جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Human-Computer Interaction
• User Modeling
• +۵ مورد دیگر

Cristina Conati is a Professor at the University of British Columbia, Canada, with an extensive research portfolio spanning artificial intelligence, human-computer interaction, and educational technology. Her work focuses on developing intelligent systems that adapt to user characteristics and behaviors, particularly through the analysis of eye-tracking data and other implicit user signals. Her research interests include user-adaptive visualizations, intelligent tutoring systems, explainable AI, and eye-tracking analysis. She investigates how AI can be used to personalize user experiences, particularly in educational contexts, by modeling user cognitive abilities, emotional states, and learning behaviors. Her work often bridges theoretical AI research with practical applications in education and decision support. Analysis of her recent publications (2023-2025) shows a strong focus on explainable AI in educational contexts, adaptive visualization techniques, and the use of eye-tracking to understand and support user interactions. Her research demonstrates a consistent trajectory toward making AI systems more transparent, adaptive, and effective for diverse users across various domains including education, healthcare, and decision-making. Extensive work on user modeling through eye-tracking data Development of adaptive visualization techniques Research on explainable AI for educational applications Studies on how individual differences affect user-system interaction Her research has significant implications for designing more effective educational technologies, decision support systems, and user interfaces that can adapt to individual user needs and characteristics. She has been instrumental in advancing the field of user-adaptive interfaces through both theoretical contributions and practical implementations.

• Gaze-based Interaction
• Eye Tracking
• Mobile Eye Tracking
• +۵ مورد دیگر

Dr. Michael Johannes Barz is an Associated Member at the Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) in Saarbrücken, Germany, where he conducts research within the Ubiquitous Media Technology Lab (UMTL). His work bridges human-computer interaction with artificial intelligence, focusing on gaze-based interaction, eye tracking, and interactive machine learning systems. Dr. Barz has established himself as a significant contributor to the field with over 50 publications spanning from 2015 to 2024. His research interests center on gaze-based interaction , mobile eye tracking , user modeling , and interactive machine learning , with recent work expanding into cognitive load measurement using digital pen technology and mixed reality applications for industrial training. Dr. Barz has developed influential tools like IMETA for eye tracking annotation and pEncode for visualizing pen signals, demonstrating his commitment to creating practical research methodologies. Dr. Barz's publication record shows consistent contributions to top-tier conferences including ACM ETRA, IEEE VR, and the International Conference on Intelligent User Interfaces. His 2022-2024 work reveals increasing focus on making AI systems more transparent and interactive, with publications on explaining machine learning model explanations and interactive deep learning frameworks. His research often involves interdisciplinary collaboration across computer science, cognitive science, and educational technology. Special recognition for an outstanding review at ACM ETRA 2020 Active conference reviewer for IJCAI, ACM IUI, KI, ACM ETRA, and IEEE VR Journal reviewer for Journal of Eye Movement Research Dr. Barz has contributed to teaching as an assistant for 'Intelligent User Interfaces' at TU Kaiserslautern and 'Artificial Intelligence' at Saarland University. His current research suggests strong engagement with both theoretical advancements in interactive machine learning and practical applications in educational and industrial contexts, particularly through the MASTER-XR project for mixed reality manufacturing training. The Ubiquitous Media Technology Lab provides the collaborative environment where Dr. Barz develops his innovative approaches to human-AI interaction.

• Clinical Psychology
• Cognitive Behavioral Therapy
• Obsessive-Compulsive Disorder
• +۳ مورد دیگر

Björn Elsner is a scientific collaborator at the Institute of Psychology , Humboldt University of Berlin , specializing in Clinical Psychology with a focus on Obsessive-Compulsive Disorder (OCD) and its treatment mechanisms. He works within the Faculty of Life Sciences , contributing to research on cognitive-behavioral therapy (CBT) efficacy, neuroimaging biomarkers, and psychological treatment optimization. Research Interests : Cognitive-behavioral therapy mechanisms, OCD treatment resistance, neural correlates of performance monitoring, emotional reactivity, epigenetic factors in OCD, and exposure therapy outcomes. Key Article Trends : Recent work (2020–2024) emphasizes OCD treatment predictors, neuroimaging-based outcome modeling, fear/safety learning dynamics, and the role of habituation and expectancy violation in therapy success. Collaborations span neuroscience, psychiatry, and psychopharmacology. Contact : bjoern.elsner@hu-berlin.de | Room 0'225, Rudower Chaussee 18, 10099 Berlin | Phone: (030) 2093-98766

