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Mats KüssnerView profile

Researcher

• Multimodal perception of music
• Music and mental imagery
• 4E music cognition
• +3 more

Mats Küssner is a Researcher at Humboldt University of Berlin's Department of Musicology, specializing in Transcultural Musicology and Historical Anthropology of Music. Previously, he held a Peter Sowerby Research Associate position at the Royal College of Music in London. He earned his doctorate from King's College London, focusing on cross-modal mappings between sound and visual forms. His work bridges music psychology, cognitive science, and cultural studies, emphasizing how music shapes human thought and emotion through multi-method approaches. Research interests include multimodal perception, music-induced mental imagery, 4E cognition (embodied/embedded/enactive/extended), and performance science. Notable awards include the Aubrey Hickman Award for music psychology research and a teaching excellence award. He teaches courses like 'Diversity in Berlin's Sound Scene' and 'Emotions in Music' at both BA and MA levels. His publications span over 40 peer-reviewed articles across journals like Music & Science , Psychomusicology , and Frontiers in Psychology , with a focus on visual-aural interplay, music cognition disorders (e.g., aphantasia), and cross-cultural performance dynamics. He co-edited the landmark volume Music and Mental Imagery (Routledge, 2022). Recent work explores environmental ecomusicology education and music's role in mitigating social isolation. Küssner leads projects on music-induced emotion prediction using cross-modal correspondences and is part of Berlin's interdisciplinary music psychology scene, collaborating with neuroscientists and cultural anthropologists. His lab employs eye-tracking, EEG, and behavioral experiments to study embodied music cognition processes.

Shlomo DubnovView profile

Professor

• Music Technology
• Audio Signal Processing
• Machine Learning for Audio
• +3 more

Shlomo Dubnov is a Professor in the Department of Music at the University of California, San Diego. With an h-index of 36 and over 4,803 citations, his research spans the intersection of music, audio processing, and machine learning, making significant contributions to music information retrieval, sound synthesis, and speech recognition technologies. Dr. Dubnov's primary research interests focus on Music Technology and Audio Signal Processing , with particular expertise in: Machine learning applications for music and audio analysis Sound texture synthesis and modeling Music improvisation systems and computational creativity Audio adversarial examples and security Pitch tracking in noisy environments Multimodal learning for audio-text alignment His recent work demonstrates a strong trend toward applying transformer architectures and contrastive learning techniques to audio processing tasks, with publications in top conferences like IEEE International Conference on Acoustics, Speech, and Language Processing. Dr. Dubnov's research bridges theoretical music concepts with practical audio engineering applications, creating innovative solutions for music generation, analysis, and security. Dr. Dubnov has established productive collaborations with researchers across disciplines, particularly with: G. Assayag (102 Publications • 2,475 Citations) - music technology and improvisation systems Taylor Berg-Kirkpatrick (31 Publications • 1,459 Citations) - machine learning applications K. Chen (18 Publications • 1,150 Citations) - audio transformer models Cheng-i Wang (21 Publications • 305 Citations) - music information retrieval His work is supported by grants focused on advancing the state-of-the-art in music technology, audio processing, and machine learning applications for creative domains. Dr. Dubnov leads research in the Music Technology laboratory at UC San Diego, where his team develops innovative systems for music analysis, synthesis, and human-computer interaction in musical contexts. The lab focuses on creating intelligent systems that can understand, generate, and interact with musical content in real-time applications.

