Find Academic Supervisors

Jean-Luc BoullandView profile

Associate Professor

• Spinal Cord Injury
• Locomotion
• Neural Plasticity
• +4 more

Jean-Luc Boulland is an Associate Professor at the University of Oslo's Institute of Basic Medical Sciences (Division of Physiology). His research focuses on spinal cord injury recovery mechanisms, functional electrical stimulation, and translational medical technologies. He leads projects on spinal trauma models in large animals and has affiliations with the NERD (NEurotrauma, Repair and Development) research group. His work integrates biomechanical injury models, sensor technologies, and stem cell tracking methods. Key contributions include developing MEMS-based pressure sensors and multimodal spinal injury apparatuses. Articles highlight innovations in sensor systems, locomotor circuitry repair, and cell-based therapies. Boulland's collaborations span neurobiology, biomedical engineering, and regenerative medicine. His lab explores tissue regeneration pathways and clinical validation of novel treatments, aiming to bridge basic science with clinical applications.

Wenjin WangView profile

Assistant Professor

• Biomedical Optics
• Signal Processing
• Computer Vision
• +3 more

Wenjin Wang is an Assistant Professor at Eindhoven University of Technology (TU/e) and a Scientist at Philips Research. He is affiliated with the Electronic Systems and Signal Processing Systems groups within the Department of Electrical Engineering. His research focuses on biomedical optics, signal/image processing, and camera-based health monitoring for applications like vital signs and activity measurement. He holds a BSc from Northeastern University (China), MSc from University of Amsterdam, and PhD from TU/e (2017), with a thesis on remote photoplethysmography. He actively contributes to academia through organizing workshops at IEEE conferences and editing a book on contactless vital signs. Education: BSc in Biomedical Engineering, Northeastern University (2011) MSc in Artificial Intelligence, University of Amsterdam (2013) PhD in Electrical Engineering, TU/e (2017) Research Interests: His work addresses challenges in non-invasive health monitoring, including camera-based vital signs (e.g., heart rate, respiratory motion), neonatal care, and privacy-preserving AI solutions. Key areas include biomedical optics integration, machine learning for medical imaging, and clinical validation of monitoring systems. Collaborations & Projects: He co-leads the FORSEE project (2020–2025), focusing on video monitoring for adverse event detection in healthcare. His work spans multi-center clinical trials, federated learning implementations, and hardware-software co-design for medical sensors. Publications & Impact: Over 50 peer-reviewed articles, 11 patents, and contributions to IEEE journals/conferences. His research bridges academia and industry, with prototypes commercialized through Philips Research.

Martin GevondenView profile

Assistant Professor

• Psychophysiology
• Stress Research
• Autonomic Nervous System
• +5 more

Dr. Martin Gevonden is an Assistant Professor at the Faculty of Behavioural and Movement Sciences, Department of Biological Psychology at Vrije Universiteit Amsterdam. He also holds appointments in the APH - Mental Health and APH - Personalized Medicine units. He is the Director of VU - Ambulatory Monitoring Solution, a company focusing on ambulatory health monitoring technologies. His research focuses on psychophysiological mechanisms of stress and trauma, wearable sensor technologies, and autonomic nervous system reactivity in clinical populations. His expertise includes heart rate variability analysis, stress biomarkers, and the development of ambulatory monitoring systems. He teaches courses on Psychophysiological Measurements , Analyses Toolbox , and Behaviour and the Brain part 1: Addiction . Current research projects include ambulatory recording of autonomic nervous system activity and its health implications, as well as the effect of fitness on stress reactivity. Key research themes include posttraumatic stress disorder (PTSD), wearable sensor validation, and machine learning applications in physiological signal analysis. His work bridges clinical psychology, biomedical engineering, and public health, with a focus on translating physiological insights into practical monitoring tools. Dr. Gevonden has supervised one PhD thesis and is actively involved in interdisciplinary collaborations, contributing to global health initiatives such as studies in Nepal, Colombia, and South Africa. His publications emphasize methodological rigor in wearable technology validation and the ecological validity of stress assessment methods.

• e-Health
• Human-Computer Interaction
• Behavioral Change
• +3 more

Femke Nijboer is an Assistant Professor affiliated with the TechMed Centre and Digital Society Institute, focusing on e-Health technologies, behavioral change, and neuroethics. Her work bridges healthcare, technology, and ethics, particularly in assistive technologies for locked-in syndrome patients and aging populations. Research interests include co-design methodologies for older adults, brain-computer interface (BCI) usability, and ethical implications of neurotechnology. She actively collaborates with interdisciplinary teams to develop user-centered solutions addressing late-life loneliness and healthcare accessibility. Key contributions include studies on BCI preferences for locked-in syndrome patients and frameworks for managing emotional requirements in software ecosystems. Her work is driven by UN Sustainable Development Goals related to health equity and innovation. Public engagement includes media contributions discussing aging, technology ethics, and participatory design. Supervision and grant activities are ongoing but details are currently limited. Labs/teams: TechMed Centre (BCI applications), Digital Society Institute (eHealth innovation), and collaborative networks in gerontechnology and neuroethics.

