Nicholas Carnevale is a Senior Research Scientist in Neuroscience at Yale School of Medicine, affiliated with the Department of Neuroscience. His work focuses on computational neuroscience, neuronal modeling, and electrophysiological simulations using the NEURON software. He holds an MD and PhD from Duke University, with postgraduate training at Stanford University and the University of California, San Diego. Carnevale collaborates extensively with Michael Hines on advancing the NEURON simulation environment, including its porting to multiple platforms and integration with real-time dynamic clamp systems. His research emphasizes understanding neuronal structure-function relationships, particularly in hippocampal and olfactory bulb neurons. He has developed educational tools and short courses to disseminate simulation methodologies. Key contributions include the Electrotonic Workbench, ModelDB database, and the Neuroscience Gateway, promoting open-access computational resources. His work bridges theoretical models with experimental neuroscience, advancing drug screening methods and understanding neural network dynamics.
Dr. Quoc Cuong Ngo is a Research Fellow in the School of Engineering at RMIT University, specializing in biomedical engineering and machine learning applications in healthcare. His research focuses on neurological disorders, particularly Parkinson’s disease, using advanced techniques such as facial expression analysis, speech assessment, and EEG signal processing. He also investigates medical imaging for conditions like leg ulcers and sleep apnea, contributing to automated diagnostic tools and healthcare technology innovations. Active in supervision, he mentors students on projects involving AI-based clinical symptom analysis and biometric authentication. His work bridges computer science and medicine, emphasizing interdisciplinary collaboration. Notable projects include developing NeuroDiag software for handwriting-based Parkinson’s diagnosis and pioneering chatbot-driven vocal screening systems. Despite no explicitly listed awards, his extensive publication record highlights contributions to medical diagnostics and biomedical engineering.
Dr. Maayke Hunfeld is a researcher in the Department of Neurology at Erasmus MC, Rotterdam, specializing in pediatric neurocritical care. Her work focuses on improving outcomes for children after cardiac arrest, particularly through advanced neuroimaging and neurophysiological monitoring techniques. Her research interests span several interconnected domains: Pediatric cardiac arrest and resuscitation science Neurological prognostication using MRI and EEG Application of machine learning in predicting survival and recovery Long-term outcomes in pediatric neurocritical conditions such as traumatic brain injury and submersion injuries Development and validation of clinical prognostication guidelines The recent publications highlight a strong trend toward data-driven approaches in pediatric neurology, combining large observational datasets with advanced analytical methods. A significant portion of her work contributes to national and international efforts in standardizing care and outcome prediction in pediatric intensive care settings. Scientific contributions include: Co-leading studies in the PROGNOSE Group Collaborative Investigators Contributing to the development of a Dutch nationwide pediatric cardiac arrest registry Advancing neuromonitoring algorithms for traumatic brain injury Pioneering use of quantitative EEG with machine learning for survival prediction Dr. Hunfeld actively collaborates with multidisciplinary teams across Erasmus MC and national institutions, contributing to high-impact research in pediatric neurology and critical care. She has co-authored numerous peer-reviewed articles and doctoral theses, indicating active involvement in research training and academic mentorship. Her work is frequently published in leading journals such as Neurology , Pediatric Neurology , and Resuscitation Plus , and she is associated with open-access and data-sharing initiatives.
Matti Hämäläinen is a Professor at the Department of Neuroscience and Biomedical Engineering , Aalto University. He is a leading expert in Magnetoencephalography (MEG) , with a focus on sensor design, neural connectivity, and clinical applications. His work contributes to understanding brain disorders like autism and epilepsy. Doctorate in Materiaalifysiikka, Teknillinen Korkeakoulu (1989) Diplomi-insinööri in Teknillinen Fysiikka, Teknillinen Korkeakoulu (1983) Hämäläinen's research spans MEG technology , auditory and visual cortex dynamics , and functional connectivity analysis . He develops open-source tools like MNE-Python and HNN-Core for neural data interpretation. Scientific Awards : None explicitly mentioned. He has led projects such as NIH Scalable Software for MEG/EEG and Device-Independent Real-Time MEG EEG Source Localization , with media coverage in outlets including Massachusetts General Hospital and Targeted News Service.
