Jonas Kantic is a Ph.D. student and Researcher at the Chair of Integrated Systems within the Faculty of Electrical Engineering and Information Technology at Technical University of Munich . Holding a Master of Science in Technical Informatics from Leibniz University Hannover, his work focuses on AI acceleration architectures and embedded systems design. Education Master's in Technical Informatics (2017-2020), Leibniz University Hannover Chinese Language Studies (2018-2019), Beijing Foreign Studies University Bachelor's in Technical Informatics (2013-2017), Leibniz University Hannover Research Focus Specializes in reservoir computing architectures Expertise in FPGA-based AI acceleration Investigates temporal/spatial compression techniques Develops efficient edge AI inference systems Applies machine learning to motorcycle control systems Works on hyperdimensional computing models Supervision Mentored 5+ students in RNN accelerators, CNN optimization, and stochastic computing Collaborates with industry partners (BMW Motorrad, NXP Semiconductors) Publications 2024 - Complex & Intelligent Systems: Cellular Automata for Reservoir Computing 2024 - IEEE NorCAS: FPGA Implementation for High-Speed Reservoir Models 2021 - Current Directions in Biomedical Engineering: Hearing Aid CNN Optimization
Marina Rosenfeld is an Adjunct Professor of Sonic Arts at the Milton Avery School of the Arts, Bard College, where she has been faculty since 2005 and has co-chaired the Music/Sound department since 2007. Based in New York, she is a composer and artist whose interdisciplinary work spans sound art, performance, and visual art, with presentations at major institutions including the Park Avenue Armory, Museum of Modern Art, Guggenheim Museum, Tate Modern, and Stedelijk Museum. Adjunct Professor, Sonic Arts, Conservatory of Music, School of Visual, Media and Performing Arts Faculty member, Milton Avery School of the Arts at Bard College since 2005 Co-chair, Department of Music/Sound since 2007 2024 Alpert Award in Visual Art recipient Rosenfeld's research interests center on the social and perceptual dimensions of sound, exploring how sonic experiences transform through bodily engagement and spatial contexts. Her work interrogates the shape and sociality of perception, examining how sensual experiences become hearing, sight, and touch. She creates installations that embrace traces, after-sound, distortion, and the body as an amalgam of social, libidinal, and inherited possibilities. Her practice spans performance, installation, electronic media, experimental musical notation, sculpture, photography, and improvisation, with a focus on inventing new forms that challenge existing categories and engage critically with power structures. Her recent publications and critical reception reveal a strong focus on the material conditions of sound, with particular attention to the Deathstar series, Teenage Lontano, and The Sheer Frost Orchestra. Critics examine her work through lenses of object-oriented ontology, neoliberal critique, and the politics of sound in institutional spaces. Her approach to sound as both material and social practice has generated significant scholarly interest in how sonic experiences shape and are shaped by cultural contexts. Alpert Award in Visual Art (2024) Foundation for Contemporary Arts Grants to Artists Award (2011) New York Foundation for the Arts Composer Fellowship (2004) Watermill Foundation Inga Maren Otto Fellowship (2019) Harvestworks Van Lier Fellowship (2002) EMPAC Artist Residency and Commission (2024) Rosenfeld has advised numerous artists through her teaching at Bard College and has received multiple grants supporting her artistic research, including commissions from Ensemble MusikFabrik (2017) and Ensemble Contrechamps (2022). Her collaborative practice spans decades, working with choreographers like Merce Cunningham (2004-08), Ralph Lemon (2014-15), and Maria Hassabi (2016-17), as well as musicians including Christian Marclay and Warrior Queen. She has also been commissioned by major festivals including Donaueschingen Musiktage and documenta 14. Rosenfeld's most enduring project is The Sheer Frost Orchestra, a temporary all-female amateur ensemble she founded in 1993 that continues to be performed today. This project explores power dynamics through unconventional instrumentation, with performers using nail polish bottles to tap electric guitars lying on the ground. Her Deathstar series represents another significant body of work, with installations and performances evolving since 2017, including a 48-hour performance in Switzerland in 2023 where audience members slept, woke, ate, and listened together over an extended period.
