جست‌وجوی استاد و پژوهشگر

• Spiking Neural Networks
• Brain-Computer Interfaces
• EEG Analysis
• +۴ مورد دیگر

Gang Pan is a Professor at Zhejiang University's College of Computer Science and Technology, where he leads research in neural networks, brain-computer interfaces, and neuromorphic computing. His work bridges computer science, neuroscience, and biomedical engineering, focusing on developing novel AI approaches inspired by biological neural systems. He maintains extensive collaborations with researchers including Shijian Li, Qian Zheng, and Huajin Tang. Professor Pan's research centers on spiking neural networks (SNNs) and their applications in brain-computer interfaces, medical diagnostics, and efficient neuromorphic computing. His work explores how SNNs can model biological neural processes while offering energy-efficient alternatives to traditional deep learning. Recent projects include EEG-based mental health diagnostics, neural decoding of visual perception, and battery-free neural recording systems. His approach integrates computational neuroscience with practical AI applications, particularly in healthcare contexts. Analysis of his 15 most recent publications reveals strong trends in neuromorphic computing, with particular emphasis on spiking neural networks for medical applications. His work spans from theoretical advances in SNN architectures to practical implementations in EEG analysis, mental health diagnostics, and neural interface hardware. The interdisciplinary nature of his research connects computer science, neuroscience, and biomedical engineering, with increasing focus on clinical applications of neural decoding technologies. Professor Pan actively mentors students and researchers, as evidenced by his numerous collaborative publications across multiple labs. His research is supported by significant grants enabling work on neuromorphic hardware, brain-computer interfaces, and medical AI applications. The consistent high-impact output demonstrates sustained funding support for his innovative research directions. His laboratory focuses on neuromorphic computing systems, brain-computer interface development, and neural signal processing. The research environment integrates theoretical AI development with practical hardware implementation, creating a pipeline from algorithm design to clinical application. The lab maintains strong connections with neuroscience researchers and medical professionals to ensure clinical relevance of their technological innovations.

• Computer Vision
• Deep Learning
• Object Detection
• +۴ مورد دیگر

Zheng Ge is an active researcher specializing in computer vision and deep learning, with a prolific publication record spanning from 2018 to 2025. They have authored or co-authored 81 publications in top-tier venues including CVPR, ICLR, ECCV, and NeurIPS, demonstrating consistent research productivity and impact in the field of artificial intelligence. Dr. Ge's primary research interests focus on computer vision with particular emphasis on object detection, 3D perception, and multimodal learning systems. Their work bridges theoretical advancements with practical applications, especially in video understanding, image generation, and large vision-language models. Recent research directions show a strong focus on developing more efficient and capable multimodal systems that can handle complex visual tasks with human-like understanding. The analysis of their recent publications reveals a clear trend toward building more sophisticated multimodal systems that integrate vision, language, and temporal understanding. Their work on video generation, perception modeling, and instruction tuning demonstrates a strategic focus on advancing the capabilities of multimodal large language models while addressing practical challenges in scalability and robustness. While specific awards are not documented in the available publication records, Zheng Ge's consistent presence in top-tier computer vision and machine learning conferences indicates significant recognition within the research community. Their work has contributed to advancing state-of-the-art techniques in multiple subfields of computer vision. Dr. Ge appears to be actively collaborating with a large research team, frequently working with researchers such as Xiangyu Zhang, Jianjian Sun, Chunrui Han, and Liang Zhao. These collaborations span multiple institutions and research groups, suggesting a well-integrated position within the computer vision research ecosystem. While specific grant information isn't available in the publication metadata, the scale and scope of their projects indicate substantial research funding support. Based on publication patterns and technical focus, Zheng Ge appears to be part of a large research organization or lab specializing in computer vision and multimodal AI systems. Their work on technical reports for video foundation models and large-scale multimodal systems suggests involvement in industry-leading AI research initiatives with strong engineering capabilities.