Nadeem GhaniView profile

Teaching Professor

• Human Vision and Psychophysics
• Neurophysiology
• Human Factors
• +3 more

Nadeem Ghani is an Assistant Teaching Professor at Syracuse University, located in room 4-232 CST. His research and teaching focus on Human Vision and Psychophysics, Neurophysiology, Human Factors, and Software Engineering and Design. He also explores multi-disciplinary approaches to GUI design and visualizations, as well as Biology-inspired computing. Contact him via email at nghani@syr.edu. His research interests include: Human Vision and Psychophysics: Investigating how humans perceive visual information and its applications in design. Neurophysiology: Studying nervous system functions relevant to visual processing. Human Factors: Enhancing system design to better suit human capabilities and limitations. Software Engineering and Design: Focusing on effective software development methodologies. Multi-disciplinary Approaches: Applying cross-disciplinary methods to challenges in GUI design and visualization. Biology-inspired Computing: Exploring computational models based on biological systems. No scientific awards or grants are listed in the provided information. Nadeem Ghani has advised no recorded students.

Aaron S.W. WongView profile

Part-Time Lecturer

• Computer Science
• Artificial Intelligence
• Robotics
• +16 more

Dr Aaron Wong is an Honorary Lecturer at the School of Psychology , University of Newcastle , with dual affiliations in Computer Science and Software Engineering . His career spans interdisciplinary research in robotics, cognitive neuroscience, and neuroimaging, supported by roles in the Functional Neuroimaging Laboratory and Priority Research Centre for Stroke and Brain Injury . Education: PhD in Computer Science, University of Newcastle Bachelor of Engineering (Computer Engineering) with Honours Bachelor of Computer Science Research Focus: Dr Wong's work bridges machine learning , humanoid emotion modeling , and multimodal neuroimaging (MRI/EEG). Key projects include developing brain-computer interfaces , acoustic localization systems for rescue robots, and semi-supervised manifold alignment techniques. Publication Trends: Recent articles emphasize theta oscillations in cognitive control , EEG-MRI joint modeling , and deep learning for speech emotion recognition . Collaborative themes span neurodevelopmental studies , stroke research , and urban robotics . Scientific Awards: 2008 RoboCup SPL World Champion 2011 RoboCup Australian Open Champion 2014 Global Leader of Innovation (UON) 2011 Best Student Paper (ANZAScA) Supervision & Grants: Served as co-supervisor for two PhD students. Secured $23,775 in grants including a 2018 Hunter Medical Research Institute award for EEG-MRI predictive modeling and 2016 Keats Endowment funding for cognitive flexibility research . Labs & Teams: Central to the Functional Neuroimaging Laboratory and PRC Stroke and Brain Injury , with global RoboCup collaborations and contributions to the Age-ility Project data repository.

Alex ReinhartView profile

Academic

• Statistical Pedagogy
• Educational Research
• Natural Language Processing
• +5 more

Alex Reinhart is an Associate Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. from CMU and a BS in Physics from the University of Texas at Austin. His research focuses on statistical pedagogy, natural language processing, and applications of spatio-temporal data analysis to crime prediction and radiation detection. Reinhart’s work emphasizes improving statistical education through innovative methods like writing in the age of AI and psychology case studies. He also explores the intersection of large language models and human-like text generation. His contributions include the book Statistics Done Wrong , which critiques common statistical errors in scientific research. His recent publications address pandemic-related challenges, such as vaccine hesitancy and real-time data analysis through surveys like the US COVID-19 Trends and Impact Survey. His research spans statistical software development (e.g., the pseudobibeR package) and interdisciplinary applications in public health, biomechanics, and education.

