Tiago de Oliveira Paiva is a Researcher at Lusófona University 's School of Psychology and Life Sciences , affiliated with the Faculty of Sports and Physical Education and the HEI-Lab - Digital Laboratories of Environments and Human Interactions . Key Research Themes: Neuroscience, Psychology, Mental Health, Decision-Making, Psychometrics, and Neuroeconomics. His work integrates event-related potential (ERP) biometrics, psychopathy , externalizing behaviors , and uncertainty processing in decision-making. Current projects include neurophysiological studies on empathy and psychopathology etiology through statistical learning. Recent publications highlight trends in ERP-based diagnostics , aggression correlates , and predictive processing models . He collaborates internationally on topics like mental health and social cognition . As a core member of HEI-Lab, Paiva contributes to interdisciplinary research bridging digital environments and human interactions.
Simon Lind Kappel is an Assistant Professor at the Department of Electrical and Computer Engineering under the School of Engineering at Aarhus University . His work bridges biomedical engineering, neuroscience, and signal processing, with a focus on developing wearable EEG technologies for sleep monitoring and neural interaction analysis. Primary Affiliation: Department of Electrical and Computer Engineering, Aarhus University Contact: slk@ece.au.dk | +45 20 69 34 56 | Aarhus N, 5125-329 Kappel's research spans several domains: Biomedical Engineering: Characterizing dry-contact EEG electrodes, wearable diagnostics, and portable neural monitoring systems. Neuroscience: Sleep stage classification, hypoglycemia detection via ear-EEG, and joint action research in collaborative systems. Machine Learning: Applying interpretable AI models like cross-modal transformers for sleep data analysis and knowledge distillation techniques to enhance EEG signal processing. His projects include DONUT (European Doctoral Network for Neural Prostheses) and Sleep in Orbit , which explore both fundamental neural mechanisms and applied health monitoring solutions. Selected Research Trends: Recent publications highlight advancements in ear-EEG technology for sleep studies, neural decoding of gestures from electrocorticography, and collaborative dynamics in larger human groups. His work often involves interdisciplinary collaborations with teams like Kidmose Lab and IEEE Engineering in Medicine and Biology Society. Projects: Active in EU-funded initiatives such as DONUT (2024–2027) and Sleep in Orbit (2022–2025), focusing on neural prostheses, sleep research, and biomedical instrumentation.
Vanessa D'Amario is an Assistant Professor in the Health Informatics Department at the Dr. Karen C. Patel College of Osteopathic Medicine , Nova Southeastern University. She holds a Ph.D. in Computer Science from Università degli Studi di Genova, Italy, following bachelor's and master's degrees in Physics. Ph.D. in Computer Science (Università degli Studi di Genova, Italy) M.S. in Physics (Università degli Studi di Genova, Italy) B.S. in Physics (Università degli Studi di Genova, Italy) Her research focuses on Machine Learning applications in Healthcare , particularly in epilepsy research and AI-driven medical education. She has developed systems for electronic health records (EHR) in international medical outreach settings and explored systematic generalization in AI models for visual question answering. Recent publications highlight her work in Generative AI adoption in medical education, Deep Learning efficiency for healthcare applications, and Transformer Networks for neurological data analysis. Her scholarly output spans three major domains: (1) AI ethics and optimization in clinical contexts, (2) Epilepsy diagnosis through computational modeling, and (3) Global health informatics systems.
Shangfei Wang is a full Professor at the School of Computer Science and Technology, University of Science and Technology of China (USTC). His research focuses on pattern recognition, affective computing, and probabilistic graphical models, with significant contributions to facial expression analysis, emotion recognition, and multimodal human-robot interaction. He leads the Key Laboratory of Computing and Communication Software of Anhui Province and has received multiple international competition awards including placements in the OMG Empathy Prediction Challenge and Detecting Depression with AI Sub-Challenge. PhD in Computer Science, USTC (2002) MSc in Electronic Science and Technology, USTC (1999) BSc in Electronic Engineering, Anhui University (1996) His work combines domain knowledge with advanced machine learning techniques, including adversarial learning, dual learning, and multimodal deep regression Bayesian networks. Research themes include: Emotion-aware medical consultation systems Thermal image-based facial recognition Privileged information learning for emotion detection Spontaneous vs. posed expression differentiation EEG and physiological signal integration AI applications in mental health support Key projects include multiple National Nature Science Foundation of China grants and international collaborations with French institutions. He serves as Associate Editor for IEEE Trans. on Affective Computing and ACM Trans. on Multimedia Computing, and organized several international conference tracks.