Martijn Froeling serves as an Assistant Professor at University Medical Center Utrecht, actively contributing to the Precision Imaging research group within the High Field division. His work bridges advanced MRI technology development with clinical applications targeting critical health domains including brain cancer, circulatory health, dementia, and musculoskeletal disorders. His academic foundation includes: Master's in Biomedical Engineering from Eindhoven University of Technology (July 2009) PhD in Diffusion Tensor Imaging of the human forearm from Amsterdam University Medical Center and Eindhoven University of Technology (October 2012) Dr. Froeling's research centers on pioneering quantitative MRI methodologies, with specialized expertise in Diffusion Tensor Imaging (DTI) across multiple organ systems (brain, peripheral nerves, muscle, kidney, heart). He drives innovation in 7T MRI hardware development—including specialized coils for multi-nuclei imaging—and conducts clinical studies focused on neuromuscular diseases. His QMRITools software platform for Mathematica enables sophisticated quantitative MRI analysis, directly supporting his mission to 'see the unseen' for advancing clinical diagnostics in cancer, cardiovascular disease, stroke, and MSK conditions. Analysis of his 2024-2025 publications reveals a cohesive research trajectory: DTI applications dominate clinical studies (hamstring injuries, fasciculation mapping), while parallel technical work advances ultra-high-field hardware (double-tuned coils for ²H/³¹P imaging). This dual focus on clinical impact and technological innovation demonstrates his commitment to translating engineering breakthroughs into tangible medical solutions. No scientific awards were documented in the provided materials. While the text confirms Dr. Froeling's role in supervising PhD work (evidenced by his PhD completion under prominent supervisors), no current students or specific grant funding details are explicitly mentioned in the source material. He operates within the High Field group at University Medical Center Utrecht, leading the Precision Imaging research initiative. This team specializes in developing cutting-edge MRI hardware (particularly for 7T systems), maintaining the QMRITools analysis platform, and executing clinical trials targeting neuromuscular pathologies alongside broader applications in oncology and cardiovascular medicine.
Professor Maria Schweigel Prof. Maria Schweigel is a Professor at the Department of Autonomous Systems, Schmalkalden University of Applied Sciences. Her teaching focuses on automation control, electronic control systems, robotics, and embedded systems. She leads the research group 'Eingebettete Diagnosesysteme' (Embedded Diagnostic Systems) and specializes in artificial intelligence algorithms, optimization using genetic and evolutionary methods, and biosignal analysis for anesthesia and sleep studies. Her research spans robotics, embedded systems, image processing, and software development with tools like C/C++, MATLAB/Simulink, and LabView. Key projects include real-time EEG classification for anesthesia monitoring, low-SNR biomedical signal extraction, and SVM-based classification on resource-constrained platforms. She has contributed to international conferences like ECT and IWK TU Ilmenau, focusing on interdisciplinary applications in medical technology and autonomous systems. No scientific awards were explicitly listed in the provided materials. Her advising and grants are not detailed here, but her extensive publication record (2009–2016) demonstrates active involvement in collaborative research with colleagues like Wenzel, Walther, and Baumgart-Schmitt. She co-developed tools such as the Multi-EEG-Viewer and explored wireless biomedical signal transmission for relaxation control systems.
Pierre Mégevand is a Professor and group leader at the Faculty of Medicine, University of Geneva, where he leads the Human Neuron Lab. His research is centered on understanding the neural mechanisms underlying human cognition and epilepsy, using intracranial recordings from awake patients. He is actively involved in both clinical and basic neuroscience research, with a focus on speech, language, and seizure dynamics. His research interests span cognitive neuroscience, clinical neurophysiology, and neuroengineering. He investigates how neurons encode sensory stimuli and behavioral responses, particularly in the context of speech and language. His lab uses stereo-EEG and microelectrode recordings to identify seizure-specific neural patterns and develop patient-tailored biomarkers for seizure detection and prediction. Key areas include multisensory integration, functional brain networks in epilepsy, and neural decoding of imagined speech. His recent publications reflect a strong trend in human intracranial electrophysiology, with studies on audiovisual illusions, electrode localization tools, functional networks in epilepsy, and neural correlates of speech and emotion. His work combines clinical data with advanced computational methods to uncover the dynamics of human brain function. Among his scientific contributions are methodological advancements and clinical insights in epilepsy monitoring and treatment. Though no specific awards are listed, his publications in top journals like Nature Communications and Epilepsia indicate high recognition in the field. Mégevand advises graduate students and postdoctoral researchers, including Lora Fanda and Jonathan Monney. His lab collaborates on grants related to neurotechnology, brain-computer interfaces, and epilepsy research, though specific grant details are not provided. He is part of a multidisciplinary team at the University of Geneva that includes engineers, neurologists, and neurosurgeons. The Human Neuron Lab, based at the Centre Médical Universitaire (CMU), is equipped for advanced human electrophysiology studies. The team includes a master assistant, graduate students, and postdoctoral researchers, reflecting an active and collaborative research environment focused on translational neuroscience.
