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Nicola SavillView profile

Senior Lecturer

• Cognitive Psychology
• Neurobiology of Language
• Developmental Dyslexia
• +3 more

Dr Nicola Savill is a Senior Lecturer in Psychology at York St John University and co-lead of the REF Unit of Assessment. She holds a PhD from Bangor University (2011) and MSc/BSc degrees in Psychological Research and Human Psychology. Her research focuses on cognitive and neurobiological mechanisms underlying language processing, particularly reading, verbal short-term memory, and phonological binding. She employs experimental psycholinguistics, EEG, and neuroimaging techniques, collaborating with the University of York on projects like phonological binding in verbal memory. Education includes a PhD in Psychology from Bangor University, an MSc in Psychological Research from the University of Wales Bangor, and a BSc in Human Psychology from Aston University. Her work addresses semantic and phonological influences on language processing, with studies on developmental dyslexia and bilingualism. Research interests span phonological function, semantic retrieval, and neuroimaging. She is a Fellow of the Higher Education Academy and a reviewer for journals including Journal of Cognitive Neuroscience and PLoS ONE . She teaches modules on biological bases of behavior, advanced topics in brain and behavior, and research methods. Her lab collaborations focus on cognitive neuroscience and EEG studies. She welcomes PhD candidates interested in experimental psychology and EEG-based research on language and memory.

• Computational Neuroscience
• Cognitive Neuroscience
• Mental Health Research
• +3 more

Sammi Chekroud is a Postdoctoral Researcher in the Computational Cognitive Neuroscience (CCN) Lab led by Jill O'Reilly at the University of Oxford, and a Tutor for Statistics in the undergraduate psychology program. They hold affiliations with the Oxford Centre for Human Brain Activity and the Wellcome Centre for Integrative Neuroimaging. Chekroud completed their DPhil in 2022 under Kia Nobre and Nils Kolling, following a BA in Experimental Psychology from Oxford (2015). Their research focuses on computational modeling of cognitive processes, particularly how humans evaluate uncertainty in internal representations shaped by attention and sensory input. They combine psychophysics, EEG/MEG, and eye-tracking to study working memory, perceptual decision-making, and attentional dynamics. Additionally, they investigate large-scale datasets to improve mental health interventions, notably collaborating on studies about exercise and mental health. Their work bridges computational neuroscience, clinical psychology, and public health. Key educational milestones include a BA (2015) and DPhil (2022) from the University of Oxford, with three years as a Research Assistant in the Brain and Cognition lab. Their research spans computational cognitive neuroscience, mental health epidemiology, and translational research. Notable contributions include analyzing 1.2 million individuals to demonstrate exercise’s mental health benefits and elucidating how attention modulates working memory confidence. Research interests emphasize uncertainty evaluation in cognitive processes, with studies on gaze behavior reflecting attentional focus and neural mechanisms underlying working memory-guided actions. Their articles explore topics ranging from motor selection during memory tasks to the neural correlates of prioritization in memory systems. Chekroud actively contributes to interdisciplinary collaborations, integrating computational methods with clinical data to address real-world mental health challenges.

• Non-invasive brain stimulation
• Transcranial electrical stimulation
• Electroencephalography (EEG)
• +3 more

Jake Toth is a Postdoctoral Research Fellow at the University of Oxford's Institute of Biomedical Engineering, specializing in non-invasive brain stimulation and brain-computer interfaces. He holds a PhD from the University of Sheffield, where his research focused on transcranial electrical stimulation (TES) and electroencephalography (EEG). His current work at Oxford explores transcranial ultrasound stimulation for modulating human brain activity as part of the BUBBL research group. Education: BEng (Bachelor of Engineering) MSc (Master of Science) PhD in Biomedical Engineering His research interests include developing standardized TES protocols, investigating vigilant attention mechanisms, and advancing brain-machine interface technologies. He has contributed to projects involving EEG-based navigation systems and classification algorithms for cognitive tasks. Recent work emphasizes translational applications of neurostimulation techniques in clinical and robotic contexts. Key Contributions: Pioneering EEG feedback systems for robot navigation Advancing Bayesian learning methods in neural data analysis Exploring ethical and technical challenges in non-invasive brain stimulation His work bridges clinical neuroscience, biomedical engineering, and robotics, with applications in personalized cognitive enhancement and human-machine collaboration.