Sofie Mangaard is a PhD Student at the Department of Clinical Research of the University of Southern Denmark , affiliated with the Research Unit of Neurology (Odense) and Karolinska Institute (KI). Her research focuses on epilepsy, executive dysfunction, and psychiatric comorbidities. Active participant in the project Long-term outcome of status epilepticus since 2017. Published three peer-reviewed articles in Epilepsia and Epilepsy & Behavior (2021-2025). Collaborates with researchers at Karolinska Institute and Odense University Hospital . Her work explores the intersection of epilepsy subtypes (e.g., juvenile myoclonic, childhood absence), EEG patterns, and psychiatric symptoms like impulsivity. She utilizes nationwide surveys and advanced phenotyping techniques to assess disease burden and seizure classification trends. Publications highlight interdisciplinary research in Neuroscience , Machine Learning , and Mental Health , with subtopics spanning tonic-clonic seizures, ictal activity, and neurobehavioral linkages. No specific awards are mentioned in available data.
Dr. Palina Salanevich is an Assistant Professor at Utrecht University's Mathematical Institute, specializing in Mathematical Modeling. Her work bridges harmonic and time-frequency analysis, geometric functional analysis, signal and image processing, and high dimensional probability theory. Her research focuses on: Geometric properties of random frames and matrices, particularly Gabor frames Phase retrieval problems and their applications in ptychography and speech recognition Machine learning with randomization, especially Random Vector Functional Link networks Data processing using non-negative matrix factorization for audio and EEG signals Harmonic and time-frequency analysis on graphs Dr. Salanevich's recent publications show a strong focus on phase retrieval with Gabor frames, random matrix theory applications in signal processing, and randomized approaches to machine learning. Her work consistently bridges theoretical mathematics with practical signal processing applications, particularly in developing stable and efficient algorithms for complex inverse problems. Her notable scientific achievements include: NWO Veni grant (2021) for "Time-frequency structured measurements in phase retrieval: stability and reconstruction" Board member of the NDNS+ research cluster (2024) Dr. Salanevich actively supervises students, including PhD student Olov Schavemaker (since December 2021), and offers various projects for Bachelor and Master students in areas related to mathematical modeling and signal processing. She has been involved in several collaborative research projects with institutions like CWI and researchers including Deanna Needell, Goetz E. Pfander, and others. She is organizing the "Phase retrieval in mathematics and applications" workshop at the Lorentz Center in August 2024, demonstrating her leadership in her research community.
Sophie Adama is a Researcher at the Department of Neuromorphic Information Processing within the Faculty of Mathematics and Computer Science at the University of Leipzig . Her work focuses on machine learning applications in neuroprosthetics, brain-computer interfaces, and signal processing for consciousness assessment. Research areas: Machine Learning, Neuroprosthetics, EEG Signal Processing, Disorders of Consciousness Key collaborations: Prof. Dr. Martin Bogdan, Ujwal Chaudhary, Niels Birbaumer Email: adama@informatik.uni-leipzig.de Her publications since 2016 demonstrate a consistent focus on developing and applying machine learning techniques to analyze EEG data in clinical contexts, particularly for disorders of consciousness and neurological rehabilitation. She has pioneered soft-clustering approaches for consciousness assessment in CLIS patients. Current research includes: EEG-based consciousness detection BCI-FES integration for stroke/Parkinson's rehabilitation Coherency analysis in neural signals Automated sleep stage analysis for neurological patients Development of DoC-Forest diagnostic tool Time-domain EEG signal processing for attention focus prediction
Giuseppe Sansonetti is an Associate Professor at the Department of Civil, Computer and Aeronautical Engineering, University of Rome III. His research focuses on Artificial Intelligence, Machine Learning, and their applications in cultural heritage, social robotics, and recommender systems. He teaches courses such as Elements of Computer Science and Artificial Intelligence and Machine Learning across multiple engineering programs. His research explores: Multimodal AI for museum visitor analysis Deep learning applications in infrastructure monitoring Context-aware recommendation systems Emotion recognition via physiological sensing Recent publications (2023-2025) demonstrate interdisciplinary work spanning computer science, civil engineering, and cultural technology. Key trends include integrating AI with social and cultural contexts, improving recommendation systems, and developing adaptive learning environments.