• Human-Computer Interaction
• Quality of Experience (QoE)
• User Experience (UX)
• +7 more

Lieven De Marez is a Professor at Ghent University, affiliated with the Faculty of Engineering and Architecture and the Department of Information Technology. His research spans human-computer interaction, quality of experience (QoE), smart homes, digital education, and social media behavior, reflecting a strong interdisciplinary approach combining technical and social sciences. His research focuses on understanding user interactions with emerging technologies, particularly in educational, healthcare, and domestic environments. Key interests include learning analytics, privacy in social media, VR/AR experiences, and the impact of digital technologies on youth and vulnerable populations. He frequently investigates how users perceive and adapt to new systems, emphasizing user-centered design and ethical implications. The most recent publications highlight a trend toward digital well-being, algorithmic transparency in education, and the integration of AI in health and urban contexts. His work often involves mixed-method studies, longitudinal data analysis, and experimental designs to assess user acceptance, behavior, and experience. Recipient of multiple collaborative research grants (inferred from sustained publication output). Active advisor and collaborator with PhD and postdoctoral researchers (inferred from co-authorship patterns). He has contributed to major projects on mobile TV, smart cities, and digital inclusion, often in collaboration with Flemish and European institutions. His work appears in top journals such as Telematics Informatics , New Media & Society , and IEEE Transactions . Lieven De Marez leads or contributes to interdisciplinary research teams focused on interactive technologies, including work on VR prototyping, personal data stores (Solid), and ambient intelligence in public spaces. His recent involvement in projects like ExperienceDNA and Art and Science Interaction Lab underscores his leadership in experimental and user-centric research infrastructures.

• Information Retrieval
• Digital Libraries
• Informetrics
• +5 more

Philipp Mayr-Schlegel is a Professor at the University of Göttingen’s Institute of Computer Science, leading the team 'Information & Data Retrieval' at GESIS - Leibniz-Institute for the Social Sciences. His research focuses on interactive information retrieval systems, scholarly recommendation mechanisms, and the integration of bibliometric methods with digital library technologies. University of Göttingen - Institute of Computer Science GESIS - Leibniz-Institute for the Social Sciences His work spans information retrieval , digital libraries , and applied informetrics , with particular emphasis on: Interactive search systems Non-textual ranking algorithms Scholarly document processing Knowledge representation Semantic search technologies User behavior analysis Recent research outputs (2025) include studies on LLMs for scholarly search, bibliometric reproducibility tools, scientific uncertainty annotation, and preprint adoption disparities. He has published extensively in Scientometrics , International Journal on Digital Libraries , and top conference proceedings like ACL and ECIR . As organizer of the International Workshop on Bibliometric-enhanced Information Retrieval (BIR) and Scholarly Document Processing (SDP) series, he has shaped academic discourse through editorial roles at ISSI , JCDL , and SocInfo conferences. His projects have attracted significant national and European funding.

Yana BoevaView profile

Research Fellow

• Science and Technology Studies
• Human-Computer Interaction
• Digital Fabrication
• +5 more

Dr. Yana Boeva is a Junior Research Group Leader at the University of Stuttgart , affiliated with both the Institute for Social Sciences and the Cluster of Excellence IntCDC . Her work bridges Science and Technology Studies (STS) with Computational Design , focusing on critical examinations of digital infrastructure, human-computer interaction, and sustainability in architectural contexts. Research Interests: Algorithmic entanglements in design and construction Participatory technology production Digital fabrication and material practice Epistemic cultures of computational design Socio-political implications of Building Information Modeling (BIM) Her recent publications analyze timber construction innovation trajectories , platformization in urban environments , and ghost labor in automation . She co-edited the volume Algorithmic Regimes (2024), which investigates algorithmic knowledge production across societal domains. Scientific Awards: Recipient of the DigitalFUTURES Young Award for critical computational design research Dr. Boeva teaches courses on Algorithmic Sociology and Digital Sustainability at the MA and BA levels. Her work has been supported by projects under the Excellence Cluster IntCDC and BBSR Innovationsprogramm Zukunft Bau .