Simon RigoulotView profile

Associate Professor

• Affective Neuroscience
• Neuroscience of Emotion
• Auditory and Visual Perception
• +3 more

Simon Rigoulot is an Associate Professor in the Department of Psychology at Université de Montréal, affiliated with the Faculté des arts et des sciences. He holds dual appointments with the Centre de Recherche sur le Cerveau, la Langue et la Musique (CRBLM) and the BRAMS International Laboratory for Brain, Music and Sound Research. His work focuses on affective neuroscience, particularly the neural mechanisms underlying emotional perception in auditory and visual modalities. He employs EEG, MEG, and psychophysiological techniques to study how cultural background and individual differences influence emotional processing. Recent projects explore links between musical expertise and emotional competence, as well as the impact of regional accents on emotional prosody recognition. Education: PhD in Cognitive Sciences from Université de Lille (2008) Postdoctoral work at McGill University and under Dr. Simone Dalla Bella Director of the COGNAC lab (COGnition, Neuroscience, Affect et Comportement) Research interests include: multimodal emotion integration, neural correlates of vocal and musical emotions, and the development of psychopathologies. He has supervised multiple master's theses on topics like neurostimulation-music mood effects and cultural influences on prosody perception. Key grants include leadership in the CRBLM strategic research group (2017-2023) and collaborations with Quebec Bio-imaging Network projects. His lab actively seeks participants for studies exploring social neuroscience and emotion processing.

Uri Tzvi EdenView profile

Professor

• Neural Data Analysis
• Statistical Modeling
• Point Process Theory
• +5 more

Uri Tzvi Eden is a Professor and Associate Chair of the Department of Mathematics and Statistics at Boston University's College of Arts and Sciences. He is a member of the Probability and Statistics research group, where he focuses on developing mathematical and statistical methods to analyze neural spiking activity. His work integrates model identification, statistical inference, signal processing, and stochastic estimation with point process observation models specifically tailored for neural systems. Professor Eden's research spans two main categories: methodological development of statistical frameworks for relating neural activity to biological and behavioral signals, and practical applications of these methods to real neural systems. His work enables dynamic modeling of individual neuron spiking properties, characterization of neural ensemble representations, and real-time signal reconstruction. His research interests center on neural data analysis, statistical modeling of point processes, machine learning applications in neuroscience, signal processing techniques for neural recordings, and mathematical neuroscience. Eden has made significant contributions to developing estimation algorithms, goodness-of-fit analyses, and mathematical theory applicable to neural spiking systems. Professor Eden's publication record shows a consistent focus on neural data analysis with recent work expanding into sleep spindle dynamics, epilepsy research, Parkinson's disease applications, and advanced machine learning approaches for neural decoding. His research demonstrates a trajectory from foundational methodological work toward increasingly sophisticated applications in clinical neuroscience. Professor Eden earned his Ph.D. in Medical Engineering/Medical Physics from Harvard/MIT Division of Health Sciences and Technology in 2005 with a thesis on point process filters in neural spiking model analysis. He has co-authored the textbook 'Analysis of Neural Data' published by Springer in 2014.

• Computational Neuroscience
• Neurodegenerative Diseases
• Alzheimer's Disease
• +3 more

David Aquilué Llorens is a Research Fellow in the Computational Neuroscience Group at Pompeu Fabra University, supported by the prestigious INPhINIT Fellowship from Fundació la Caixa. His work focuses on developing whole-brain computational models to understand misfolded protein propagation in Alzheimer's Disease and improve early diagnostic interventions for neurodegenerative disorders. Education : Bachelor's in Engineering Physics Master’s in Computational Neuroscience and Neuroengineering, Université Paris-Saclay Research Focus : Integrating Computational Neuroscience with Machine Learning and Deep Learning, his research analyzes EEG data and simulates neural dynamics to investigate how pathological proteins spread through brain networks. This work bridges physics-based modeling and clinical neuroscience to decode neurodegeneration mechanisms in Alzheimer's Disease. Awards : INPhINIT Fellowship (Fundació la Caixa) Professional Experience : Previously served as a Research Engineer at Starlab Barcelona, implementing ML/DL algorithms for EEG analysis in neurodegenerative disease contexts, strengthening his translational research capabilities. Research Environment : Collaborates within UPF's Computational Neuroscience Group on interdisciplinary projects merging artificial intelligence, neuroscience, and clinical applications to advance understanding of brain disorders.