Johannes Stegmaierمشاهده پروفایل

استادیار

• Biomedical Image Processing
• Microscopy Image Analysis
• Computer Vision
• +۷ مورد دیگر

Johannes Stegmaier serves as Junior Professor for Biomedical Image Processing at the Institute of Imaging and Computer Vision at RWTH Aachen University, where he has been employed since April 2018. Previously, he held an Acting Professor (W3) position at the same institution from October 2022 to September 2024. His academic journey includes postdoctoral research at Caltech's Center for Advanced Methods in Biological Image Analysis (2016-2017) and at the Karlsruhe Institute of Technology (2015-2017). Stegmaier earned his Doctorate (Dr.-Ing.) from the Karlsruhe Institute of Technology in June 2016 with a dissertation titled 'New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty.' He completed his Master of Science in Bioinformatics and Systems Biology at the University of Freiburg (2009-2011) and his Bachelor of Science in Bioinformatics at the University of Tübingen (2006-2009). During his doctoral studies, he was a Short Term Scholar at the Janelia Research Campus (2014). His research focuses on developing advanced computational methods for biomedical image analysis, particularly in microscopy and medical imaging. Stegmaier's work bridges computer vision, machine learning, and biomedical applications, with significant contributions to diffusion models for image synthesis, 3D image analysis, and embryomics. His research has practical applications in cancer detection, retinal prosthetics, and developmental biology. Analysis of his recent publications reveals a strong trend toward applying diffusion models and transformer architectures to medical imaging challenges. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on generating realistic medical image data, improving segmentation accuracy, and developing multimodal analysis frameworks that integrate imaging with clinical data. Stegmaier actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record in high-impact journals including Nature Neuroscience, Nature Communications, and PLOS Computational Biology. His work demonstrates a consistent focus on solving practical biomedical imaging challenges through innovative computational approaches.

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۵ مورد دیگر

Thomas Kosch serves as Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. His research group Human-Computer Interaction for Scientific Software develops innovative interfaces for scientific applications, with laboratory facilities at Rudower Chaussee 25, 12489 Berlin-Adlershof and administrative correspondence via Unter den Linden 6, 10099 Berlin. His research spans Human-Computer Interaction, Virtual/Augmented Reality, Artificial Intelligence, and Neurophysiological Computing. Key interests include cognitive augmentation through EEG/EMG systems, motor learning with electrical muscle stimulation, large language model integration in UX workflows, and privacy implications of tracking technologies. His work bridges theoretical HCI frameworks with empirical validation through controlled experiments involving physiological measurements and immersive environments. Analysis of his 15 most recent publications (all 2025) reveals three dominant trends: (1) Critical examination of LLM limitations through prompt-hacking and deceptive design studies, (2) Neurophysiological validation of cognitive phenomena using multimodal sensing (EEG/eye-tracking), and (3) Application-driven XR solutions for veterinary care, navigation, and motor assessment. These works consistently employ rigorous user studies with quantitative behavioral metrics. No scientific awards are documented in the provided materials, though his publication volume in top-tier venues (e.g., CHI, UIST) indicates significant field contributions. Professor Kosch actively supervises doctoral candidates, with at least one PhD defense (Michael Piechotta, Dipl.-Bio-Inf.) scheduled for September 2025. His research group likely secures competitive grants supporting equipment-intensive projects involving EEG systems, VR setups, and physiological sensors, though specific funding sources aren't detailed. The Human-Computer Interaction for Scientific Software group operates as an interdisciplinary hub combining computer science, cognitive psychology, and domain-specific applications. Current projects involve developing open-source tools like MorphoHaptics for medical imaging, MIRAGE for fall hazard detection, and Senscon for physiological sensing integration in VR controllers.