• Neuroimaging
• Medical Imaging
• Data Analysis
• +3 more

Dr. Thomas Close is a National Imaging Facility Informatics Fellow at the School of Biomedical Engineering, University of Sydney . His work focuses on neuroimaging analysis, computational neuroscience, and developing reproducible research tools. He holds a PhD and has contributed to advancements in medical imaging technologies like MRI/PET motion correction and multimodal data integration. Education: PhD (details not specified). Affiliations: National Imaging Facility, School of Biomedical Engineering, and collaborations with institutions like Monash University. Research Interests: Dr. Close specializes in neuroimaging methodologies, including motion correction algorithms, multimodal imaging (fMRI/PET), and open-source software frameworks for reproducible neuroscience. His work bridges computational techniques and clinical applications, with a focus on cerebellar function and neurodegenerative diseases such as Friedreich ataxia. Publications: His recent research includes Neurodesk (an open science tool for neuroimaging), cortico-cerebellar architecture studies, and advancements in diffusion MRI tractography. His work emphasizes reproducibility, open science practices, and collaborative platforms like Brainhack. Grants/Advising: No specific grants or advisees listed in the provided texts. However, his research collaborations suggest engagement with interdisciplinary teams. Labs/Teams: Associated with the National Imaging Facility, where he contributes to informatics initiatives, and the University of Sydney’s Biomedical Engineering labs focusing on neuroimaging technologies.

Paul von BünauView profile

Adjunct Professor

• Artificial Intelligence
• Healthcare Technology
• Machine Learning
• +4 more

Dr. Paul von Bünau serves as Managing Director (CEO) of IDALab, a strategic advisory firm focused on artificial intelligence in healthcare and biotechnology. He concurrently holds an adjunct academic role at Europa-Universität Viadrina Frankfurt (Oder), where he teaches courses on AI strategy and regulatory compliance in healthcare. His educational background includes a PhD in Machine Learning (TU Berlin), M.Sc. in Pure Mathematics (University of St Andrews), and B.Sc. in Computer Science (University of Potsdam). **Professional Background**: Paul provides strategic AI guidance to multinational healthcare companies, biotech startups, and medical device manufacturers. Key engagements include building AI innovation units for a medtech company, accelerating drug discovery through AI-driven R&D, and developing AI tools for healthcare market access under EU regulations. His approach emphasizes aligning technical solutions with organizational needs and regulatory landscapes. **Research & Teaching**: At Viadrina, he delivers seminars on AI strategy (2024) and regulatory compliance in healthcare AI (2025). His research focuses on non-stationary data analysis, clinical data harmonization, and AI applications in neuroscience. Recent work includes a validated clinical data pipeline for scalable AI deployment (JMIR Medical Informatics, 2023) and algorithms for stationary subspace analysis (Physical Review Letters, 2009). **Key Takeaways from Projects**: Companies with strong engineering cultures often underestimate the cultural and structural changes required for AI integration. Rapid prototyping, talent alignment, and cross-disciplinary teams (e.g., AI + healthcare experts) are critical for success. His work highlights the transformative potential of AI in healthcare while emphasizing ethical and regulatory considerations.

Elizabeth DavenportView profile

Assistant Professor

• Neuroimaging
• Traumatic Brain Injury
• Alzheimer's Disease
• +4 more

Dr. Elizabeth Davenport is an Assistant Professor of Radiology, Biomedical Engineering, and Advanced Imaging Research at UT Southwestern Medical Center, where she serves as Technical Director of the Magnetoencephalography (MEG) Center of Excellence. Her research investigates subconcussive head impacts in contact sports, traumatic brain injury biomarkers, and neurodegenerative diseases using advanced neuroimaging techniques. Education: PhD in Biomedical Engineering from Virginia Tech-Wake Forest University, BS in Biomedical Engineering from North Carolina State University. Research Focus: Dr. Davenport's lab develops MEG and MRI biomarkers for brain injury, analyzing neural network alterations from repetitive head impacts. Her work integrates diffusion tensor imaging, functional connectivity, and biomechanical modeling to understand cognitive outcomes in athletes and neurological patients. Publication Analysis: Recent works (2020-2025) demonstrate expertise in MEG signal processing, concussion biomarkers, and neural network dynamics. Key contributions include standardized MEG guidelines (ACR White Paper), microhemorrhage detection in athletes, and computational models linking biomechanics to neuroimaging changes. Lab Leadership: Directs the Davenport Lab focused on quantitative neuroimaging methods Leads NIH-funded studies on youth football head impact exposure Collaborates with ANSIR Lab and Biomedical Engineering programs

• Glottology
• Linguistics
• Speech Production
• +3 more

Francesco Sigona is a Researcher in Glottology and Linguistics at the University of Salento's Department of Humanities, affiliated with the DReAM Laboratory (Diffused Laboratory of Interdisciplinary Research Applied to Medicine) at Vito Fazzi Hospital in Lecce. He holds a PhD in Information Technologies for Engineering. His research explores the intersection of linguistics, neuroscience, and technology, with focus areas including: Neural mechanisms of language processing and speech production Voice biometrics and forensic speaker recognition Articulatory phonetics and speech technology development Clinical applications of linguistic analysis in voice disorder detection His extensive publication record demonstrates a consistent focus on multimodal approaches combining physiological measurements, neurophysiological data, and computational methods across medical and linguistic domains.