Dr. Gaia Lapomarda is a researcher at the Institute of Psychology, University of Innsbruck, based in the Department of Affective Neuroscience. Her work integrates neuroscience and psychology to explore how emotional states influence cognition and perception, with a strong focus on clinical applications. Research Interests: Affective neuroscience and emotion regulation mechanisms EEG-based biomarkers of emotional processing Neuroimaging of psychiatric disorders (bipolar disorder, borderline personality disorder) Impact of childhood trauma on brain structure and function Temporal perception and sensory processing under emotional states Her research bridges basic neuroscience with clinical psychiatry, employing advanced techniques like multimodal neuroimaging (mCCA+jICA), machine learning for biomarker discovery, and EEG-based investigations of neural oscillations. A recurring theme is how affective states modulate fundamental cognitive processes like time perception and visual processing speed. Key Findings from Publications: Dr. Lapomarda's work reveals altered thalamic-parahippocampal circuits in schizophrenia/bipolar disorder, demonstrates sustained EEG changes post-emotion regulation, and identifies structural brain patterns predicting trauma-related symptoms in borderline personality disorder. Her 2025 study on language comprehension adds a novel social dimension, exploring how perceived speaker identity influences sentence processing.
Dr. Saugat Bhattacharyya is a Lecturer in Computer Science at Ulster University, specifically within the School of Computing, Engineering & Intelligent Systems at the Magee Campus in Derry~Londonderry. His research focuses on the intersection of cognitive neuroscience, artificial intelligence, and human-machine interaction, with particular emphasis on brain-computer interface systems for neuro-rehabilitation applications. Dr. Bhattacharyya received his academic training in India, earning a Bachelor's Degree in Biomedical Engineering from West Bengal University of Technology (2009), followed by a Master of Engineering Degree in Biomedical Engineering from Jadavpur University, Kolkata (2011). He completed his Ph.D. in Biomedical Engineering from Jadavpur University in 2015, with research focused on "Human-Computer Interface for Motion Control of Artificial Limb(s)". During his doctoral studies, he was a Visiting Scientist at Paul Valery University of Montpellier, France (2014-2015) through the Erasmus Mundus-Svaagata Project Fellowship. His primary research interests center on developing brain-computer interfacing systems that utilize robust signal processing and machine learning algorithms to interpret users' cognitive states through neural and physiological signals. Dr. Bhattacharyya has made significant contributions to the fields of cognitive neuroscience and neuro-rehabilitation, particularly in applying AI and machine learning to enhance human-machine interaction. His work often addresses practical challenges in stroke rehabilitation and decision-making support systems. Dr. Bhattacharyya's research output demonstrates a strong focus on translating theoretical advances into practical applications, with particular attention to mental fatigue monitoring, collaborative brain-computer interfaces for group decision making, and advanced signal processing techniques for neural data. His recent work shows increasing integration of quantum computing concepts with traditional machine learning approaches for EEG analysis. Unravelling the Forest Fires in Lower Himalayan Forests: A Comprehensive Study of Indian Forest Regions of Uttarakhand using IoT technology (2019) Motion control of artificial limb(s) through a human-computer interface (2012) Study of the probabilistic nature of Motor Imagery Electroencephalography signals and its correlation with Electromyography signals for closed loop control of a robotic manipulator (2014) Dr. Bhattacharyya serves as Principal Investigator for multiple research projects including "AI-EPOCMON" (2022-2026), "A Brain-Computer Interface driven Mental Fatigue Monitoring System to improve Stroke Rehabilitation Therapy" (2024-2025), and "Patient and Public Involvement in Developing Accessible Stroke Rehabilitation Technology" (2024). He also contributes as a Co-Investigator on projects like the "Smart Nano-Manufacturing Corridor" and "Upgrading Magnetoencephalography(MEG) system with Internal Helium Recycler". His research is supported by funding from the Department for the Economy and Medical Research Council. His laboratory work focuses on developing intelligent neuro-technologies for rehabilitation applications, with current projects emphasizing mental fatigue monitoring in stroke rehabilitation and collaborative brain-computer interfaces for group decision making. The team employs advanced signal processing techniques, machine learning algorithms, and novel hardware integration to create more effective neuro-rehabilitation systems.