• Human-Computer Interaction
• Cognitive Science
• Eye Tracking
• +۳ مورد دیگر

Jan Ehlers is affiliated with the Faculty of Health at Witten/Herdecke University. His research focuses on Human-Computer Interaction (HCI), Cognitive Science, Eye Tracking, and Biomedical Engineering. He has collaborated extensively with institutions like Bauhaus University Weimar and Ulm University. Key research interests include developing assistive technologies for mental workload assessment, designing interfaces for special needs populations (e.g., dyscalculia training systems), and exploring novel input methods such as eye tracking, head gestures, and wearable devices. His work bridges cognitive science principles with engineering solutions. Recent publications (2023-2025) investigate pupil dynamics in noisy environments, social VR interaction for older adults, and automated decision-making in dating apps using biometric data. His methodologies often combine experimental psychology with advanced sensor technologies. Ehlers' contributions span over 15 peer-reviewed articles in venues like CHI, ETRA, and INTERACT. His interdisciplinary approach integrates neuroscience, engineering, and computer science to create user-centered solutions for real-world challenges.

Monika Pötter-Nergerمشاهده پروفایل

استاد

• Parkinson's Disease
• Deep Brain Stimulation (DBS)
• Neurology
• +۳ مورد دیگر

Prof. Dr. med. Monika Pötter-Nerger is a Senior Physician and Medical Specialist in Neurology at the University Medical Center Hamburg-Eppendorf (UKE), affiliated with the Faculty of Medicine and the Department of Neurology within the Head and Neurocenter. Her work focuses on movement disorders, particularly Parkinson's disease, with expertise in deep brain stimulation (DBS), gait disorders, and neurophysiological mechanisms. She leads research on DBS efficacy, clinical outcomes, and neuroimaging applications for neurodegenerative diseases like progressive supranuclear palsy. Her contributions include guideline development for Parkinson's disease management and advancements in DBS programming and patient quality of life assessment. Research Interests: - Deep Brain Stimulation (DBS) for Parkinson's and movement disorders - Gait and motor dysfunction in neurodegenerative diseases - Neuroimaging (PET, MRI) and biomarkers for disease progression - Clinical trials evaluating DBS outcomes and therapeutic combinations - Neurophysiological mechanisms underlying PD symptoms Key Publications Trends: Her recent work emphasizes DBS optimization, gait modulation, and quality-of-life metrics for Parkinson's patients. She investigates imaging techniques (e.g., FDG-PET) for early disease detection and explores pharmacological and neurotechnological interventions to improve motor and non-motor symptoms. Awards: No specific awards listed, though her extensive publication record and clinical leadership reflect peer recognition. Grants & Advising: Active in clinical trials and translational research, though specific grant details are not provided in the text. Supervises students in neurology and movement disorder research. Labs/Teams: Collaborates with interdisciplinary teams in the Department of Neurology and affiliated centers like the Center for Molecular Neurobiology Hamburg (ZMNH).

• Educational Psychology
• Spatial Thinking
• Eye Tracking
• +۷ مورد دیگر

Dr. Benedict C. O. F. Fehringer is a Researcher at the Department of Psychology within the School of Social Sciences at the University of Mannheim. He is affiliated with Stefan Münzer's Lab and focuses on educational psychology, spatial thinking, and advanced psychophysiological measurement techniques like eye tracking and pupillometry. His work examines cognitive processes during learning and testing scenarios, particularly exploring how gaze patterns and pupillary responses reflect cognitive workload and spatial reasoning. Key research interests include adaptive teaching/learning procedures, bias awareness in education, and the diagnostic potential of eye tracking data. He has developed specialized tests such as the R-Cube-SR and R-Cube-Vis for spatial ability assessment, implemented in OpenSeame software. His interdisciplinary approach integrates machine learning and data mining to analyze educational interventions and classroom diversity effects. Recent publications emphasize retest effects in spatial cognition, cultural diversity metrics in classrooms, and the validation of bias awareness scales for teachers. His work bridges cognitive psychology, educational technology, and policy-relevant issues in multicultural education settings.