• Information Retrieval
• Large Language Models
• Evaluation Methodologies
• +3 more

Charles L. A. Clarke is a Professor at the University of Waterloo, Canada, with a focus on Information Retrieval and Large Language Model evaluation . He actively contributes to research in search algorithms, human-computer interaction, and computational linguistics. Recent Research Trends : His work examines LLM limitations in relevance assessment, adversarial robustness in legal domains, and hybrid human-AI evaluation frameworks. Workshop Leadership : Co-organizer of the Search Futures Workshop (ECIR 2024/2025) and LLM4Eval@SIGIR. Collaborations : Works with researchers from NII, Microsoft, and ACM SIGIR on testbed development and evaluation methodologies. Key Article Trends : His 2024-2025 publications analyze LLM vulnerabilities, develop evidence retrieval systems, and create metrics for human-AI alignment in generative applications. Subfields include adversarial attacks, prompt sensitivity, and semantic graph frameworks. Scientific Contributions : Focuses on bridging algorithmic performance with human judgment validity, emphasizing ethical AI deployment and robust information access systems.

• Ontology Engineering
• Knowledge Representation
• Natural Language Processing
• +4 more

Dr. C. Maria Keet is a Professor in the Department of Computer Science at the University of Cape Town, South Africa. With a PhD from the Free University of Bozen-Bolzano (2008), she has established herself as a leading researcher in ontology engineering and multilingual natural language processing, particularly for African languages. Her research interests span Ontology Engineering , Knowledge Representation , Natural Language Processing for African languages (especially isiZulu), Conceptual Data Modeling , and Temporal Data Modeling . She has developed significant frameworks for ontology modularization, competency questions, and multilingual knowledge representation. Her work bridges theoretical computer science with practical applications for linguistic diversity in Africa, addressing the critical need for technology that serves low-resourced languages. Her recent publications (2023-2025) reveal a strong focus on advancing ontology engineering methodologies, improving multilingual language processing capabilities, and developing tools for temporal data modeling. She has pioneered approaches for representing African languages in computational systems, with particular attention to noun classification, verb conjugation, and part-whole relations in Bantu languages. Dr. Keet has made substantial contributions to educational resources in her field, including her 2023 book The What and How of Modelling Information and Knowledge - From Mind Maps to Ontologies , which serves as a comprehensive guide to knowledge representation techniques. She actively supervises graduate students and has developed in-house tools specifically for ontology engineering education, demonstrating her commitment to advancing both research and teaching in her field.

• Data Mining
• Database Systems
• Spatial Databases
• +5 more

Yubao Liu is a Professor at Sun Yat-sen University's School of Data and Computer Science, Department of Computer Science, with a prolific research career spanning over two decades in computer science. His work demonstrates significant contributions to database systems, data mining, and spatio-temporal analysis. Professor Liu's research interests focus on Data Mining , Database Systems , Traffic Flow Prediction , and Graph Neural Networks . His recent work has concentrated on developing advanced techniques for large-scale traffic flow prediction, crowd flow analysis, and spatio-temporal modeling using deep learning approaches. His research bridges theoretical computer science with practical applications in transportation systems and urban computing. Liu's publication record shows consistent high-impact contributions, with recent work emphasizing graph-based neural network architectures for traffic forecasting problems. His research demonstrates a clear evolution from foundational database work to cutting-edge applications of deep learning in transportation and social network analysis. Professor Liu has collaborated extensively with researchers including Weiyang Kong, Kaiqi Wu, Sen Zhang, Genan Dai, and Youming Ge, indicating a strong research group focused on spatio-temporal data analysis and deep learning applications. His academic advising is evident through publications where his students appear as first authors, suggesting an active mentorship role in training the next generation of computer scientists specializing in data-intensive applications.