• Biomedical Signal Processing
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Jie Luo is a researcher affiliated with Harvard Medical School's Radiology Department, Brigham and Women's Hospital, and collaborates with institutions like Colorado State University and Beihang University. Their work spans biomedical signal processing, machine learning, computer vision, and wireless communication. Key research interests include neuroscience applications such as epileptic zone localization, AI-driven medical imaging with transformers, and telecommunications advancements in hollow-core fiber transmission. Publications reflect interdisciplinary expertise in robot evolution , steganography , and 3D reconstruction . Recent articles focus on integrating high-frequency EEG analysis robust point cloud merging quantum-classical signal co-transmission lightweight AI for industrial quality control across 2025 journals like Biomedical Signal Processing and Control and IEEE Transactions . No awards or student advising data are currently available.

• Machine Learning
• Computer Vision
• Medical Imaging
• +۵ مورد دیگر

Wenwen Zhang is a Professor at the Department of Electronic Engineering, College of Information Science and Electronic Engineering, Zhejiang University. With an extensive publication record spanning from 2016 to 2025, Dr. Zhang has established herself as a prominent researcher in multiple interdisciplinary fields at the intersection of computer vision, machine learning, and sensor systems. Her work demonstrates significant contributions to medical imaging, sensor array systems, wireless communications, and AI-assisted applications. Dr. Zhang's research interests encompass a wide range of topics including medical image analysis, sensor array systems, wireless communications, and AI-assisted applications. Her work demonstrates particular expertise in developing innovative deep learning architectures for medical imaging tasks such as cardiac segmentation and nuclei detection, as well as creating sophisticated models for gas sensing and wireless communication systems. She has made significant contributions to the fields of one-shot object detection, medical image segmentation, and sensor fusion techniques, with her research often bridging theoretical advancements with practical applications in healthcare and engineering. Analysis of Dr. Zhang's recent publications reveals a strong focus on cutting-edge deep learning approaches applied to medical imaging and sensor systems. Her work shows increasing sophistication in model architectures, moving from traditional CNNs to more complex transformer-based and hybrid models. There's a clear trajectory toward more explainable and clinically relevant AI systems, particularly in medical applications. Her research also demonstrates growing interest in multimodal approaches, combining different types of data and sensors to improve system performance. Dr. Zhang maintains active collaborations with researchers at Zhejiang University, particularly with Yuanjin Zheng and Zhiping Lin in the field of electronic engineering and sensor systems. She also collaborates extensively with Fei-Yue Wang from the University of Chinese Academy of Sciences, evidenced by multiple publications on parallel vision frameworks. Her international collaborations include work with researchers from institutions in Canada on intelligent knee sleeves and other biomedical applications. Her publication record shows consistent productivity with 12 publications in 2025 (as of this writing), 25 in 2024, and 26 in 2023, indicating an active and growing research program across multiple high-impact journals and conferences.

• Affective Computing
• Machine Learning
• Human-Computer Interaction
• +۱۲ مورد دیگر

Subramanian Ramanathan is a researcher at the School of Computing, National College of Ireland , specializing in affective computing, multimodal behavior analysis, and human-computer interaction. His work spans machine learning, computer vision, and neuro-signal processing. Key research areas: Affective Computing, Deep Learning, Stress Detection, Depression Biomarkers Notable collaborations: Roland Göcke, Abhinav Dhall, Nicu Sebe His publications focus on: EEG-based cognitive load estimation Head motion pattern analysis for mental health Deepfake detection systems Audio-visual saliency prediction Transformers in behavioral modeling Stress detection via multimodal fusion Recent work includes medical imaging applications for autism detection and computational advertising systems using emotion recognition. He contributes to open science through dataset creation (SALSA, DECAF) and collaborative research in affective computing.

• Computer Vision
• Natural Language Processing
• Computational Thinking in Education
• +۳ مورد دیگر

Jiyoung Kim is a Researcher in computer science, with a focus on computer vision , natural language processing , and geospatial data analysis . She has collaborated extensively across disciplines, particularly in robotics , educational technology , and signal processing . Her research interests include: Developing advanced diffusion models for high-fidelity talking head generation . Creating automated pipelines for detoxifying Korean language in AI systems. Applying deep learning to haze removal and indoor navigation for accessibility. Exploring 6G communication through computer vision-aided beamforming . Recent publications highlight trends in multi-task learning , unsupervised segmentation , and geospatial knowledge graphs . Her work bridges theoretical and applied domains, from hardware design to educational interventions for computational thinking in early childhood.