Prof. Dr. Stephan Schwan is the head of the Realistic Representations research group at the Institute for Knowledge Media (IWM) at the University of Tübingen. He holds a professorship in psychology and has extensive experience in cognitive and educational research, particularly focusing on how dynamic audiovisual media and digital representations influence human learning and perception in informal settings like museums. His career includes a professorship for e-learning at Johannes Kepler University Linz (2002–2004) and a tenure as Deputy Director of the IWM (2017–2024). He earned his doctorate and habilitation in psychology from the University of Tübingen. His research explores the cognitive processing of realistic media, including haptic exploration of museum artifacts, AI-generated language effects, and spatial-temporal data visualization. Projects such as 'Digital Materialities' and 'Historical Sounds' investigate how digital tools and authentic objects enhance learning in museums. Key interests include museum visitor behavior, media authenticity, and the role of sensory engagement in knowledge acquisition. Publications span cognitive psychology, educational technology, and museum studies, with a focus on multimedia learning principles and their application in real-world settings. His work bridges theoretical insights with practical applications, emphasizing the interplay between digital media and human cognition. Current projects include studying the cognitive impact of AI-generated narratives, immersive digital environments, and spatiotemporal data representation. Prof. Schwan collaborates across disciplines, integrating psychology, education, and digital humanities to advance understanding of media-driven learning processes.
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.
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.
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.
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.
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.
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.
Professor Eliathamby Ambikairajah is a leading researcher at the University of New South Wales , specializing in speech signal processing, emotion recognition, and engineering education. His work spans multiple disciplines including Computer Science , Signal Processing , and Human-Computer Interaction , with collaborations across institutions in Australia and internationally. Research Focus: Speech Emotion Recognition and Ambiguity Modeling Transformer-based Speech Enhancement and Neural ODE Anti-Spoofing Systems for Speaker Verification Engineering Pedagogy and AI Integration in Education Publication Trends: Recent work explores adaptive audio front-ends , ambiguity-aware emotion prediction , and Transformer length generalization . Earlier studies focused on replay attack detection and GMM-HMM for blood pressure estimation . Awards & Grants: Not explicitly stated in the provided text. Advising: Mentored researchers in speech processing and engineering education, though specific students are not named.
Hilde Kuehne is a Full Professor at the University of Tuebingen's Tuebingen AI Center with significant affiliations at MIT-IBM Watson AI Lab, Goethe University Frankfurt, and University of Bonn. Her research leadership spans computer vision, multimodal learning, and artificial intelligence, with emphasis on video understanding and foundational model development. She actively collaborates with IBM Research and MIT across multiple high-impact projects. Her research program focuses on critical challenges in visual intelligence: Developing explainability methods for Vision Transformers to enhance model transparency Creating robust multimodal frameworks for audio-visual alignment and spatio-temporal grounding Addressing representation biases in video benchmarks through structured debiasing approaches Advancing zero-shot recognition capabilities using large language models Exploring associative memory mechanisms for next-generation foundation models Analysis of her 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) Multimodal foundation models showing strong emphasis on fine-grained audio-visual synchronization, (2) Explainable AI techniques targeting Vision Transformer interpretability, and (3) Systematic debiasing methodologies for video understanding benchmarks. Her work consistently bridges theoretical innovation with practical applications, particularly in instructional video analysis and training-free recognition systems. Key scientific recognition includes: NeurIPS 2024 Oral Presentation for "Convolutional Differentiable Logic Gate Networks" (top 2% acceptance rate) Professor Kuehne mentors a productive research group with notable PhD students including Walid Bousselham (ICCV 2025 first-author), Sivan Doveh (ICCV 2025 first-author), and Nina Shvetsova (CVPR 2025 first-author). Her research is supported through strategic partnerships with IBM Research and MIT, evidenced by consistent co-authorship on high-impact publications. She serves on the Scientific Advisory Board of the Carl-Zeiss-Foundation and contributed to Germany's 2024 Commission of Experts for Research and Innovation annual report. She leads research initiatives within the Tuebingen AI Center and MIT-IBM Watson AI Lab, while co-organizing influential workshops including the 3rd Workshop on What is Next in Multimodal Foundation Models (CVPR 2025) and New Frontiers in Associative Memories (ICLR 2025), demonstrating her leadership in shaping next-generation multimodal AI research directions.
