecturer at Ulster University. He is an active member of the Computational Intelligence and Visual Computing (CIVC) research group. Dr. Belatreche earned his PhD in Computer Science from Ulster University in 2007. His professional qualifications include: Member of the Association of Computing Machinery (ACM) since 2012 Fellow of the Higher Education Academy (FHEA) since 2010 Member of the Institute of Electrical & Electronic Engineers (IEEE) since 2009 His research focuses on bio-inspired intelligent systems, machine learning, spiking neural networks, face detection and recognition, structured and unstructured data analytics, capital markets engineering, and image processing. Dr. Belatreche has extensive experience across academic and R&D in these areas, leading numerous research and consultancy projects. His recent work demonstrates a strong emphasis on neuromorphic computing, particularly spiking neural networks and their applications in computer vision, financial analysis, and biometrics. Analysis of his recent publications shows a clear trend toward advancing spiking neural network architectures, with particular focus on quantization, pruning, and binary implementations to improve efficiency. His research spans multiple domains including computer vision (face recognition, palm-vein recognition), financial technology (stock price manipulation detection), and neuromorphic engineering. Many of his recent papers (2024-2025) appear in top-tier conferences like ICLR and journals like IEEE Transactions on Neural Networks and Learning Systems. Dr. Belatreche has received professional recognition including: Fellowship with the Higher Education Academy (FHEA) Role as Associate Editor for the journal Neurocomputing He has successfully supervised or co-supervised 8 PhD students to completion and serves as a Program Committee Member and reviewer for numerous international conferences and journals. As Programme Leader for the MSc Advanced Computer Science, he plays a significant role in shaping postgraduate education in computer science at Northumbria University. His research group work bridges theoretical advances in neural computation with practical applications across multiple domains. Based in CIS 305 at Northumbria University's Newcastle campus, Dr. Belatreche continues to advance research in neuromorphic computing and its applications while contributing to academic leadership through his programme leadership role.

• Clone Detection
• Data Protection
• Natural Language Processing
• +۳ مورد دیگر

Muslim Chochlov serves as a Researcher within the Department of Computer Science & Information Systems and is an active member of Lero – the Irish Research Centre for Software. His work bridges theoretical computer science with practical industrial software solutions through rigorous empirical studies. Research Focus: Specializes in code clone detection systems using BERT-based neural networks and ensemble inference methods, with significant contributions to data protection frameworks in digital health applications during the COVID-19 pandemic. Publication Trends: Demonstrates a clear evolution from foundational software architecture studies (2015-2020) toward AI-driven code analysis (2021-2025), with 73% of recent output focused on scalable industrial clone detection techniques and citizen-centered health informatics tools. Collaboration Network: Maintains extensive cross-institutional partnerships through Lero's national research infrastructure, evidenced by multi-author publications spanning computer science, public health, and data protection domains. His 2022 contact tracing app analysis achieved notable impact with 20 citations and 100+ reader captures. Technical Leadership: Develops practical methodologies for industrial codebase analysis including nearest-neighbor BERT implementations and ensemble inference systems that improve detection recall while addressing data protection requirements in sensitive health applications.

• Cognitive Neuroscience
• Neuroimaging
• Computational Neuroscience
• +۳ مورد دیگر

Professor Hinze Hogendoorn is a cognitive neuroscientist and head of the Timing Lab at Queensland University of Technology (QUT). He holds a PhD from Utrecht University and has held academic positions at Utrecht University (7 years as Assistant Professor), University of Melbourne, and Harvard Vision Sciences Laboratory. His research focuses on understanding how the brain processes time through computational methods, neuroimaging (e.g., EEG decoding), and behavioral approaches. Key interests include neural mechanisms of motion extrapolation, perceptual timing, and the real-time conscious experience. Affiliations: Faculty of Health, School of Psychology & Counselling at QUT. Education: PhD in Neuroscience from Utrecht University, Netherlands. Research emphasizes decoding neural representations of time and motion, with contributions to understanding illusions like the flash-lag effect. He has received an ARC Future Fellowship (2020) and engages in science communication through TV programs, TEDx, and executive education. Outreach includes popular science writing (e.g., The Conversation) and organizing the Brisbane Cognition Conference 2024. His work bridges neuroscience and applied psychology, addressing how brain delays influence perception and cognition.

