Antonio Krüger is a Professor at Saarland University and the CEO & Scientific Director of the German Research Center for Artificial Intelligence (DFKI). He leads the Ubiquitous Media Technology Lab and the Cognitive Assistance Systems department at DFKI, specializing in human-machine interaction, artificial intelligence, and intelligent user interfaces. University: Saarland University Department: Ubiquitous Media Technology Lab Organizational Role: CEO & Scientific Director, DFKI Research Interests Human-Machine Interaction User Modeling Cognitive Sciences Ubiquitous Computing Publications (2025) span disciplines including neuroscience, VR, and federated learning, focusing on topics like EEG-based BCIs, immersive plant trait analysis, and long-term mental modeling for well-being. Contact: ceo@dfki.de
Prof. Dr. Matthias Krauledat is a faculty member at Hochschule Rhein-Waal , specifically within the Faculty of Technology and Bionics . His academic career spans both theoretical research and industrial application, with a focus on Machine Learning and Brain-Computer Interfaces . After completing his PhD in Electrical Engineering/Computer Science at Technische Universität Berlin , he has contributed significantly to the advancement of EEG-based communication systems and neural signal processing methodologies. Born in Essen, Germany Studied Mathematics with a minor in Computer Science at University of Münster/Oxford Doctoral research at TU Berlin on Brain-Computer Interfaces Industrial experience at Henkel AG & DMT GmbH Research Interests focus on Machine Learning applications in Neuroscience and Biomedical Engineering , specifically Brain-Computer Interfaces , EEG Signal Processing , and Adaptive Classification Systems . His work explores how algorithms can be developed to enable self-learning computers to solve complex tasks involving neural data interpretation and prediction for previously unseen data in clinical and technological contexts. Publications demonstrate a consistent contribution to Neuroscience and Machine Learning fields, with particular emphasis on Brain-Computer Interface systems from 2004 through 2009. His research has focused on reducing training requirements, improving signal processing accuracy, and developing novel interaction paradigms like the Hex-o-Spell mental typewriter while addressing statistical challenges like covariate shift in neural data analysis. Professional Experience includes academic research at TU Berlin's Intelligent Data Analysis group, industrial software development roles at Henkel AG's Scientific Computing department, and TÜV Nord Group's Optical Metrology and Machine Diagnostics divisions. He maintains active research connections through collaborative publications with leading experts in the field.
Surjo R. Soekadar is the Einstein Professor of Clinical Neurotechnology at Charité – University Medicine Berlin. He leads the Clinical Neurotechnology Laboratory , which focuses on developing noninvasive neurotechnologies for treating neurological and psychiatric disorders through closed-loop brain stimulation and advanced brain-machine interfaces (BCI/BMI). His work integrates real-time EEG/MEG monitoring with electromagnetic stimulation to modulate pathological brain oscillations and enhance neuroplasticity in conditions like stroke, spinal cord injury, and psychiatric disorders. Education : Studied medicine in Mainz, Heidelberg, and Baltimore Clinical Training : Residency in Psychiatry and Psychotherapy at University of Tübingen Academic Journey : 2008-2011 Research Fellow at NINDS (USA); 2017 Venia Legendi at University of Tübingen; 2018 First Professor of Clinical Neurotechnology in Germany His research interests span: • Closed-loop neurostimulation combining real-time brain state monitoring with targeted intervention • Next-generation BCI using optically pumped magnetometers (OPM) for mobile MEG recordings • Neurorehabilitation through exoskeleton control and sensory feedback • Neurophysiological modeling of entropy measures and phase flows Recent publications highlight: • Adaptive deep brain stimulation protocols • Real-time phase-sensitive tACS applications • OPM-based BCI innovations • Stroke recovery mechanisms through corticospinal tract analysis Scientific recognition includes: International BCI Research Award BIOMAG Award NARSAD Young Investigator Award Funded by the European Research Council (ERC) , his lab trains doctoral students like David Haslacher (EEG/MEG integration), Khaled Nasr (multicoil TMS optimization), and Annalisa Colucci (entropy-driven BCI development). The team also explores quantum AI applications in clinical decision-making and bidirectional BCI systems using OPM and tES.