• Multi-agent Systems
• Educational Technology
• Intelligent Tutoring Systems
• +5 more

Demetrio Arturo Ovalle Carranza is a Professor in the Department of Computer Science at the National University of Colombia's School of Engineering. With an academic career spanning over three decades since earning his PhD from Joseph Fourier University in Grenoble, France in 1991, he has established himself as a prominent researcher in multi-agent systems and educational technology. His research focuses on developing intelligent systems for education, particularly in the areas of multi-agent architectures, adaptive learning environments, context-aware educational applications, and learning object recommendation systems. His work bridges artificial intelligence with practical educational applications, creating systems that adapt to individual learner needs and contexts. Analysis of his recent publications (2015-2020) reveals a strong trend toward developing sophisticated multi-agent systems for educational applications, with increasing emphasis on context-awareness, personalization, and adaptive learning techniques. His research demonstrates consistent evolution from foundational multi-agent work to sophisticated educational applications integrating fuzzy logic, ontologies, and collaborative filtering. Ovalle Carranza has mentored numerous researchers who have become active contributors in the field, including Oscar M. Salazar, Néstor D. Duque, Valentina Tabares Morales, and others who frequently appear as co-authors on his publications. His collaborative network extends across Colombian institutions and international research groups. His laboratory work centers around developing and testing multi-agent educational systems, with particular emphasis on creating adaptive learning environments that can respond to individual student needs and group dynamics. Current projects appear to focus on serious games, health-care applications of intelligent agents, and advanced recommendation systems for educational resources.

• Robotics
• Industrial Automation
• Control Systems
• +6 more

Petter Falkman is a researcher at Chalmers University of Technology, specializing in robotics, industrial automation, and control systems. His work bridges theoretical advancements with practical applications in manufacturing, leveraging technologies like digital twins, eye tracking, and virtual reality. Key Research Areas: Robotics, Industrial Automation, Control Systems, Digital Twins, Human-Computer Interaction, Machine Learning. Collaborations: Frequently works with Bengt Lennartson, Kristofer Bengtsson, Martin Dahl, and colleagues across institutions. Publication Trends: Recent articles focus on gaze-based human intention prediction, ROS2 control architectures, and compositional automated planning. His work integrates machine learning with industrial control systems, emphasizing event-driven design and virtual commissioning. Methodologies: Develops frameworks like EPypes for data pipelines, contributes to STEP AP214 model generation, and explores energy optimization in multi-robot systems.

• Ecohydrology
• Terrestrial Carbon Cycle
• Machine Learning Applications in Ecology
• +5 more

Jacob A. Nelson serves as Project Group Leader for the Cross-Scale Terrestrial Ecophysiology (XTE) group within the Biogeochemical Integration Department at the Max Planck Institute for Biogeochemistry in Jena, Germany. His research bridges ground-based ecosystem measurements with global carbon, water, and energy cycle analysis through knowledge-guided data-driven methodologies. The XTE group focuses on synthesizing ecophysiological understanding from direct measurements while developing advanced models for global flux estimation. Nelson's research centers on ecohydrological processes, particularly plant water use and its interaction with carbon cycles. His work employs machine learning approaches guided by physiological understanding to estimate transpiration across diverse ecosystems. Current research priorities include advancing the FLUXCOM framework for global data-driven estimates of terrestrial carbon, energy, and water fluxes, with significant contributions to the X-BASE product development. His methodological innovations address critical challenges in eddy covariance measurements, energy balance closure, and evapotranspiration partitioning. Analysis of Nelson's 15 most recent publications reveals strong emphasis on resolving measurement limitations in ecosystem flux quantification while developing next-generation scaling frameworks. His work consistently integrates machine learning with biophysical understanding to improve terrestrial flux estimates, with particular focus on evapotranspiration dynamics, carbon-water interactions, and addressing energy imbalance issues in flux tower data. The research spans from vineyard-scale applications to global FLUXCOM-X products. Nelson actively supervises a research team including doctoral researchers Deep Prakash Sarkar, Xiuzhi Chen, Sinikka Jasmin Paulus, Laura Nadolski, Yucong Hu, Luca Tuzzi, and Kai-Hendrik Cohrs, along with bachelor student Jakob Lambert-Hartmann. He recently supervised Weijie Zhang through successful PhD defense at Ghent University. Current projects include EO-LINCS for terrestrial carbon cycle assessment and FLUXCOM for upscaling biosphere-atmosphere fluxes from FLUXNET sites to global scales.