Ian StevensonView profile

Associate Professor

• Computational Neuroscience
• Statistical Analysis of Neural Data
• Neural Dynamics
• +5 more

Ian Stevenson is an Associate Professor in the Department of Psychological Sciences at the University of Connecticut. He earned his Ph.D. from Northwestern University in 2011 and leads the UConn Statistical Neuroscience Lab. His affiliations include the Behavioral Neuroscience Program, Department of Biomedical Engineering, and CT Institute for the Brain and Cognitive Sciences. Stevenson's research bridges computational neuroscience and neural data analysis with two primary focus areas: Developing computational tools to model neural dynamics, interactions, and adaptation mechanisms Linking neural activity patterns to perception, behavior, and learning processes His recent publications demonstrate strong trends in Bayesian neural decoding methods and synaptic plasticity modeling, with applications spanning auditory processing, neural connectivity inference, and educational neuroscience. Computational approaches dominate his research, particularly for analyzing high-dimensional neural datasets. He maintains active collaborations through shared data repositories and tools for neural data analysis. His lab is currently seeking undergraduate and graduate students interested in computational neuroscience research.

Chen ZengView profile

Professor

• Theoretical and Computational Biophysics
• Biomolecule Modeling and Drug Design
• Deep Learning Algorithms
• +3 more

Chen Zeng is a Professor of Physics at George Washington University, affiliated with the Department of Physics. His research integrates theoretical and computational biophysics with machine learning to address challenges in biomolecular modeling, drug design, and systems biology. He leads the Zeng Lab, focusing on topics such as single-cell metabolomics, autoimmune genomics, and neural circuit decoding in the mouse motor cortex. Zeng holds a BS in Physics from the University of Science and Technology of China (1987) and a PhD in Physics from Cornell University (1994). His academic contributions span advanced computational methods for RNA-protein interactions, deep learning applications in biomedicine, and phase transition modeling in complex systems. He teaches core courses including Machine Learning I/II, Statistical Mechanics, and Biophysics. Recent research emphasizes trace metabolite detection, biomarker identification in cancer, and neurophysiological studies of social behavior and motor learning. Zeng’s work bridges physics and biology, with a focus on interdisciplinary solutions for disease mechanisms and technological applications. His lab collaborates on projects ranging from HIV transcription inhibition to chimpanzee tool-use analysis, reflecting his broad scientific curiosity and methodological rigor.

Jan KujalaView profile

Professor

• Neuroscience
• Cognitive Neuroscience
• Neuroimaging
• +8 more

Professor Jan Kujala is a full professor of brain development at the University of Jyväskylä, Department of Psychology, within the Faculty of Education and Psychology. He earned his Ph.D. from Helsinki University of Technology in 2008 and joined the University of Jyväskylä in 2019. His research focuses on neural mechanisms underlying human cognition, behavior, and development, leveraging advanced neuroimaging techniques like magnetoencephalography (MEG) and computational analysis. His research interests include understanding how neural activity and connectivity form adaptive systems, particularly in healthy individuals across the lifespan and clinical populations. Key areas include oscillatory activity, cortico-cortical connectivity, and links between neural processes and behavior. He leads the Neural Processes and Connectivity group and is part of the Centre of Excellence for Learning Dynamics and Intervention Research InterLearn. Recent work highlights studies on breathing’s neural modulation, abstract concept processing via MEG, and test-retest reliability of language-related brain connectivity. His 2024 and 2025 publications explore dog-human interaction physiology, empathy’s role in neurophysiological decoding, and GABAergic inhibition in visual memory. He also investigates sensorimotor beta oscillations’ heritability and the neural underpinnings of reading and executive functions in aging. His research emphasizes methodological advancements in MEG and EEG, particularly in connectivity analysis and artifact removal. Collaborative projects include interdisciplinary studies on human-animal interactions, brain plasticity, and clinical applications like stroke and Parkinson’s disease.