Kai Kunze is a Professor at Keio University's Graduate School of Media and Governance in Yokohama, Japan, where he leads research in wearable computing and human augmentation. His work bridges engineering, design, and cognitive science to develop cooperative human-computer frameworks that enhance rather than replace human capabilities. Previously, he participated in the Dagstuhl Seminar on Eyewear Computing and has maintained active collaborations with researchers across Japan and internationally. Dr. Kunze's research focuses on creating wearable technologies that augment human senses and capabilities. His work spans eyewear computing, physiological sensing, activity recognition, and affective interfaces. He investigates how technology can enhance human perception in domains ranging from music performance to sports, with particular interest in how sound and physiological feedback can create new interaction paradigms. His philosophy emphasizes cooperative frameworks between humans and computers, seeking to empower rather than replace human capabilities. His publication record shows consistent contributions to top-tier conferences including CHI, UbiComp, ISWC, and SIGGRAPH, with a clear progression from foundational work on smart eyewear (2014-2016) toward more sophisticated human augmentation systems (2020-2023). Recent work explores generative AI applications, emotion augmentation, and the implications of commercial AR/VR platforms like Apple Vision Pro. His research demonstrates strong interdisciplinary connections between computer science, cognitive science, and the arts. As a mentor, Dr. Kunze has supervised multiple students including Katsutoshi Masai, Shoya Ishimaru, and Katsuma Tanaka, who have contributed to projects on facial expression recognition, activity recognition with smart glasses, and other wearable computing applications. His research has been supported by competitive grants including a JST Presto (Sakigake) project grant on Open Collective Eyewear, one of few non-Japanese researchers to receive this funding. He actively participates in academic discourse through conference presentations, workshops, and public engagement events like the Miraikan Science Quest in Tokyo. Dr. Kunze maintains an active research laboratory focused on perception-aware computing, with projects spanning from fundamental sensor development to practical applications for older adults, performers, and athletes. His team explores how physiological data can inform interaction design and how wearable technology can create new forms of human expression and understanding.
Huayue Zhang is a Researcher at the Professorship of Audio Information Processing, Technical University of Munich. He holds a Master's in Architectural Technology from Harbin Institute of Technology (2019–2022) and a Bachelor's in Civil Engineering from Harbin University of Science and Technology (2014–2018). Since 2023, he has served as a Scientific Assistant at TUM. Research Areas : Virtual Acoustic Environments for Learning Spaces Psychoacoustics and Auditory Modeling Applications of Hearing Aids and Cochlear Implants Acoustic Monitoring and Virtual Acoustics Zhang’s work spans interdisciplinary fields, including deep learning for remote sensing , slope stabilization monitoring , and image processing techniques . His recent publications focus on neural network architectures for image denoising, SAR-based disaster assessment, and LiDAR semantic segmentation. Key Projects : HAPPAA: Exploring human auditory perception in acoustic environments Auralization: Sound-field simulation for virtual spaces Binaural Unmasking: Enhancing speech intelligibility in noise He collaborates with international teams on environmental monitoring, leveraging multi-source satellite data and drone imagery for flood and landslide assessments. His technical expertise includes signal processing and multimedia systems design.
Dr.-Ing. Anna Krause is a researcher at the Chair of Data Science (Informatik X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. She leads the Deep Learning for Dynamical Systems Group and has been actively involved in teaching at the university since 2019, including courses on Machine Learning for Time Series Analysis and Data Mining. Doctoral degree in Electrical Engineering (2019), University of Hannover Diploma in Electrical Engineering (2009), Technical University Dresden Her research focuses on Environmental Sensing and Time Series Analysis , particularly on enhancing physics-based models using machine learning techniques for meteorological applications and sparse sensor networks. She has made significant contributions to explainable AI, climate modeling, and fraud detection systems. Anna's recent publications demonstrate expertise in climate modeling (ConvMOS, ICLR 2024-2025), physics-informed neural networks (TaylorPDENet, ECMLPKDD 2023), and fraud detection (MIDAS workshops, ECMLPKDD 2020-2023). She actively contributes to conferences as organizer and PC member, including ECMLPKDD and ICLR workshops. Scientific Awards Best ML Innovation Award (2020) for Deep Learning in Climate Modeling Best Student Paper Award (2020) for Multi-Task Land Use Regression Best Paper Award (2020) for Financial Fraud Detection with INALU The DynaBench dataset introduced in 2023 provides benchmark tools for learning dynamical systems from low-resolution data. Her work combines theoretical advancements with practical implementations, including edge computing applications for beekeeping monitoring systems.
Florian Zeeh, born 1990, is a Lecturer for Hybrid Sound Composition and Advanced Systems at the Institute for Music and Media, Robert Schumann University of Applied Sciences Düsseldorf. He studied at the Robert Schumann Academy of Music in Düsseldorf, KABK The Hague, and Royal Conservatoire The Hague, focusing on interdisciplinary boundaries and methodological bridges between art forms. His research interests span algorithmic composition , audiovisual installations , and technological epistemology . He explores how technical systems "color" artistic output, emphasizing critical engagement with tools through workshops like The Colour of Doing and collaborative works with Partita Radicale. Key projects include Geosonic Landscape (2024) examining ecological transitions through sound/video, Long-term study @ Bandfabrik (2022) on temporal experimentation, and could change (2021) critiquing technological mediation. His work has been supported by the Ministry of Culture and Science of North Rhine-Westphalia and various local cultural offices. Teaching philosophy emphasizes technological transparency and critical decision-making in artistic practice. Collaborations with artists like Lennart Melzer and Mavi Garcia explore themes of repetition , failure , and systemic critique across multimedia formats.