• Educational Data Mining
• Learning Analytics
• Cognitive Psychology
• +۵ مورد دیگر

Caitlin Mills is an Assistant Professor in the Department of Educational Psychology at the University of Minnesota's College of Education and Human Development. She also maintains affiliations with the University of Southern California's Keck School of Medicine (Division of Bioinformatics) and Harvard Medical School. Her research bridges educational psychology, cognitive science, and learning analytics. Her primary research interests focus on mind wandering detection during learning , educational data mining , learning analytics , and eye tracking technologies for educational purposes. She investigates how cognitive processes like attention, task-unrelated thoughts, and comprehension can be measured and enhanced through technological interventions. Her work increasingly addresses neurodivergent learners and inclusive educational technologies. Analysis of her recent publications shows a strong trend toward developing non-invasive methods for monitoring cognitive states during learning, particularly through webcam-based eye tracking and keystroke analysis. Her research spans multiple domains including virtual reality learning environments, automotive safety interfaces, and language-based cognitive assessment. She has made significant contributions to understanding how mind wandering impacts learning outcomes across diverse populations. Mills has served as co-editor for the Proceedings of the International Conference on Educational Data Mining (EDM 2025) and regularly publishes in top venues including the Journal of Learning Analytics (LAK), Educational Data Mining (EDM), and Cognitive Science. Her collaborative work includes major projects with researchers from USC, Harvard, and other institutions, focusing on developing technologies that can detect and respond to learners' cognitive states in real-time to improve educational outcomes. She has developed innovative approaches to measuring attentional states during learning tasks using various physiological and behavioral metrics.

Johanna Schwabمشاهده پروفایل

پژوهشگر

• Special Education
• Inclusive Education
• Autism Spectrum Disorders
• +۳ مورد دیگر

Johanna Schwab is a researcher at the Chair of Special Education IV - Pedagogy for People with Intellectual Disabilities within the Faculty of Human Sciences at the University of Würzburg. Her work focuses on inclusive education, autism spectrum disorders, and media literacy in special education contexts. Current affiliation: University of Würzburg (since 2019) Prior: Ludwig-Maximilians-Universität Munich (2017-2019) Research Interests: Dr. Schwab specializes in pedagogical approaches for students with intellectual disabilities, particularly examining teacher attitudes toward autism, inclusive schooling during pandemics, and digital storytelling in special education. Her projects include Bewegte inklusive Schule (movement in inclusive education) and CoTeach: Digital Storytelling in inclusive classrooms, with a focus on the Würzburg-Bielefeld Study on Teacher Professionalization for science instruction. She has investigated effects of school closures on special education during the Corona pandemic through multiple studies. Publications: Her recent work spans media psychology, suicide prevention in media, and social behavior analysis. Key topics include evolutionary media theory, facial emotion recognition algorithms, and digital communication dynamics.

Adrien Doerigمشاهده پروفایل

استاد مهمان

• Computational Neuroscience
• Cognitive Neuroscience
• Machine Learning
• +۵ مورد دیگر

Adrien Doerig is a Visiting Professor in the Department of Education and Psychology at Freie Universität Berlin, where he leads research in the Cognitive Computational Neuroscience Lab. He is also affiliated with the Bernstein Center for Computational Neuroscience. His work bridges cognitive science, computational modeling, and neural mechanisms of perception. Doerig's research focuses on the intersection of computational neuroscience and artificial intelligence, with particular emphasis on visual perception, consciousness, and the development of biologically plausible neural network models. His work explores how the brain processes visual information, with special attention to phenomena like crowding, feature integration, and the temporal dynamics of perception. He has made significant contributions to understanding the limitations of convolutional neural networks for modeling human vision and has pioneered work on topographic neural networks that better capture cortical organization. A key trend in Doerig's recent publications is the exploration of connections between language models and visual processing in the brain. His groundbreaking 2025 Nature Machine Intelligence paper demonstrated that large language model representations align closely with visual representations in the human brain, opening new avenues for using language-based AI in modeling biological visual processing. His work consistently challenges existing paradigms while developing more biologically plausible computational frameworks. Doerig teaches a wide range of courses including Master's in Cognitive Neuroscience, Probability and statistical modeling (both theoretical and practical with focus on ANNs and neuroimaging), Introduction to programming, Applied computational cognitive neuroscience, and specialized courses on vision, language, affective & social neuroscience, and consciousness. He also supervises graduate students through the Cognitive Computational Neuroscience Lab, which includes postdocs, predocs, master's students, and interns working on cutting-edge research questions at the intersection of cognitive science and machine learning.