Hillel Chiel is a Professor at Case Western Reserve University with joint appointments in the Department of Biology (College of Arts and Sciences), Department of Neurosciences (School of Medicine), Department of Biomedical Engineering (Case School of Engineering), and the Neural Engineering Center. His office is located in DeGrace Hall Room 304. Dr. Chiel leads a research laboratory focused on understanding how the brain and body generate adaptive behavior through neurophysiology, computational modeling, biomechanics, and robotics. Key research areas include: Neural control of feeding in Aplysia californica using MRI and 3D modeling Biologically-inspired robotic systems mimicking animal mechanics Wireless neural recording/stimulation techniques Infrared neural inhibition and thermal modulation of neural activity Neuromechanics of multifunctional behaviors His recent publications (2019-2021) demonstrate strong interdisciplinary focus on: computational neuroscience, infrared neural modulation techniques, biomechanics of invertebrate feeding systems, biomimetic robotics, and neuroprosthetic applications. Research consistently integrates experimental neurobiology with engineering principles.
Cara E. Stepp is a Professor at Boston University, with appointments in the Speech, Language & Hearing Sciences , Biomedical Engineering , and Otolaryngology – Head and Neck Surgery departments under the Sargent College of Health & Rehabilitation Sciences . Her research focuses on applying engineering tools to rehabilitate sensorimotor disorders of voice and speech, aiming to develop novel therapeutic interventions for conditions like Parkinson’s disease and laryngeal dystonia. Education : S.B. in Engineering Science from Smith College (2004), S.M. in Electrical Engineering and Computer Science from MIT (2008), Ph.D. in Biomedical Engineering from Harvard-MIT Division of Health Sciences and Technology (2009), and postdoctoral training in Computer Science & Engineering and Rehabilitation Medicine at the University of Washington (2011). Dr. Stepp’s research spans sensorimotor rehabilitation engineering , speech motor control , and neurotechnology development . She investigates acoustic and kinematic parameters in voice disorders, cortical biomarkers for motor control, and videogame-based rehabilitation tools. Her work integrates disciplines like biomedical instrumentation, signal processing, and neural dynamics. Key scientific awards include: Presidential Early Career Award for Scientists and Engineers (2019) Fellow of the American Speech-Language-Hearing Association (2018) NSF CAREER Award (2015) Early Career Contributions in Research Award (2012) Peter Paul Career Development Professorship (2012) AIMBE Fellow (2024) As director of the STEPP LAB , she mentors PhD and Master’s students in projects involving Parkinson’s disease, gender-affirming voice care, and computational modeling. The lab fosters a multidisciplinary, inclusive environment with collaborators from engineering, neuroscience, and clinical medicine.
Dr. Nigel Rogasch is an ARC Externally-Funded Research Fellow at the University of Adelaide's School of Biomedicine within the Faculty of Health and Medical Sciences. He is affiliated with the SAHMRI (South Australian Health and Medical Research Institute) and the Adelaide Health & Medical Sciences Building (AHMS). His primary research focuses on combining transcranial magnetic stimulation (TMS) with neuroimaging techniques (EEG, MRI) to investigate brain dynamics, plasticity, and their roles in healthy and disordered cognition. Research Interests: Understanding mechanisms of working memory and short-term memory Developing TMS-EEG methods to study cortical networks Exploring excitation/inhibition imbalances in schizophrenia and other mental illnesses Modeling how brain stimulation interacts with cortical circuits His work emphasizes translational applications of brain stimulation in clinical settings, such as treating aphasia and autism spectrum disorder. He actively supervises Honours and HDR students in cognitive neuroscience, neurophysiology, and engineering-related fields. Labs & Teams: Brain stimulation, imaging and cognition group at SAHMRI and AHMS.