• Computer Science
• Neuroscience
• Medical Imaging
• +3 more

Fengyu Cong is a Visiting Professor at the Faculty of Information Technology of the University of Jyväskylä (Finland). His research focuses on interdisciplinary computational neuroscience, biomedical signal processing, and machine learning applications in healthcare. Key contributions include EEG/fMRI analysis for mental health disorders such as depression and autism spectrum disorder, as well as federated learning methods for privacy-sensitive medical data. He has presented at major conferences including the 6th Conference on Mismatch Negativity (2012) and ESCAN 2014 , showcasing work on preattentive facial expression processing in neurological populations. His affiliations include the Engineering and Secure Communications Engineering and Signal Processing groups within his faculty. Research interests span neural signal analysis, multimodal imaging techniques, and AI-driven diagnostics. Notable methodologies include tensor decomposition for fMRI preprocessing, HMM-based sleep stage classification, and federated learning frameworks for distributed healthcare applications. His work bridges computer science and clinical neurology, with publications in top-tier journals addressing topics like vascular cognitive impairment, neural oscillations in autism, and artifact removal in biomedical signals.

Ozan ÖzdenizciView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Robustness and Safety in ML
• +4 more

Assoc. Prof. Ozan Özdenizci is a faculty member at the Institute of Machine Learning and Neural Computation, TU Graz, Austria. His research focuses on robustness, safety, and efficiency in machine learning, particularly in adversarial robustness, spiking neural networks, and privacy-aware learning. He holds a PhD from Northeastern University (2016), and MSc/BSc degrees from Sabancı University (2010/2008). Previously, he served as a postdoc at TU Graz (2016–2020) and a research group leader at Montanuniversität Leoben (2020–2023). Affiliations: TU Graz (2023–present), Montanuniversität Leoben (2020–2023), TU Graz Postdoc (2016–2020) Education: PhD (Northeastern University, 2016), MSc/BSc (Sabancı University, 2010/2008) Research Interests: Developing ML systems with robustness guarantees, efficient deep learning (e.g., spiking networks), and privacy-aware mechanisms. Key areas include adversarial defense mechanisms, sparse network design, and neuromorphic computing applications. His work bridges theoretical foundations with practical implementations in computer vision and autonomous systems. Recent Contributions: Advances in privacy-aware lifelong learning (ICLR 2025), robust spiking networks (TMLR 2024), and weather-resistant vision models (TPAMI 2023). His research emphasizes trade-offs between model efficiency, robustness, and scalability. Awards: Top Reviewer at NeurIPS 2023, Outstanding Reviewer at ICML 2022/ICLR 2022 Labs/Teams: Member of Graz Center for Machine Learning (GraML) and ELLIS Unit Graz

Barry de GrootView profile

Assistant Professor

• Learning Disabilities
• Dyslexia
• ADHD
• +14 more

Dr. Barry de Groot is an Assistant Professor at the University of Groningen's Faculty of Behavioural and Social Sciences, specializing in Special Needs Education and Youth Care. His research focuses on learning disabilities, dyslexia, ADHD, and neuroscientific methods like eye tracking and EEG. He has contributed to tools such as the Dutch Phonemic Analysis Test and leads studies on reading difficulties in children and higher education students. Active in international collaborations, he publishes on developmental disorders and cognitive interventions. His work aligns with UN SDGs related to quality education and inclusive societies. Education: Ph.D. in developmental neuropsychology (2009–2015). Key roles include co-authoring diagnostic tools and supervising undergraduate research. Grants and collaborations involve educational technology (e.g., GraphoLearn) and social-emotional learning instruments (Kindbegrip). Labs/Teams: Involved in the Department of Special Needs Education and Youth Care, contributing to interdisciplinary projects on neurocognitive profiling and early intervention strategies.