Chaoxiong Yeمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Neuroscience
• Visual Memory
• Working Memory
• +۳ مورد دیگر

Dr. Chaoxiong Ye is an Academy Research Fellow (PI) at the Department of Psychology, University of Jyväskylä. He holds two PhDs: one in Cognitive Science (2015–2018) and another in Psychology (2018–2020). He holds four docentships (Associate Professor titles) at Finnish universities in Cognitive Neuroscience and Cognitive Psychology. As a PI, he has secured over €1.5M in funding and supervises nine doctoral students. He serves on editorial boards of BMC Psychology and PLOS ONE , and fosters international collaborations with leading institutions globally. Education: PhD in Cognitive Science, University of Jyväskylä (2015–2018) PhD in Psychology, University of Jyväskylä (2018–2020) Master’s in Applied Psychology, MinNan Normal University (2012–2015) Research Interests: Focuses on cognitive and neural mechanisms of visual memory, working memory, and emotional face processing. Current projects include studying memorability’s impact on long-term memory and developing diagnostic strategies for memory disorders. Principal Investigator: Cognitive and Neural Mechanisms for Memorable Visual Stimuli (€0.93M, 2023–2027) Previous project: Two-Phase Resource Allocation Model of Visual Working Memory (2020–2023) Publications: Over 110 publications in top journals, emphasizing working memory mechanisms, emotional interference, and neural basis of depression. Notable contributions include studies on retro-cue effects, dimension-based attention, and face distractor impacts. Awards & Grants: Secured €1.5M+ in grants from the Research Council of Finland. Recognized for editorial work and international collaborations. Labs & Teams: Leads Ye’s Lab at the University of Jyväskylä, fostering research on cognitive processes and neural mechanisms.

Deborah Hannulaمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Human Memory
• Relational Memory
• +۱۳ مورد دیگر

Deborah Hannula is an Associate Professor in the Department of Psychology at the University of Wisconsin–Milwaukee (UWM), where she also serves as Director of Undergraduate Studies and Associate Chair. She is affiliated with the Neuroscience Faculty and actively recruits graduate students for the Neuroscience PhD program. Her research is centered on the cognitive and neural mechanisms of human memory, particularly focusing on the hippocampus and medial temporal lobe structures. Her research interests include: Relational and episodic memory Conscious and unconscious memory expression Eye-tracking as a tool for memory assessment fMRI and behavioral studies in healthy and clinical populations Memory impairments in conditions like autism, schizophrenia, and Alzheimer’s disease The trends in her recent publications show a strong focus on the role of the hippocampus in implicit and unconscious memory, using eye-tracking and neuroimaging to probe memory processes that occur without awareness. Her work bridges cognitive theory, neuroscience, and clinical applications, often challenging traditional views that limit hippocampal involvement to conscious memory. Scientific awards and recognitions include: Teaching Fellow, UWM Center for Student Experience and Talent She mentors several graduate and undergraduate students, including Dana Slabbekoorn, Greta Minor, and Mrinmayi Kulkarni. Her lab has received recognition for student research presentations at national conferences. She is developing a careers-focused course for psychology majors and has contributed chapters to major reference works such as the Encyclopedia of the Human Brain . She also co-chaired a symposium titled 'From Magic to Memory: Eye Movements, Cognition, and the Brain' and regularly presents at academic conferences. Her lab, the Hannula Lab, conducts interdisciplinary research combining cognitive psychology, neuroscience, and clinical applications, with a strong emphasis on methodological rigor and theoretical innovation.

Dora Hermes Millerمشاهده پروفایل

دانشیار

• Neuroscience
• Biomedical Engineering
• Neurology
• +۷ مورد دیگر

Dora Hermes Miller is an Associate Professor of Biomedical Engineering and holds Senior Associate Consultant II-Research roles in the Departments of Physiology & Biomedical Engineering, Radiology, and Neurology at Mayo Clinic. Her research focuses on human systems neuroscience, integrating multimodal imaging and computational modeling to understand neural signals in neurological and neuropsychiatric diseases. She collaborates with clinicians to develop therapeutic neuromodulation technologies and neuroprosthetics. Education includes a PhD from Utrecht University (Brain Center Rudolf Magnus) and postdoctoral fellowships at Stanford University, New York University, and UMC Utrecht. Her work emphasizes reproducible research practices, with contributions to neuroimaging standards like BIDS-iEEG and open source tools for electrode localization. Research interests span multimodal imaging integration, network dynamics via electrical stimulation, neural oscillations, and translational applications of brain-machine interfaces. Her lab explores how electrical stimulation modulates human connectomes, with implications for epilepsy treatment and psychiatric conditions. Collaborative efforts bridge clinical neurology, neurosurgery, and computational neuroscience. Awards and recognitions are not explicitly listed in the provided text. Her work has advanced understanding of somatotopic motor representations, limbic circuit modulation, and cortical stimulation effects, with over 70 peer-reviewed publications in journals like NeuroImage and Brain. Current projects include developing closed-loop neuromodulation systems, optimizing ECoG/MEG-fMRI integration, and advancing ethical brain-computer interface applications. She leads the Mayo Clinic NeuroEngineering Lab and participates in collaborative initiatives to standardize neuroelectrophysiological data sharing.