Ricarda WinkelmannView profile

Professor

• Climate System Analysis
• Ice Sheet Modeling
• Sea-Level Rise
• +5 more

Ricarda Winkelmann is Professor of Climate System Analysis at the Potsdam Institute for Climate Impact Research (PIK) and University of Potsdam, where she leads the FutureLab on Earth Resilience in the Anthropocene and the Working Group on Ice Dynamics. She serves as a member of the Earth Commission and holds leadership roles in major international projects including EU-H2020's TiPACCs and PROTECT, Leibniz Project DominoES, and multiple DFG-funded initiatives. Her research focuses on ice sheet dynamics in Antarctica and Greenland, investigating tipping points in the climate system through advanced numerical modeling. She specializes in sea-level rise projections, ice-ocean interactions, and the stability thresholds of polar ice sheets using the Parallel Ice Sheet Model (PISM) and Ice Shelf Cavity Model (PICO). Her work integrates glaciology, climate modeling, and Earth system science to assess planetary boundaries and potential domino effects in the Earth system, with emphasis on anthropogenic climate impacts. Professor Winkelmann's scientific recognition includes: Young Scientist of the Year (2018) EGU Outstanding Early Career Scientists Award (2017) Karl-Scheel-Prize (2017) Teaching award 'E-Learning UP' from University of Potsdam (2017) Young Elite Award 'Top 40 under 40 in Science' (2016, 2017) Publication Award by Leibniz-Kolleg Potsdam (2013) She has secured substantial research funding as Principal Investigator for EU-H2020 projects TiPACCs and PROTECT, DFG projects VAST, GEOLQUEA, XMELT, GIS-TIP, HASH, and PISM-FESOM, and Leibniz Project DominoES. Her grant portfolio examines ice sheet vulnerability, glacial isostatic adjustment, and climate policy implications of Antarctic tipping points. While student advisees aren't listed in source materials, she mentors through leadership of interdisciplinary teams and scientific expeditions. Professor Winkelmann directs the Ice Dynamics Working Group within PIK's Department Earth System Analysis and leads the FutureLab on Earth Resilience. Her research team develops open-source ice sheet models and conducts fieldwork through Antarctic expeditions (ANT-XXVII/2 and ANT-XXXIII/2 on Polarstern) and the interdisciplinary Expedition Anthropocene in the Andes with the German Young Academy.

Roderich GroßView profile

Professor

• swarm robotics
• multi-robot-systems
• modular and reconfigurable robotics
• +1 more

Professor Roderich Groß is a faculty member at Technical University of Darmstadt, where he serves as Head of the RCPS Lab (Robotics, Control, and Physical Systems Laboratory). His extensive research career spans over two decades with publications dating back to 2006, establishing him as a leading figure in swarm robotics. His work appears in top-tier robotics journals including IEEE Transactions on Robotics, Nature Communications, and The International Journal of Robotics Research. Professor Groß's research focuses on swarm robotics, multi-robot systems, modular and reconfigurable robotics, and human-robot interaction. His work explores how groups of relatively simple robots can collectively perform complex tasks through decentralized control mechanisms. He investigates modular systems that can adapt their physical structure to different tasks and develops human-robot interaction paradigms that enable effective collaboration between humans and robot swarms. His research has practical applications in search and rescue, environmental monitoring, construction, and industrial automation. Analysis of Professor Groß's recent publications reveals a clear evolution from foundational swarm behaviors toward increasingly sophisticated applications. His work addresses critical challenges including energy management in robot swarms (CapBot), multi-operator control interfaces, heterogeneous systems for industrial applications, and bio-inspired collective behaviors. His research bridges theoretical swarm intelligence principles with practical implementation on physical robot platforms like Kilobots, with recent work integrating artificial intelligence techniques such as language models for swarm control. As Head of the RCPS Lab, Professor Groß leads a productive research group with extensive international collaborations. The lab works with various robot platforms focusing on both theoretical modeling and practical implementation of swarm behaviors, contributing significantly to advancing the field of swarm robotics from theoretical foundations to real-world applications.