Naser Al Madiمشاهده پروفایل

استادیار

• Eye Tracking
• Program Comprehension
• Human-Computer Interaction
• +۳ مورد دیگر

Naser Al Madi is an Assistant Professor of Computer Science at Colby College, where he teaches core courses including Data Structures and Algorithms (CS231) and Software Engineering (CS321). His research integrates eye tracking with software engineering to enhance source code comprehension through analysis of developer behavior and eye movement patterns during software development. His educational background includes a PhD from Kent State University (2020), followed by a visiting research scholar position at Harvard University's School of Engineering and Applied Science and Schepens Eye Research Institute in 2023. Prior to joining Colby, he began his teaching career at Hamilton College where he taught Operating Systems and Wearable Technology courses. PhD, Kent State University, 2020 Visiting Research Scholar, Harvard University, 2023 Began teaching career at Hamilton College Dr. Al Madi's research focuses on the intersection of eye tracking technology and software engineering, particularly examining how developers comprehend source code through eye movement analysis. His work extends to Human-Computer Interaction applications in clinical rehabilitation settings and the impact of AI tools like GitHub Copilot on code readability and developer workflows. He maintains an active research blog discussing cognitive aspects of programming and regularly collaborates with undergraduate students on research projects. His recent publications analyze lexical similarity in identifier names, the readability of AI-generated code, and longitudinal eye tracking studies of developers progressing from novice to expert levels. These works collectively explore how cognitive processes affect software development practices and how tools can be designed to better support developer cognition. Dr. Al Madi is deeply committed to inclusive computer science education, advocating that 'anyone can become a computer scientist if they work hard' regardless of background. He actively mentors undergraduate researchers, emphasizing the importance of diversity in technology development to prevent exclusionary design patterns. His teaching philosophy integrates modern software engineering practices with critical analysis of AI tools, requiring students to understand and verify all AI-generated code rather than using it uncritically. Based in the Davis Science Center at Colby College, he maintains an active presence in the software engineering research community, serving on program committees for major conferences including ASE and FSE. His blog features practical career advice for students, including guidance on resume building, internship applications, and navigating the tech industry.

• Biology Education
• Science Education
• Educational Technology
• +۵ مورد دیگر

Dr. Marit Kastaun serves as Lecturer for special tasks in the Didactics of Biology at the University of Kassel since November 2023, with continuous affiliation to the department since 2013. She is a core researcher at the FLOX Teaching and Learning Laboratory and contributes to the outdoor education site Freilandlabor Dönche. Her educational background includes: First State Examination for Gymnasium Teaching (Biology/History), University of Kassel (2017) Cumulative PhD in Biology Didactics, University of Kassel (2024) Dr. Kastaun's research pioneers the integration of AI tools in biology teacher education, focusing on adaptive feedback systems for scientific hypothesis formation and diagnostic competence development. Her work bridges digital learning environments with hands-on experimentation, particularly through video-based resources and eye-tracking studies of cognitive processes during scientific inquiry. Current projects emphasize context-aware AI applications in both classroom and outdoor laboratory settings. Her funded research includes: Shift project (2023-present): AI-driven hypothesis feedback systems ProfiLL (2020-2023): Intelligent systems for teacher professionalization Digital Media Handling (2020-2022): Baseline digital competencies for biology students At the FLOX Teaching Laboratory, she develops and tests digital learning scenarios that merge theoretical knowledge with practical experimentation, with recent focus on Mimosa pudica movement studies and photosynthesis measurement techniques. Her approach emphasizes inclusive design principles for diverse learner needs in science education.