Nuno Barbosa-Rocha, PhD is an Associate Professor and Vice-President at the School of Health of Polytechnic of Porto (ESS | P.PORTO). He serves as a Senior Researcher (integrated member) at NeuroLab and Director of the Center for Translational Health and Medical Biotechnology Research (TBIO). His research focuses on cognitive/social dysfunctions in mental disorders, leveraging behavioral methods and brain stimulation for intervention development. He pioneers scalable digital solutions for translational health, emphasizing personalized interventions and cognitive monitoring through data-driven approaches. Education: PhD in Psychology from the University of Porto (2012). His work bridges clinical neuroscience and technology, addressing mental health challenges through innovative solutions like EEG sensors for sleep studies and video-based stress monitoring systems. He actively explores the impact of digital media on child development and workplace well-being, with cross-national studies on psychosis-like experiences contributing to global mental health research. Research Interests: Cognitive remediation, digital health technologies, neurophysiological interventions, and occupational health strategies. His lab develops unobtrusive sensors and AI models for real-world health applications, advancing personalized care paradigms. Key innovations include wearable EEG devices and multimodal stress detection systems. Labs/Teams: Directs TBIO Center and collaborates with NeuroLab. Active in translational research bridging academia-industry partnerships. His work emphasizes ethical AI implementation and scalable healthcare solutions.
Alvaro Joffre Uribe Quevedo is an Associate Professor in the Game Development and Interactive Media department at the Faculty of Business and Information Technology, Ontario Tech University. He holds a PhD in Mechanical Engineering from the Universidade Estadual de Campinas (Brazil) and has conducted postdoctoral research at the University of Waterloo's Games Institute. His work focuses on Virtual Reality (VR), serious games, and 3DUI, emphasizing medical applications like surgical training simulations and exer games for healthcare. Education: Postdoctoral Fellow, University of Waterloo (2016) PhD in Mechanical Engineering, Universidade Estadual de Campinas (2011) MSc in Mechanical Engineering, Universidade Estadual de Campinas (2008) Bachelor's in Mechatronics Engineering, Universidad Militar Nueva Granada (Colombia, 2003) Research Interests: Dr. Quevedo's research explores immersive technologies' role in healthcare, including VR for physiotherapy, surgical training, and dementia care. He investigates how serious games and 3DUI design enhance engagement and skill retention, particularly in medical contexts. Recent work includes VR simulations for cardiac auscultation, epidural insertion, and radiation visualization in nuclear engineering. Articles Trends: His publications emphasize VR's applications in medical education (e.g., surgical simulations), exergames for elderly populations, and neurophysiological studies of immersion. Recent studies focus on haptic feedback, EEG-based biomarkers, and UI design for surgical training. Grants & Collaborations: Recipient of grants from Universidad Militar Nueva Granada (Colombia) for VR medical projects. Collaborations include interdisciplinary teams in healthcare, robotics, and nuclear engineering. Labs & Teams: Member of the Software and Informatics Research Centre (SIRC) at Ontario Tech University, focusing on VR, exergames, and medical simulations.
Charles S. Peskin is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He specializes in mathematical modeling and simulation of biological systems, particularly cardiovascular physiology and neurophysiology. His work focuses on the Immersed Boundary Method, a computational framework for fluid-structure interaction problems. Peskin has authored influential textbooks such as Modeling and Simulation in Medicine and the Life Sciences (2nd ed., 2002) and developed simulation tools used in biomechanics research. His research spans mathematical biology, computational fluid dynamics, and stochastic processes in biological systems. He advises PhD students on interdisciplinary projects at the interface of mathematics and life sciences. Key contributions include modeling heart valve dynamics, neural signaling mechanisms, and biomolecular motor systems. Affiliations: Courant Institute, NYU Research: Immersed Boundary Method, Cardiac Mechanics, Neurophysiology Modeling Publications: 100+ articles, 2 books Teaching: Courses on PDEs in Biology, Biomolecular Motors, and Simulation Techniques Software: MATLAB programs for cardiovascular system simulations His work bridges applied mathematics with biomedical engineering, emphasizing computational methods for complex biological systems. Recent work includes studies on entropy principles in biology and rotary molecular motor mechanisms.