Professor Gordon Cheng is a faculty member at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology (CIT). He serves as the Director of the Chair of Cognitive Systems at the Institute for Cognitive Systems (ICS), where he focuses on advancing cognitive systems, robotics, and their intersections. His research addresses fundamental challenges in creating intelligent systems capable of understanding and interacting with complex environments. Research Interests: His work spans Neuroengineering , Robotics , and Cognitive Systems , emphasizing the integration of biological principles into artificial systems. Key projects include robot skin development , EEG-based prosthetic control , and human-robot co-adaptation . Selected Publications: Recent articles highlight advancements in real-time tactile sensing , neuroprosthetics , and BCI-driven rehabilitation , reflecting his interdisciplinary approach bridging robotics and neuroscience. Supervision: He supervises PhD students from the Graduate School of Neuroscience (GSN), including Jasmin Kajopoulos and Nikolas Berberich. Contact: Email: gordon@tum.de | Website
David B. Grayden is a Professor at The University of Melbourne, affiliated with the Melbourne School of Engineering and the Department of Electrical and Electronic Engineering . His work spans Biomedical Signal Processing , Computational Neuroscience , and Brain-Computer Interfaces (BCI) , focusing on applications in Epilepsy Research and Cochlear Implants . Melbourne Neural Engineering Laboratory member Collaborator in multidisciplinary biomedical research Key research interests include: Developing Seizure Prediction Algorithms using long-term EEG/iEEG data Neural mass modeling for Epilepsy and Inhibitory Network Behavior Optimizing Cochlear Implants via computational models Advancing Endovascular BCI Systems and Neural Stimulation Recent publications highlight trends in Machine Learning , Path Signatures , and Multi-Frequency Stimulation for SSVEP-based BCIs . His work integrates Computational Modeling with Biomedical Engineering to address clinical challenges in neuroprosthetics and sensory processing. Grayden leads projects on Neural Network Dynamics , Biomedical Signal Analysis , and Neurostimulation , often collaborating with institutions like Monash University and Royal Melbourne Hospital .
Dr. Yomna Abdelrahman is a Professor of Usable Security and Privacy at the Bundeswehr University Munich . Her research focuses on thermal imaging for security and privacy , virtual reality usability, eye tracking , and biometric authentication . She collaborates with researchers like Florian Alt and Albrecht Schmidt on projects exploring how thermal sensing can enhance user identification and privacy awareness . Key Research Areas : Thermal Imaging, Human-Computer Interaction, Virtual Reality, Password Security, Biometric Authentication, Eye Tracking, Privacy Her recent publications address VR emotion detection , password usability , and privacy implications of thermal imaging . She has contributed to conferences like CHI, MUM, and INTERACT, often examining security-privacy trade-offs and non-invasive biometric systems . Notably, her work on thermal attacks reveals vulnerabilities in mobile authentication through thermal residue. She holds a PhD in Computer Science from the University of Stuttgart (2018), where her thesis, "Thermal Imaging for Amplifying Human Perception," laid the foundation for her later work on thermal-based interaction and cognitive load estimation . Her collaborations span diverse domains, from smart home notifications to industrial worker assistance , reflecting her interdisciplinary approach to usable security and interactive systems .
Stefan Ehrlich is a Researcher at the Institute for Cognitive Systems (ICS) at the Technical University of Munich (TUM). His work focuses on neuroengineering, particularly non-invasive brain-computer interfaces (BCI) for human-robot interaction (HRI), neuroadaptive systems, and EEG-based neurotechnology. He collaborates closely with Prof. Gordon Cheng and contributes to projects involving error-related potentials (ErrPs), affective neurofeedback, and neuroprosthetics. His research integrates cognitive neuroscience principles with robotics to enhance human-machine collaboration and adaptive systems. Key research areas include: Development of passive BCI systems for real-time robotic adaptation Design of co-adaptive human-agent interfaces using neural signals EEG-based assessment of exoskeleton and humanoid robot performance Neuroimaging techniques for music-brain interactions His publications emphasize interdisciplinary approaches, combining neuromorphic engineering, machine learning, and social robotics. Ongoing work explores low-power neuromorphic hardware for EEG decoding and neurophysiological mechanisms underlying human-robot trust dynamics. Collaborations involve institutions like the Bernstein Center for Computational Neuroscience and industry partners in wearable robotics. Labs/Teams: Active contributor to the EEG Laboratory and projects like the Soft Wearable Robotics initiative. Engages in international conferences (IROS, IEEE EMBC) and co-organizes workshops on HRI and neurotechnology.
Ali Hassan is a researcher affiliated with the National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science, Department of Computer and Software Engineering. His work spans multiple domains in computer science, engineering, and applied mathematics, focusing on areas such as machine learning, IoT, energy systems, and medical informatics. His research explores: Reinforcement learning applications for battlefield information systems IoT antenna design and performance evaluation Optimization of second-life battery systems in electric vehicles Transformers for real-time vehicle collision avoidance Image hashing techniques using visual attention models Neural network-based phasor estimation for power grids Biomedical sensor systems for non-invasive health monitoring Mathematical modeling of viral dynamics Recent publications demonstrate his emphasis on interdisciplinary approaches combining AI, signal processing, and sustainability. He has collaborated with institutions across Pakistan, France, Saudi Arabia, and the USA, with a focus on practical implementations in cybersecurity, energy optimization, and healthcare technology.