Christopher McCausland is a Data Scientist at the School of Computing, Engineering and Intelligent Systems at Ulster University, Belfast campus. His work focuses on applying advanced analytical techniques to biomedical data, particularly in sleep science and cardiology. Location: Room BA-02-005, 2-24 York Street, Belfast, BT15 1AP Contact: c.mccausland@ulster.ac.uk Phone: +44 28 9536 7494 McCausland's research bridges machine learning, signal processing, and healthcare applications. He specializes in: Time–frequency analysis for sleep stage classification Deep learning in electroencephalogram (EEG) and electrocardiogram (ECG) data interpretation Improving inter-scorer reliability in sleep studies Device-agnostic artificial intelligence for cardiac diagnostics Semantic modeling for mental health interventions Featurization techniques in traditional and deep learning ECG algorithms His recent publications highlight trends in biomedical data science, particularly leveraging time-frequency transforms and recurrent neural networks for EEG and ECG analysis. These works span disciplines including machine learning, neuroscience, cardiology, and health informatics. McCausland's contributions to digital health interventions emphasize practical applications of data science in clinical settings, though no awards or formal grants are explicitly mentioned in the available information. He does not currently appear to have advisees listed in public records.
Karl McCreadie serves as a Lecturer in Data Analytics at Ulster University's School of Computing, Engineering and Intelligent Systems, Magee Campus. His research integrates computational methods with neurotechnology to address clinical challenges, particularly in brain-computer interfaces and rehabilitation engineering. His primary research domains include Brain-Computer Interfaces (specializing in motor imagery decoding and auditory feedback systems), Virtual Reality applications for upper-body physiotherapy, and smart materials development for medical devices like stoma management systems. He employs advanced machine learning techniques to enhance classification accuracy in EEG/MEG signal processing and develops embodied VR environments for neurorehabilitation. His work bridges computer science with clinical needs, focusing on user experience optimization and assistive technology for disabilities. Analysis of his 26 publications (2011-2025) reveals a clear evolution from foundational BCI signal processing toward applied clinical solutions. Early work concentrated on motor imagery classification algorithms and auditory feedback mechanisms, while recent publications (2023-2025) emphasize extended reality rehabilitation, biodegradable electrode substrates, and semiconductor production optimization. A strong interdisciplinary thread connects his machine learning expertise with biomedical applications, particularly in stoma care innovation and neurotechnology for tetraplegia. Dr. McCreadie actively contributes to major research initiatives including the Princess Anne-opened Spatial Computing & Neurotechnology Innovation Hub (2023) and three Medical Research Council/Invest NI-funded stoma care projects: Addressing GAPS in Stoma Output Monitoring (2025-2026), STOMACAP: Reimagining Stoma Management (2025-2026), and MICA: Stomasense (2023-2026). These projects address critical healthcare challenges through composite materials and wireless monitoring systems. He has supervised three research students and maintains active collaborations within Ulster's Computer Science and Informatics group. As a core member of the Spatial Computing & Neurotechnology Innovation Hub, he participates in developing next-generation neurotechnology solutions that combine virtual reality, spatial computing, and physiological signal processing. His team's work on the Cybathlon championship training program demonstrates real-world impact in assistive technology for people with severe disabilities.
Dr. Leandro Junges serves as a Centre Fellow at the Centre for Systems Modelling and Quantitative Biomedicine (SMQB) within the Department of Metabolism and Systems Science, College of Medicine and Health at the University of Birmingham. His research specializes in developing mathematical and computational methods to advance understanding of biomedical systems and enable personalized healthcare predictions, with a primary focus on neuroscience applications. His educational background includes a BSc in Physics (2007), MSc in Theoretical Physics (2009), and PhD in Theoretical Physics (2014), all obtained from the Universidade Federal do Rio Grande do Sul (UFRGS) in Brazil. His doctoral work investigated dynamical systems subjected to delayed feedback, establishing the foundation for his current computational approaches. Dr. Junges' research integrates mathematical modelling , network science , and data analysis to address critical challenges in biomedical systems . He specializes in epilepsy research , developing frameworks for seizure forecasting and treatment evaluation using EEG/fMRI network analysis . His work bridges computational theory with clinical applications, particularly in pediatric neurology and sleep-related neural dynamics, aiming to translate complex modeling into actionable clinical tools for personalized medicine. Analysis of his 2020-2024 publications reveals consistent innovation in computational neuroscience, with emphasis on epilepsy network dynamics, chronotype classification via fMRI, and treatment response modeling. Key trends include the development of mathematical frameworks for longitudinal treatment analysis, network-based biomarkers for childhood epilepsies, and real-time digital health interventions for seizure prediction, demonstrating strong translational focus from theoretical modeling to clinical protocols. No scientific awards are documented in the available information. Details regarding student advising and research grants are not provided in the source material, though his collaborative publication record indicates active engagement with interdisciplinary research teams. As a core member of the Centre for Systems Modelling and Quantitative Biomedicine (SMQB), Dr. Junges collaborates with neurologists, data scientists, and clinicians within the Department of Metabolism and Systems Science. His work involves developing computational pipelines for EEG/fMRI analysis and contributing to the Centre's mission of advancing quantitative approaches to biomedicine through cross-departmental partnerships in epilepsy research and neural network modeling.