Konstantin NikolicView profile

Professor

• Artificial Intelligence
• Machine Learning
• Data Management
• +7 more

Konstantin Nikolic is a Professor of Computer Science specializing in Artificial Intelligence, Machine Learning, and Data Management at the University of West London's School of Computing and Engineering. Previously, he served as Senior Research Fellow and Principal Investigator at Imperial College London's Institute of Biomedical Engineering (2006–2020) and held roles as Assistant Professor and Associate Professor at the University of Belgrade's Faculty of Electrical Engineering. He holds a PhD in Physics from Imperial College London and degrees in Applied Physics from the University of Belgrade. His research spans computational neuroscience, neuromorphic engineering, neuroprosthetics, and biomedical systems. Key areas include developing AI-driven drug discovery methods, neuromorphic robotics platforms, and optogenetic models for neural interfaces. Recent work explores emergent quantum phenomena in complex adaptive systems and integrates machine learning with biosensors for medical applications. Publications emphasize interdisciplinary approaches, including neuromorphic hardware implementations, optogenetic device modeling, and biophysical simulations. Collaborations include institutions like Imperial College and the University of Belgrade, with a focus on applying computational models to real-world biomedical challenges.

• Neuroscience
• Neural Coding
• Information Theory
• +3 more

Stefano Panzeri is a Professor at the University of Hamburg's Medical Faculty, affiliated with the Department of Neuroscience. His research focuses on neural coding, information theory, and computational neuroscience, particularly in sensory systems and decision-making processes. He contributes to the Zentrum für Molekulare Neurobiologie Hamburg (ZMNH), advancing understanding of neural network dynamics and their implications for cognition. Key areas: Neural information processing, sensory systems, decision-making. Publications span topics like cortical coding, neuroimaging, and computational models. His work integrates experimental and theoretical approaches, with recent emphasis on decoding neural signals and their behavioral relevance. Collaborations include developing tools like MINT for neural data analysis.

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Nanoparticle Technology
• +5 more

Olav Haraldseth is a Professor and Director of the MI Lab at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Circulation and Medical Imaging within the Faculty of Medicine. He holds a PhD in Medicine (1992, NTNU) and an MD (1984, University of Bergen). His research focuses on advanced MRI techniques, nanoparticle development for imaging and drug delivery, and image-guided regenerative medicine. He has led major initiatives including the SFI Medical Imaging Centre (SFI), Norwegian Research School in Medical Imaging (MedIm), and national and international imaging networks like NorMIC and EATRIS. His work spans preclinical and clinical applications, with contributions to neuroimaging, cardiovascular MRI, and molecular imaging technologies. Key roles include former Dean for Research at NTNU’s Faculty of Medicine (2006-07), Head of Department (2005-05), and Vice-Head (2002-05). He has pioneered methods such as manganese-enhanced MRI and dynamic contrast-enhanced MRI for studying brain injury, myocardial viability, and tumor response to therapy. His leadership extends to national committees and European infrastructure projects like Euro-BioImaging and EATRIS. Research interests emphasize translational applications: functional MRI for brain activity mapping, multi-modal nanoparticles for simultaneous imaging and therapy, and MRI guidance in stem cell transplantation and regenerative therapies. His work bridges engineering, biology, and clinical practice, with over 100 publications in high-impact journals like Neurochemical Research , ACS Nano , and Journal of Magnetic Resonance Imaging .

Enny van BeestView profile

Academic

• Vision Science
• Behavioral Neuroscience
• Cognitive Neuroscience
• +3 more

Dr. Enny van Beest is a Senior Research Fellow at the Institute of Ophthalmology, University College London, working in the lab of Professors Carandini and Harris. Her research focuses on understanding how spatial and sensory information is integrated across the brain, with particular emphasis on distributed neural coding mechanisms. She has developed UnitMatch, software for tracking neurons across longitudinal recordings using Neuropixels probes. Education includes a PhD in Neuroscience from the Netherlands Institute for Neuroscience (2020), an MSc in Brain and Cognitive Neuroscience from the University of Amsterdam (2015), and a BSc in Psychobiology (2013). She has held postdoctoral positions at UCL since 2020, and was a Marie Skłodowska-Curie Fellow (2020). Research interests span vision science, behavioral neuroscience, and computational methods. Recent work includes studying spatial navigation in sensory contexts, basal ganglia pathways' influence on perceptual decisions, and the neural basis of figure-ground perception. Her software contributions have received awards for open science practices. Awards include the UCL Research Excellence Award (2025) and recognition for her UnitMatch tool (2024). She mentors students and actively participates in initiatives promoting inclusive recruitment and science education. Current projects aim to establish her own research group investigating distributed neural mechanisms of visually guided behavior.