Tim Düwel is a Lecturer at the Ubiquitous Media Technology Lab (UMTL) within Saarland University's Saarland Informatics Campus. He is affiliated with the Cognitive Assistants department at the German Research Center for Artificial Intelligence (DFKI).
Mina Jamshidi Idaji is a Postdoctoral Researcher at the Machine Learning Group, BIFOLD – Berlin Institute for the Foundations of Learning and Data, Technical University of Berlin. Her research focuses on AI-driven healthcare applications, including computational pathology, ophthalmology, and biomedical sensing. She holds a Dr.-Ing. (PhD) in Machine Learning from TU Berlin and the Max Planck Institute CBS, Leipzig (2022), an M.Sc. in Biomedical Engineering from Sharif University of Technology (2016), and dual B.Sc. degrees in Electrical Engineering and Mathematics from Isfahan University of Technology (2014). Her research interests span AI for healthcare, computational pathology, and biomedical engineering. Recent work includes developing explainable AI frameworks for histopathology (xMIL), EEG/MEG simulation tools (MEEGsim), and methods for nonlinear interaction decomposition (NID). She has contributed to open-source projects like Harmoni and xMIL, addressing challenges in neural data analysis and clinical AI applications. Mina’s publications highlight advancements in brain-computer interfaces (BCI), motor imagery studies, and neuroimaging techniques. Her work bridges machine learning and clinical neuroscience, emphasizing translational research in healthcare AI.
Nikolai Kapralov is a Researcher at the Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology. He is affiliated with the Neural Interactions and Dynamics and Somatosensory Group research groups. His work focuses on neural network dynamics, brain-computer interfaces (BCI), and EEG-based systems. Education: Doctoral researcher at the International Max Planck Research School NeuroCom . Research interests: Neural network dynamics and working memory mechanisms Development of sensorimotor BCI systems EEG pattern classification for motor imagery applications Optimization of BCI performance using multiverse analysis Publications: Focus on BCI innovation, EEG analysis, and interdisciplinary research in nanotechnology/materials science (2013-2024). Awards: No honors explicitly mentioned in the provided texts. Advising/Grants: No advising roles or grants documented here. Labs/Teams: Active member of the Neurology Department's research groups at the Max Planck Institute.
Dr. Michael Tangermann is an Associate Professor at the Donders Institute for Brain, Cognition and Behaviour, Radboud University. Previously, he led the Brain State Decoding Lab at the University of Freiburg (2013–2020), focusing on machine learning for decoding mental states from neuronal signals. His research addresses challenges in signal-to-noise ratio, high dimensionality, and non-stationarity in EEG/MEG data, with applications in Brain-Computer Interfaces (BCIs), neurorehabilitation, and human-robot interaction. He holds a PhD from the University of Tübingen (2005) and was a postdoc at TU Berlin and Fraunhofer FOKUS. Education: PhD in Computer Engineering, University of Tübingen, 2000–2005 Postdoc, TU Berlin (Machine Learning Group), 2007–2013 Researcher at Fraunhofer FOKUS, 2005–2017 Research Interests: Machine learning, BCI development, neural signal processing, rehabilitation engineering, and adaptive systems. His work emphasizes real-time applications, including stroke rehabilitation, closed-loop deep brain stimulation, and auditory BCI paradigms. Labs/Teams: Current affiliation with the Donders Institute. Previously, led the Brain State Decoding Lab (Freiburg) within the BrainLinks-BrainTools excellence cluster. Collaborates with institutions like the Berlin Brain-Computer Interface group and Fraunhofer FIRST/FOKUS.