Mohamed S. Ameen is a Postdoctoral Researcher at the University of Salzburg's Department of Psychology within the Laboratory for Sleep, Consciousness, and Cognition Research. His work integrates cognitive neuroscience and sleep physiology to investigate neural mechanisms during sleep states, utilizing advanced neuroimaging techniques including hdEEG and fMRI. His academic journey includes: PhD in Cognitive Neuroscience (2019-2024) from University of Salzburg under Kerstin Hoedlmoser M.Sc. in Neuroscience (2014-2016) through Joint Master program (Strasbourg/Freiburg/Basel) with thesis at Cambridge University B.Sc. in Pharmacy with Honours (2008-2013) from Cairo University Dr. Ameen's research centers on sleep-consciousness interactions and neural information processing , with particular emphasis on how the brain selectively processes external stimuli during sleep. His investigations into (non-)oscillatory neural activity reveal how aperiodic signals and oscillations track sleep architecture changes and support memory consolidation. He actively promotes scientific literacy through science communication initiatives targeting both academic and public audiences. Analysis of his publication record shows consistent focus on sleep-dependent neural mechanisms, particularly the role of sleep spindles in memory processing and the brain's capacity for selective auditory processing during sleep. His methodological expertise in analyzing both oscillatory and aperiodic neural activity has advanced understanding of sleep's functional architecture. His scientific recognition includes: Young Talent Award for Sleep Research (2023) Young Investigators Award (2022) DOC doctoral fellowship (2019) Early Career Postdoctoral Grant (2024) Dr. Ameen has secured significant research funding including a €42,693 Early Career Grant for his REM Sleep Structure project and €115,000 DOC fellowship. As Student Speaker for the Imaging the Mind Doctoral College (2019-2024), he facilitated academic development while maintaining active collaborations across Columbia University, Free University of Brussels, and Cambridge University. His technical proficiency spans Python, MATLAB, and advanced EEG analysis toolboxes. He operates within the Center for Cognitive Neuroscience Salzburg, collaborating closely with Prof. Manuel Schabus and Assoz.-Prof. Kerstin Hoedlmoser. His international research network extends through recent stays at Columbia University's Electrophysiology Lab and Free University of Brussels' Neuropsychology Unit, driving cross-institutional investigations into sleep's role in cognitive processing.
Smith Khare is an Assistant Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, specializing in Applied AI and Data Science. He is affiliated with the Centre for Clinical AI (CAI-X) in Odense and contributes to healthcare analytics through interdisciplinary research. Ph.D. in Electronics and Communication Engineering (2022) from IIITDM Jabalpur M.Tech in Electronics and Telecommunication Engineering (2015) from Veermata Jijabai Technological Institute His research focuses on explainable AI, deep learning, and signal processing for healthcare applications. Key areas include medical imaging, neurological disorder detection, and biomedical signal analysis. His work integrates AI with clinical diagnostics to improve early disease identification. Smith’s recent publications emphasize explainable AI in cervical cancer screening, liver segmentation, and neurological conditions like Alzheimer’s and Parkinson’s. He combines deep learning with optimization algorithms and hardware implementation for medical IoT systems. Best Paper Award, IEEE Sensors Journal (2024) He teaches courses such as Artificial Intelligence for Healthcare Data and Tools of Artificial Intelligence , and mentors students in AI-driven healthcare projects. His collaborations span institutions like Karolinska Institute (KI) and Odense University Hospital (OUH).