Stephanie Thevesمشاهده پروفایل

پژوهشگر

• Structure-learning and neural concept representations
• Neural mechanisms of relational reasoning
• Neuro-cognitive processing mechanisms underlying intelligence

Dr. Stephanie Theves is a Research Group Leader at the Max Planck Institute for Empirical Aesthetics. Her work focuses on understanding the neural mechanisms underlying intelligence, relational reasoning, and concept representation through cognitive neuroscience approaches. Department of Cognitive Neuropsychology Her research explores how the hippocampus and prefrontal cortex encode hierarchical concepts, construct cognitive maps, and support relational memory systems. Key findings demonstrate that neural representations prioritize conceptual relationships over raw feature distances, and that place-cell-like mechanisms in the brain modulate memory organization. Theves' recent publications examine category abstraction, spatial cognition, and beta-band activity modulation through sensory training. Her methodological approach combines neuroimaging with computational modeling to investigate neural processes. As part of the Max Planck Society, her work contributes to foundational knowledge in cognitive neuroscience while intersecting with interdisciplinary research themes in music, language, and computational auditory perception.

• Explainable AI
• Human-AI Interaction
• Audiovisual Affective Computing
• +۴ مورد دیگر

Dr. David Johnson is a Junior Independent Group Leader at Bielefeld University's Faculty of Engineering , leading the Human-Centric Explainable AI research group within CITEC. His work bridges Explainable AI with Audiovisual Affective Computing , focusing on high-stakes human-AI collaboration and industrial sound analysis. Current Position: Junior Independent Group Leader, Bielefeld University (since 2021) Previous: Postdoctoral Researcher at Fraunhofer Institute for Digital Media Technology (2019-2021) Education: PhD in Sound and Music Computing from University of Victoria (2019), MSc in Computing in the Arts from College of Charleston (2014) Research interests span Explainable AI , Human-AI Interaction , and Extended Reality applications, particularly in medical diagnostics and industrial sound processing. Recent publications highlight his work on trust dynamics in high-stakes AI and federated learning architectures . His collaborative projects include involvement in the TRR 318 'Constructing Explainability' initiative. While no formal awards are mentioned, his research has produced multiple publications and practical implementations in industrial and educational contexts.

• Learning with historical reconstructions
• Informal learning in museums
• Cognitive processing of uncertain information
• +۳ مورد دیگر

Dr. Manuela Glaser is a Researcher at the Leibniz Institute for Knowledge Media (IWM) in Tübingen, Germany, where she has been a core member of the Realistic Depictions lab since January 2007. Her work bridges cognitive psychology and museum education, focusing on how visitors process historical reconstructions and uncertain information through digital media. Her educational background includes a PhD in Psychology (2010) from the University of Tübingen, with a dissertation on hybrid documentary formats in archaeological television, and a Diploma in Psychology (2006) from the same institution specializing in pedagogical and media psychology. Glaser's research centers on cognitive mechanisms in informal learning environments, particularly examining how spatial audio, VR applications, and visual-verbal cueing influence attention and knowledge acquisition in museum contexts. She investigates the processing of uncertainty in archaeological reconstructions and the role of reception goals in virtual experiences, frequently employing eye-tracking methodologies to capture real-time cognitive responses. Analysis of her 15 most recent publications reveals a strong trajectory toward immersive technologies, with increasing emphasis on spatial sound design (2022–2025) and uncertainty communication in 3D reconstructions (2019–2023). Her work consistently applies cognitive psychology principles to museum education, demonstrating how multimedia design choices directly impact learning outcomes in historical contexts. No scientific awards are documented in the provided materials. Glaser actively contributes to major research projects including Historical Sounds (2022–2025), which explores spatial audio in VR tugboat simulations, and Digital Materialities (2021–2024), investigating digital versus physical exhibition objects. She has organized international conferences like Exhibiting the Sound of History (2024) and frequently presents at EARLI and IGEL conferences. As a key researcher in the Realistic Depictions lab, she collaborates with interdisciplinary teams to develop evidence-based frameworks for museum technology, currently leading studies on virtual reality applications for historical soundscapes and 3D reconstruction design principles.

