جست‌وجوی استاد و پژوهشگر

Olga Moskatovaمشاهده پروفایل

استاد

• Media Theory
• Visual Culture
• Media Aesthetics
• +۱۲ مورد دیگر

Prof. Dr. Olga Moskatova is Professor of Media Theory at HfG Offenbach (Offenbach University of Art and Design), where she teaches and researches in the Department of Art. She previously held a junior professorship in media studies (visuality and image cultures) at Friedrich-Alexander University Erlangen-Nuremberg from 2018 to 2023. Her academic journey includes research at the Bauhaus University Weimar and a doctorate from the Berlin University of the Arts. PhD in Media Theory, Berlin University of the Arts, 2017 Studies in Social and Business Communication, Berlin University of the Arts & Université Stendhal 3 Grenoble Her research centers on the theory and aesthetics of visual media, with a focus on media materiality, new materialism, networked image cultures, avant-garde and media art, and the emerging concept of protective media and immunization dispositifs. She critically examines how AI technologies shape visual culture, particularly in surveillance capitalism and algorithmic image production. The most recent publications and projects reflect a strong engagement with AI and visual media, exploring how machine learning algorithms classify, generate, and moderate images across platforms. Her editorial work includes significant volumes on video conferencing, televisual seriality, and messy image networks, indicating a sustained interest in digital transformation and media infrastructures. Monograph: Painting on Celluloid: On Relational Materialism in Cameraless Film (2019) Monograph: Human-machine eye: The motif of artificial vision in film (2009) Editor: Video Conferencing: Practices, Politics, Aesthetics (2023) Guest Editor: # messy images , Networked Images in Surveillance Capitalism She founded the international research network 'AI and Visual Media', bringing together scholars from media studies, data studies, and contemporary art to investigate the societal and cultural impacts of visual AI. Her work emphasizes interdisciplinary methodologies, including data set archaeology and media ecology analysis. She has presented lectures at institutions such as Yale University, highlighting the global reach of her research. Olga Moskatova leads research initiatives rather than traditional lab structures, fostering collaborative inquiry into the politics inscribed in AI infrastructures. Her recent workshop 'On Protective Media' explores boundary-forming mechanisms in digital culture, indicating a forward-looking trajectory in theoretical media studies.

Christian Meltebrinkمشاهده پروفایل

استاد

• Intelligent sensor systems
• Physics
• Automated and autonomous agricultural systems
• +۱۲ مورد دیگر

Prof. Dr.-Ing. Christian Meltebrink is a faculty member at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on intelligent sensor systems , autonomous agricultural machinery , and safe environmental perception for outdoor robotics. Key research areas include: Validation methodologies for sensor systems in variable conditions Humanoid test bodies for autonomous vehicle safety Reproducible simulation of agricultural interference factors Embedded systems for field robots Optoelectronic sensor technology Recent publications address sensor validation for autonomous machines, with emphasis on detection reliability and environmental perception . His work integrates CCD/CMOS imaging , spectral analysis , and sensor fusion for agricultural applications. As head of the Laboratory for Intelligent Sensor Systems (located in buildings SO and SP, Sedanstraße 26, Osnabrück), he leads projects like "Amaizeing" field robot and high-speed sensor chip development . Collaborative efforts extend to industry partners (e.g., SICK AG) and institutions like Technische Universität Berlin. Teaching responsibilities include Physics I/II and Sensor Systems for engineering students. His laboratory delivers hands-on research opportunities in imaging technology , autonomous systems , and embedded sensor solutions .

• Computer Science
• Multimodal Learning
• 3D Vision
• +۴ مورد دیگر

Junjie Fei is an active researcher in Computer Science with a focus on Multimodal Learning , Vision-Language Understanding , and Retrieval-Augmented Generation . They have collaborated with prominent researchers like Mohamed Elhoseiny , Sanjoy Chowdhury , and Jun Chen on various projects. Key research contributions include: Part-Aware 3D Vision-Language Understanding (2024) with novel tasks like Part-Aware Point Grounding MAGNET (2025) - a multi-agent framework for audio-visual retrieval WikiAutoGen (2025) - automated Wikipedia-style multimodal article generation V-RAG (2024) - vision-centric retrieval-augmented generation for document haystacks While no explicit academic affiliation or awards are stated in the provided text, their work spans 3D object segmentation , multi-video temporal grounding , multimodal knowledge integration , and large document reasoning , indicating significant contributions to AI research.

• Information Retrieval
• Large Language Models
• Parameter Efficient Fine-Tuning
• +۵ مورد دیگر

Marco Braga is a researcher focused on information retrieval, large language models, and parameter-efficient fine-tuning techniques. His work spans personalized community question answering systems, synthetic data generation, and adapter-based model optimization. Recent research interests include: Zero-shot learning and task arithmetic for LLMs Context-aware personalization in retrieval systems Hybrid neural-symbolic approaches for structured reasoning Telecommunications domain modeling Key article trends show expertise in: Adapter modules and low-rank parameter optimization Community question answering personalization Synthetic dataset creation for training LLMs Model efficiency in resource-constrained settings Collaborations include Gabriella Pasi, Pranav Kasela, and Alessandro Raganato. His work appears in SIGIR, COLING, WI-IAT, and IIR conferences, with notable Zenodo dataset contributions.