Johannes LangerView profile

Researcher

• Machine Teaching
• Abstract Reasoning
• Neuro-Symbolic/Hybrid AI
• +3 more

Johannes Langer is a Researcher affiliated with the Cognitive Systems Group at Faculty of Information Systems and Applied Computer Sciences , Otto-Friedrich-Universität Bamberg, Germany. His academic journey includes a B.Sc. in Psychology (Faculty of Human Sciences and Education) and subsequent roles in Computing in the Humanities. Research and Teaching Assistant (since 2024) Former Teaching Assistant (2022-2024) His research focuses on Machine Teaching , exploring how humans teach machines through sequential interactions. Additional interests include Abstract Reasoning , Neuro-Symbolic/Hybrid AI , and Representation Learning . Recent publications and workshop activities highlight his work in explanatory machine learning, inductive programming, and AI education. Article trends show interdisciplinary work bridging Cognitive Science and Computer Science , particularly in Human-Centered AI and Educational Technology . He participates in academic seminars like the Dagstuhl Seminar on Approaches and Applications of Inductive Programming.

• Technology Assessment
• Work and Technology
• Human-Machine Relations
• +5 more

Dr. Bettina-Johanna Krings is a Scientific Associate at the Institute for Technology Assessment and Systems Analysis (ITAS) at the Karlsruhe Institute of Technology (KIT), where she has been working continuously since 1995. She currently serves as Deputy spokesperson for the topic "Work and Technology" within the focus area "Humans and Technology" at KIT and is a Member of the Ethics Advisory Board of the research network "Village Community 2.0 – Aging in Rural Areas." Her role includes being the Responsible Coordinator for Technology Assessment in Teaching at KIT, demonstrating her commitment to integrating research and education. Dr. Krings completed her dissertation in 2015 on "Strategien der Individualisierung" (Strategies of Individualization) at Goethe-Universität Frankfurt am Main, supervised by Prof. Dr. Birgit Blättel-Mink and Prof. Dr. Tilla Siegel. Her academic journey began with work as a scientific associate in educational management programs and gender equality initiatives in Argentina during the early 1990s before joining ITAS. Her research focuses on the critical intersection of technology, work, and society. Main content areas include technical innovations and their impacts on work structures, concepts of human-machine relations, theory and methods of technology assessment, and sociological theories of modernity. She examines how digitalization transforms work environments, particularly in care settings, and investigates ethical dimensions of emerging technologies like artificial intelligence. Her work consistently addresses questions of human-centered technology design, the future of work in rapidly changing technological landscapes, and the societal implications of technological change from multiple disciplinary perspectives. Her extensive publication record (2015-2025) reveals a clear evolution toward increasingly applied research on ethical technology implementation, with particular emphasis on AI applications in workplace contexts, participatory technology development in healthcare, and the transformation of work through digitalization. The interdisciplinary nature of her work is evident in collaborations across sociology, engineering, healthcare, and ethics disciplines. Dr. Krings leads and participates in multiple significant research initiatives including the investigation of 4-day week models and digitalization in geriatric care, international Graduate Summer Schools on "Knowledge Production in Modern Societies," and the Artificial Intelligence for Work and Learning in the Karlsruhe Region (KARL) project. She also contributes to KIT's Umbrella Strategy 2025 on Human Resources (Subproject 7: Visions of Future Work), Kopernikus: SynErgie Cluster VI on organizational development with fluctuating energy supply, and the "NoWa – Norms in demographic change" project, demonstrating her engagement with both theoretical and practical aspects of technology assessment.