• Haptic Interfaces
• Human-Computer Interaction
• Multimodal Interfaces
• +۵ مورد دیگر

Jeremy R. Cooperstock is a Professor at McGill University in Montreal, Canada, with a distinguished career spanning over three decades in human-computer interaction, haptics, and multimodal interface research. His work has established him as a leading figure in the development of practical haptic communication systems and accessibility technologies. Professor Cooperstock's research focuses on creating novel interfaces that enhance user experience through touch, sound, and visual feedback. His laboratory has pioneered significant advancements in vibrotactile feedback systems, telepresence technologies, and audio-haptic integration for improved user awareness and communication. His work bridges fundamental research in human perception with practical applications across multiple domains including accessibility, social interaction, and immersive environments. His recent publications demonstrate a strong trajectory in developing practical haptic applications for everyday use, with notable contributions to web accessibility frameworks, social touch interfaces, and context-aware haptic feedback systems. Cooperstock's research has evolved from foundational work in multimodal telepresence to increasingly applied research addressing real-world challenges in human communication and interaction. Throughout his career, Cooperstock has mentored numerous graduate students who have become significant contributors in the field. His collaborative approach is evident in his extensive publication record with students and colleagues across multiple institutions. His research group has consistently produced high-impact work published in top-tier venues including IEEE Transactions on Haptics, CHI, UIST, and WHC.

• Transnational Social Work
• Cultural Competence
• Decolonial Social Work
• +۴ مورد دیگر

Chaitali Das is a Professor of Transnational and International Social Work at the Frankfurt University of Applied Sciences , focusing on culturally competent social work services for minority communities, particularly Syrian refugees. She teaches courses on transnational social work methods, power dynamics, and ethical research practices. PhD in Health and Social Care (Royal Holloway, University of London, 2004) Specializes in decolonial approaches, transnationalism, and cultural competence in social work Coordinates international research projects like 'Internationalising the Social Work Curriculum' Her research explores transnational family dynamics , refugee integration, and ethical challenges in studying marginalized groups. Recent publications emphasize global social work pedagogy , risk assessment in European pilot projects, and community development in multicultural contexts. She actively collaborates with institutions across Germany, India, and the UK, and her work addresses decolonial social work and the implications of migration rhetoric in Germany.

Florian Müllerمشاهده پروفایل

استادیار

• Extended Reality
• Human-Computer Interaction
• Mobile Interaction
• +۴ مورد دیگر

Florian Müller serves as Assistant Professor for Mobile Human-Computer Interaction at Technical University of Darmstadt, leading the Urban Interaction Lab. His research pursues seamless integration of digital information, AI agents, and physical environments into unified Extended Reality (XR), with focus on novel interaction techniques for mobile XR and human-AI collaboration through body-centric, tangible, and haptic interfaces. Müller's primary research spans Extended Reality, Human-Computer Interaction, and Mobile Interaction with specialization in Haptic Interfaces, Tangible Interfaces, and Body-Centric Interaction. He investigates how multimodal sensory feedback enhances user experiences in immersive environments, particularly examining AI-driven adaptation for context-aware XR systems and the social implications of pervasive interactive technologies in urban settings. Recent publications (2024-2025) demonstrate strong interdisciplinary trends across multisensory XR (taste modulation, thermal feedback), social VR/AR applications (privacy, content sharing), and human-AI co-creation systems. Key domains include health (motor control assessment), education, and urban interaction contexts, with consistent emphasis on user experience, safety, and practical implementation challenges. Müller directs the Urban Interaction Lab at TU Darmstadt, which develops and evaluates interactive technologies for public spaces through prototyping novel mobile and XR interfaces. The lab's work emphasizes real-world deployment in urban environments, focusing on social engagement, situational awareness, and seamless interaction between physical and digital layers of city infrastructure.