Aurélie Modde serves as a Researcher in the Department of Clinical Child and Adolescent Psychology and Psychotherapy at Free University of Berlin, actively contributing to the third-party funded STARK project (Spielerische Therapieunterstützung mit adaptivem Realitätsgrad für Kinder) since 2025. Concurrently, she is pursuing dual clinical qualifications: additional training as a child and adolescent psychotherapist at Zentrum für seelische Gesundheit der Freien Universität Berlin (since 2023) and psychological psychotherapy certification in behavioral therapy at Humboldt University Berlin (since 2022). Her academic foundation includes: Master of Science in Clinical Psychology from Julius-Maximilians-Universität Würzburg (2019-2021), thesis: "Von der Nützlichkeit eines interaktiven Forums für Brain Computer Interface Nutzende – Eine qualitative Interviewstudie" Bachelor of Science in Psychology from Julius-Maximilians-Universität Würzburg (2015-2019), thesis: "Ein Vergleich verschiedener Erfassungsmöglichkeiten von Rechtschreibkompetenz im Grundschulalter" Undergraduate exchange at University of New Mexico, USA (2017-2018) Modde's research centers on gender identity development and dysphoria in youth, with significant contributions to trans and non-binary adolescent mental health through projects like DEXTRA (Daily Experiences of Trans Adolescents), TRANS*PARENT, and GenderJourney Youth Compass. Her work uniquely bridges clinical psychology with neurotechnology, particularly in Brain-Computer Interface applications and interprofessional education frameworks. Publication analysis reveals an interdisciplinary trajectory: her 2025 BCI forum study demonstrates user-centered neurotechnology design principles, while the 2021 interprofessional education review establishes foundational models for collaborative psychology training. Both works exemplify her methodological versatility across qualitative, systematic review, and clinical intervention paradigms. No scientific awards are documented in available records. Modde currently contributes to the STARK project's development of adaptive reality therapy tools for children, though no student supervision roles are indicated. Her practical experience spans clinical placements at Fliedner Klinik Berlin (DBT unit) and PINEL Netzwerk, alongside research roles at Charité Universitätsmedizin Berlin and University of Adelaide. She operates within the Youth Advisory Board team at the University Outpatient Clinic, collaborating on gender identity research while participating in clinic partnerships focused on trans youth mental health services and parental support frameworks.
Anthony N. Burkitt is a Professor at the University of Melbourne, affiliated with the Department of Electrical and Electronic Engineering within the Faculty of Engineering and Information Technology. He has established himself as a leading researcher in computational neuroscience, neural engineering, and brain-computer interfaces, with a career spanning over three decades of continuous research and publication. His research focuses on computational modeling of neural systems, with particular emphasis on spike-timing-dependent plasticity, neural network dynamics, and applications to neural prosthetics and epilepsy research. Dr. Burkitt's work bridges theoretical neuroscience with practical applications in neural engineering, particularly in the development of brain-computer interfaces and retinal prostheses. His laboratory has made significant contributions to understanding neural coding mechanisms and developing computational models that integrate cellular and network levels of neural organization. Dr. Burkitt maintains an exceptionally active research program with numerous high-impact publications in 2023-2024. His recent work demonstrates a strong focus on seizure prediction algorithms, neural mass modeling, and advanced machine learning approaches for neural signal processing. His publications reveal a consistent pattern of addressing fundamental questions in neural computation while maintaining relevance to clinical applications in epilepsy and neural prosthetics. Major Research Themes: Computational modeling of neural networks and plasticity Development of brain-computer interfaces (BCIs) Epilepsy research and seizure prediction Retinal prostheses and visual system modeling Neural signal processing and analysis Integration of neural modeling frameworks Dr. Burkitt has mentored numerous PhD students and early-career researchers who have become established scientists in computational neuroscience. His laboratory maintains strong collaborative ties with clinical researchers and engineers working on neural interface technologies, ensuring that theoretical advances translate into practical applications. His consistent publication record spanning from the 1990s to the present demonstrates remarkable scientific productivity and sustained intellectual leadership in his field.
Ulrik Beierholm is an Associate Professor in the Department of Psychology at Durham University, with affiliations in the Biophysical Sciences Institute and the Durham Research Methods Centre. He previously held positions at the University of Birmingham’s Centre for Computational Neuroscience and Cognitive Robotics. His research lies at the intersection of psychology, neuroscience, and machine learning. His research focuses on how the human brain processes uncertainty in perception, decision-making, and learning. He employs Bayesian inference and reinforcement learning models to understand human behavior, validated through psychophysics , fMRI , and pharmacological methods. Key areas include multisensory integration , causal inference , perceptual clustering , and behavioral vigor . His work often explores developmental and aging effects on perception. His recent publications reveal a strong trend in modeling multisensory perception under uncertainty, with increasing focus on open-source tools (e.g., BCI Toolbox) and educational outreach (e.g., Neuromatch Academy). Themes include reliability-weighted cue integration, dopamine’s role in motivation, and developmental changes in perceptual strategies. Facebook Faculty Award - Virtual Reality (2016) Leverhulme Trust Grant (£250k) for 'Learning to perceive and act under uncertainty' (2017) Tubingen-Durham Joint Seedcorn Fund for 'The effects of mood on effort allocation during uncertainty' (2019) Dr. Beierholm has supervised PhD students and research assistants such as Nathanael Larigaldie, Denise Foresteire, and Laura Bird. He has secured competitive grants from the Leverhulme Trust and joint university funds, supporting projects on uncertainty, effort allocation, and multisensory perception. His collaborative network spans institutions in the UK, Germany, and the US. He co-organizes research workshops including the Probabilistic Brain Workshop and Computational Models of Social Interaction , and is involved in the Biophysical Sciences Institute Executive Board . His lab conducts behavioral experiments on multisensory integration, equipped with a soundproof room, projector, and 17-speaker array.