Mario Nadj is a full Professor holding the Chair for Information Systems and Artificial Intelligence (AI) in Marketing at the University of Duisburg-Essen's Faculty of Computer Science. Previously, he served as an assistant professor and chairholder for Business & Information Systems Engineering at TU Dortmund University and held two deputy professorships at the Karlsruhe Institute of Technology (KIT), where he was also head of the Intelligent Enterprise Systems department. Bachelor's degree in Information Systems from the University of Bamberg Master's degree in Finance and Information Management from Goethe University Frankfurt Doctorate from KIT with highest praise (summa cum laude) Professor Nadj's research spans the intersection of artificial intelligence, information systems, and cognitive science. His work primarily focuses on three interconnected areas: understanding information systems through AI and neuroscience tools, investigating explainability of AI decisions to open the 'black box,' and exploring effective human-AI interaction paradigms. His research integrates methods from neurophysiology and cognitive psychology to study workplace phenomena like flow states and cognitive load in knowledge workers. His publication portfolio shows a clear trend toward human-centered AI systems, with recent work emphasizing interactive explainable AI, physiological computing for flow detection, and human-in-the-loop machine learning systems. The research spans disciplines including information systems, human-computer interaction, cognitive psychology, and data science, with a strong emphasis on empirical validation through laboratory experiments and eye-tracking studies. Vinton G. Cerf Best Student Paper Award at the International Conference on Design Science Research in Information Systems and Technology (2025) Judges' Award for the Accessibility Challenge of the International Web for All Conference (2023) Best Demo Paper Award of the International Conference on Advanced Information Systems Engineering (2021) Business Intelligence Systems among the eleven best rated lectures at KIT Department of Economics and Management (WS 2020/2021) Teaching Award of the Hector School of Engineering & Management - 2nd Place (Intake 2019) Professor Nadj's research is supported by major German companies from the automotive and software industries. His work on interactive labeling systems, physiological computing for workplace flow, and explainable AI has attracted significant industry interest. He collaborates extensively with researchers across Europe, particularly with Alexander Maedche and colleagues at the University of Mannheim. His projects often bridge theoretical information systems research with practical applications in business intelligence, data science, and human-AI collaboration. His research group at Duisburg-Essen focuses on neuro information systems, combining traditional information systems research with neuroscience methodologies. The team has developed tools like brownieR, an R-package for neuro information systems research, and has conducted pioneering work on using EEG to detect flow states in knowledge workers. Current projects emphasize making AI systems more transparent and interactive while optimizing human-AI collaboration in business contexts.
Jaime dos Santos Cardoso is a Full Professor at the Department of Electrical and Computer Engineering (DEEC) in the Faculty of Engineering of the University of Porto (FEUP), Portugal. Simultaneously, he is involved in research and development activities at INESC TEC in the Centre for Telecommunications and Multimedia, where he co-founded the Breast Research Group and the Visual Computing and Machine Intelligence (VCMI) Group. His research focuses on computer vision, pattern recognition, and machine learning with applications in medical decision support systems, biometrics, and visual information processing. University of Porto, Faculty of Engineering (2006-present) INESC TEC, Centre for Telecommunications and Multimedia (current) Co-founder of Visual Computing and Machine Intelligence (VCMI) Group Ph.D. in ECE (Computer Vision) from University of Porto (2006) Professor Cardoso's research interests span computer vision, machine learning, deep learning, medical image analysis (particularly breast and cervical cancer), biometrics, and medical decision support systems. His work emphasizes interpretable models, data-efficient learning approaches, and tackling challenges like class imbalance and ordinal data in visual information processing. The VCMI group under his leadership pursues never-ending visual information learning systems to empower intelligent systems with visual reasoning capabilities. His research has significant applications in medical imaging, particularly in breast cancer screening and diagnosis, breast cancer surgery planning and evaluation, and cervical cancer screening. The group develops Computer-Aided Diagnosis (CAD) systems that can be integrated into clinical workflows to improve detection, diagnosis, and treatment planning. World's Top 2% Scientists recognition Extensive publication record in top-tier computer vision and medical imaging conferences and journals Leadership in multiple research projects including AI-RBD, AI-Care4U, OBJECT, and AI4Lungs Professor Cardoso has supervised numerous PhD and Master's students, with many completing their degrees under his guidance. His students have worked on diverse topics including breast cancer aesthetic assessment, multimodal biometrics, medical image analysis, and deep learning applications in healthcare. He has received research funding for various projects focused on applying machine learning to medical challenges, particularly in oncology and medical imaging. The Visual Computing and Machine Intelligence (VCMI) group, which he co-founded, has evolved significantly since 2011, becoming the main research group within the Information Processing and Pattern Recognition (IPPR) Area at CTM. The group maintains a balance between senior and junior researchers, fostering a collaborative environment for advancing research in visual computing and machine intelligence.