Alexander Stublicمشاهده پروفایل

استاد مهمان

• Media Art
• Digital Installations
• Virtual Reality
• +۷ مورد دیگر

Alexander Stublic is a media artist and academic specializing in mechanisms of perception through site-specific explorations of architecture. He currently holds lectureships at University of Music, Karlsruhe (2008-2025) and Karlsruhe University of Arts and Design, with a notable yearlong guest professorship at HBKSaar School of the Fine Arts (2010-2011) as part of the Lucius Burkhardt Gedenkprofessur. His institutional affiliations include collaborations with Siemens Arts Program, membership in the 3D Alliance Karlsruhe, and association with Expanded 3Digital Cinema Laboratory. Stublic's research focuses on the intersection of digital media and physical space, particularly examining how light, video, and sound can transform urban environments. His recent work centers on 'the intrusion of simulation into reality,' utilizing AR/VR, AI, and 3D printing technologies to create immersive experiences that challenge viewers' perception. Key projects include the award-winning 'Innerland' (2019-2025), an interactive AR/VR installation based on Viktor Ullmann's piano concerto, and 'The uncanny valley' series exploring digital representation. His artistic output reveals a consistent trajectory toward increasingly sophisticated digital integration, with recent projects demonstrating mastery of immersive technologies while maintaining strong conceptual foundations in perception theory and architectural intervention. The evolution from earlier LED and projection works to current AR/VR and AI applications shows a deepening engagement with how digital simulation interacts with physical reality. Opus Klassik award for 'Innerland' (2022) Special scholarship Restart Culture (2020) IALD Award and segd Merit Award for Uniqa Tower installation Hector Kunstpreis Inclusion in Siemens Art Collection, Osram art collection, and KfW banking group art collection Stublic maintains active collaborations with institutions including Siemens Arts Program and has presented work at major venues worldwide. His work 'Innerland' represents a significant achievement in merging classical music performance with cutting-edge VR technology, creating new possibilities for immersive cultural experiences. His ongoing projects continue to push boundaries in digital art while maintaining strong connections to architectural and spatial contexts. Operating through his collaborative entity Mader Stublic Wiermann, Stublic maintains a productive studio practice that bridges academic research and professional artistic creation, with current projects extending through 2025-2026.

Philipp Bruneمشاهده پروفایل

استاد

• Artificial Intelligence
• Facial Emotion Recognition
• Cybersecurity
• +۵ مورد دیگر

Professor Dr. Philipp Brune serves as a prominent academic leader at Neu-Ulm University of Applied Sciences (HNU), holding multiple key positions including Academic Head of TTZ Günzburg, Head of the Master of Artificial Intelligence and Data Analytics program, Head of the Centre for Secure IT Applications and Infrastructures, and Head of the Institute of Agile Product and System Development within the Faculty of Information Management. His career demonstrates a notable transition from physics research (evident in his early 2000-2004 publications) to his current focus on cutting-edge information systems and artificial intelligence. Dr. Brune's research program spans several interconnected domains with particular emphasis on facial emotion recognition systems, legacy system modernization, and secure IT infrastructure development. His work investigates fundamental challenges in AI interpretability, data quality for emotion recognition, and practical approaches to modernizing legacy applications. He has made substantial contributions to understanding the limitations of current facial expression recognition technologies and developing more robust, trustworthy alternatives. Analysis of his recent publications reveals a clear research trajectory toward explainable and trustworthy AI systems, with increasing sophistication in addressing data quality issues and model interpretability challenges. His work bridges theoretical computer science with practical applications across healthcare, marketing, and smart city technologies, demonstrating strong translational research capabilities. As an educator, Dr. Brune has developed innovative pedagogical approaches specifically tailored for non-computer science majors, with research examining student motivation in programming courses and effective methods for teaching cybersecurity concepts. His leadership in the Master of Artificial Intelligence and Data Analytics program shapes curriculum development for next-generation AI professionals. Dr. Brune directs multiple research initiatives including the Centre for Secure IT Applications and Infrastructures, which addresses critical cybersecurity challenges in modern IT environments, and the Institute of Agile Product and System Development, which explores innovative software development methodologies. His collaborative research approach is evident through extensive co-authorship networks spanning multiple institutions and disciplines.