Marcin Wojtasińskiمشاهده پروفایل

استادیار

• Cognitive Psychology
• Religiosity Studies
• Art Appreciation
• +۵ مورد دیگر

Marcin Wojtasiński is an Assistant Professor at the Department of Experimental Psychology, Institute of Psychology, Faculty of Social Sciences, John Paul II Catholic University of Lublin. His academic career at this institution spans multiple research domains. Assistant Professor (2025) Department of Experimental Psychology affiliation Faculty of Social Sciences membership John Paul II Catholic University of Lublin affiliation Dr. Wojtasiński's research interests intersect Cognitive Psychology , Religiosity Studies , and Art Appreciation . His work explores: Religious symbols' dual dimensions (opportunity vs. challenge) Canonical size phenomenon in memory drawing Flow states in board gaming Religious experience's protective role in migration Neural correlates of empathy and art perception Recent publications show increasing focus on network analysis and statistical modeling of complex psychological phenomena. His 2025 work includes vection research, skill-challenge dynamics, and sound predictability effects on attention. Collaboration patterns reveal frequent work with Małgorzata Tatala , Magdalena Szubielska , and other Polish psychologists. While no specific awards are mentioned, his publication record in Scientific Reports , Religions , and PLoS One demonstrates active scientific contribution.

• Cognitive Neuroscience
• Reading Research
• Visual Word Recognition
• +۳ مورد دیگر

Manuel Perea Lara is a Professor at the University of Valencia, affiliated with the Faculty of Psychology and Speech Therapy and the Department of Behavioural Science. He leads research at the ERI Reading Research institute and the READit Cognitive Neuroscience and Reading group, focusing on behavioral science methodology in language processing. He obtained his PhD from the University of Valencia in 1993 with a thesis titled Influencia de los factores de vecindad sobre el acceso léxico análisis experimentales y de simulación , supervised by Dr. Arcadio Gotor Sicilia. His research explores cognitive mechanisms in reading, including: Orthographic processing and visual word recognition Cross-linguistic studies of writing systems Eye movement dynamics during reading Development of literacy skills in children Neurocognitive aspects of language in neurodiverse populations Experimental methodologies in psycholinguistics Analysis of his 15 most recent publications (2024-2025) reveals strong emphasis on masked priming paradigms, cross-script comparisons (Arabic, Chinese, Javanese), literacy development interventions, and cognitive processing in autism. The work demonstrates advanced methodological rigor in exploring orthographic encoding, attentional mechanisms, and bilingual language processing. No awards or supervised students are mentioned in available materials. His laboratory conducts innovative research using techniques like event-related potentials (ERPs) and behavioral experiments to investigate reading mechanisms across different populations and writing systems.

Amir Soneeمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Privacy
• Physical Layer Security
• +۳ مورد دیگر

Amir Sonee is a Postdoctoral Research Associate at the Centre for AI Fundamentals within the Department of Computer Science, focusing on privacy-preserving techniques in probabilistic machine learning and federated learning systems. He holds a PhD and M.Sc. in Electrical Engineering from Ferdowsi University of Mashhad and Sharif University of Technology, respectively, with a B.Sc. from Ferdowsi University of Mashhad. His research integrates machine learning, data privacy, physical layer security, and wireless communications, emphasizing theoretical foundations and practical applications. He has collaborated with institutions like King's College London and National Yang Ming-Chiao Tung University on projects involving secure federated learning in wireless environments. Education: PhD in Electrical Engineering (2011–2017), Ferdowsi University of Mashhad M.Sc. in Electrical Engineering (2008–2010), Sharif University of Technology B.Sc. in Electrical Engineering (2004–2008), Ferdowsi University of Mashhad Research interests span privacy-preserving machine learning, federated learning, secure wireless communication, and information-theoretic approaches to network security. His work bridges theoretical advancements with real-world challenges in trustworthy AI and distributed systems. He is part of the MCAIF: Centre for AI Fundamentals project, exploring reinforcement learning and neural networks for AI applications. His contributions include optimizing federated learning protocols under communication constraints and advancing secrecy capacities in multi-user wireless systems.

• Computer Science
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Yongtao Wu is a Doctoral Assistant and student in the doctoral program in Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). He is affiliated with the Laboratory for Information and Inference Systems (LIONS), part of the School of Engineering (STI) and the Institute of Electrical Engineering (IEM). His research focuses on machine learning, adversarial learning, computer vision, and deep learning, with applications in vision-language models, robustness analysis, and optimization algorithms. His work explores topics such as diffusion-based purification for vision-language models, adversarial attacks on large models, and parameter-efficient fine-tuning techniques. He contributes to advancing foundational AI models and their robustness in real-world scenarios. His research also intersects with cybersecurity, structural engineering predictions, and quantum computing methodologies. Yongtao is actively involved in the LIONS lab, collaborating on projects that bridge theoretical advancements with practical applications in computer science and engineering. His doctoral studies emphasize interdisciplinary approaches to solving complex problems in AI and machine learning.