• Cognitive Neuroscience
• Working Memory
• Temporal Perception
• +3 more

Elkan Akyurek is a Professor of Cognitive Neuroscience and Chair of the Department of Experimental Psychology at the University of Groningen , Netherlands. His research focuses on understanding the neural mechanisms underlying working memory, temporal integration, and visual attention, with a particular emphasis on the interplay between perception and memory. He leads a lab investigating topics such as concealed information detection, adaptive temporal processing, and the neurophysiological underpinnings of cognitive functions. Education & Affiliations : While specific educational details are not explicitly provided, his academic trajectory includes leadership roles at the University of Groningen and collaborations with institutions like the Ruhr-University Bochum and University of Oxford. He is affiliated with the Dutch Society for Brain and Cognition (NVP) and has organized conferences and symposia. Research Interests : Dr. Akyurek’s work spans cognitive neuroscience, computational modeling of memory systems, and electrophysiological techniques (EEG/MEG). His lab employs methods like impulse perturbation and perceptual acceleration to study working memory maintenance, temporal integration, and hidden knowledge detection. Key areas include: Dynamic neural coding in working memory Temporal perception and attentional control Cross-modal sensory integration Effects of dietary supplements (e.g., cocoa flavanols) on cognition Awards & Grants : He has received the Snijders-Kouwer Award (2018), Early Career Publication Award from ESCoP (2018), and a grant from the Open Research Area (ORA) for collaborative work on hidden brain states. His lab has secured funding through the Police and Science program, NWO grants, and EU initiatives. Labs & Teams : The Akyurek lab at the University of Groningen includes PhD students, postdocs, and international collaborators. Current projects involve building EEG-based models of working memory and advancing concealed information detection techniques for forensic applications. Collaborations extend to researchers like Jelmer Borst and Nikolai Axmacher.

• Information Retrieval
• Brain-Machine Interfaces
• Electroencephalography (EEG)
• +3 more

Niall McGuire is a Researcher in the Department of Computer and Information Sciences at the University of Strathclyde. He holds a Doctor of Philosophy (PhD) in Computer and Information Sciences. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure. McGuire is involved in a major EPSRC-funded project (Industrial CASE Account - University of Strathclyde 2021) as a Research Co-investigator, focusing on brain-machine interfaces and information retrieval systems for individuals with physical impairments. His research interests center on the intersection of neurotechnology and information retrieval, including EEG-based systems, brain-computer interfaces, and adaptive human-computer interaction. Key topics include error classification in physiological signals, prediction of cognitive states, and EEG applications in music and aviation contexts. McGuire's recent publications explore challenges in brain-machine interface integration, EEG query representations, and ethical considerations in neurotechnology. His work emphasizes practical applications for accessibility and safety-critical systems. He is affiliated with the Human Centric AI Research Group at the University of Strathclyde, focusing on translating neuroscientific insights into tangible technological solutions.

Miguel Serras VascoView profile

Researcher

• Multimodal Perception
• Reinforcement Learning
• Representation Learning
• +4 more

Miguel Serras Vasco is a postdoctoral researcher at KTH Royal Institute of Technology in Stockholm, Sweden, affiliated with the Robotics, Perception and Learning (RPL) division. He holds a PhD from Instituto Superior Técnico, University of Lisbon (2023), where his work earned the Best PhD Thesis in AI in Portugal award. His research focuses on multimodal perception, reinforcement learning, and aligning artificial agents with human perception. He previously worked as an RSS Pioneer and research intern at Sony AI. Education: PhD in Artificial Intelligence, Instituto Superior Técnico, University of Lisbon (2023) Research Interests: Vasco’s work bridges robotics, neuroscience, and AI, emphasizing embodied agents that co-exist with humans. He explores representation learning, human-aligned image models, and sample-efficient reinforcement learning. His recent projects include super-human autonomous racing agents and olfactory perception modeling with transformers. Key Contributions: Vasco co-developed the GT Sophy racing agent, achieved outstanding results in visual decoding from brain activity, and proposed methods like FLoRA for preference-based RL. His work on NeuralSolver advances algorithm extrapolation in reinforcement learning. Awards: Best PhD Thesis in AI in Portugal (APPIA, 2023) Outstanding Paper Award at RLC 2024 (for autonomous racing research) Grants/Advising: Vasco advises students on multimodal and reinforcement learning topics. He actively organizes conferences like the Reinforcement Learning and Video Games Workshop (RLVG) at RLC 2025 and collaborates with institutions like INESC-ID (Lisbon). Labs/Teams: Associated with the Collaborative Autonomous Systems unit at KTH and previously with the GAIPS Lab (INESC-ID, Lisbon).