• Machine Learning Security
• Adversarial Machine Learning
• AI for Healthcare
• +۵ مورد دیگر

Xiaojun Xu is a Professor at the School of Computer Science, Beijing Institute of Technology, with a prolific research career spanning machine learning security, medical AI, and robotics. Their work demonstrates strong interdisciplinary collaboration across computer science, healthcare, and engineering domains. Institution: Beijing Institute of Technology, School of Computer Science Research Focus: AI security, medical imaging, robotics, and remote sensing applications Collaborations: Extensive work with Bo Li (29 papers), Dawn Song (11 papers), and medical researchers Xu's research interests center on adversarial machine learning, with significant contributions to model security, backdoor detection, and LLM unlearning. They've pioneered techniques like Meta Neural Analysis for Trojan detection and developed frameworks for certified robustness. Their medical imaging work focuses on quantitative susceptibility mapping for neurodegenerative diseases, particularly Parkinson's and Alzheimer's. In robotics, they've advanced control systems for quadruped and amphibious vehicles. Recent publications reveal a strong trend toward large language model security, with multiple 2024-2025 papers on machine unlearning and watermarking techniques. Their work bridges theoretical security with practical healthcare applications, particularly in medical image analysis where they've developed tools for subcortical nucleus segmentation and brain age prediction. Key venues: NeurIPS, CCS, IEEE S&P, NeuroImage, IEEE Transactions Research impact: High citation count with consistent top-tier publication record Xu has secured significant research funding, evidenced by the volume and diversity of publications across multiple domains. Their work on blockchain-enabled IoT systems and RAFT-based private blockchain demonstrates expertise in distributed systems. The medical imaging research shows strong hospital collaborations, particularly in developing tools for Parkinson's diagnosis. Current projects appear focused on LLM security challenges and multimodal medical AI systems with potential clinical applications.

• Human-Computer Interaction
• 3D User Interfaces
• Gesture Recognition
• +۳ مورد دیگر

Dr. Ravin Balakrishnan is a leading figure in Human-Computer Interaction at the University of Toronto, Canada. With over 163 publications from 1994-2023, his work spans 3D interfaces , gesture recognition , mobile computing , and collaborative systems . He received the CHCCS 2020 Achievement Award for his contributions. Key research areas: Human-Computer Interaction, 3D User Interfaces, Tactile Displays, Mobile Computing Major awards: CHCCS 2020 Achievement Award Collaborators: Tovi Grossman, Daniel Wigdor, Karan Singh Publication Trends (2023-2006): 2023: Interactive camera robots for video capture 2022: Swarm robotics for physical demonstrations 2021: Drone tour interfaces and VR navigation 2020: Telepresence drones and volumetric displays 2019: Multimedia education tools 2016: Dual-screen interaction and tactile feedback 2011: Curve sketching and children's interfaces 2008: Spherical displays and tactile widgets 2006: Volumetric display techniques His work bridges academic research with real-world applications in education, accessibility, and collaborative environments. Publications in top venues like CHI, UIST, and ACM Transactions demonstrate sustained impact in interaction design.

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

Ke Huo is a researcher focused on advanced interactions in Augmented Reality (AR) , Robotics , and Sensor Systems . His work bridges physical and digital environments through innovative tools like GhostAR , V.Ra , and iSoft , enabling intuitive authoring of context-aware applications. Research Interests : Augmented Reality (AR) systems for collaborative task planning Soft sensor technology with multimodal sensing Context-aware robotics and IoT integration 3D design ideation in mixed reality Autonomous driving decision-making frameworks Publication Trends : 2014–2024: 15+ papers on AR, sensor design, and human-robot collaboration Key venues: UIST , CHI , Sensors , ACM DIS Collaborations with Karthik Ramani, Yuanzhi Cao, and Sang Ho Yoon

• 3D Shape Analysis
• Graph-Based Machine Learning
• Computer Vision
• +۴ مورد دیگر

Andrea Torsello is a researcher at the University of York, specializing in 3D shape analysis, graph-based machine learning, and quantum computing applications in computer vision. His work bridges theoretical and applied domains, focusing on pattern recognition, network thermodynamics, and remote sensing. PhD from University of York (2004) Published extensively in journals like IEEE Transactions and Pattern Recognition Research interests include: Quantum-inspired graph analysis 3D reconstruction techniques Manifold learning for complex networks Thermodynamic modeling of time-evolving systems Key contributions involve: Quantum walk-based graph similarity measures k-Anonymity for graph data Physics-driven CNN models for ocean wave reconstruction Game-theoretic approaches to shape matching

• Machine Learning
• Computer Science
• Artificial Intelligence
• +۳ مورد دیگر

Shaoyu Zhang is a researcher affiliated with the Institute of Automation at the University of Chinese Academy of Sciences . His work focuses on machine learning techniques for addressing data imbalance in visual recognition tasks. Research interests include: Long-tailed learning and imbalanced data handling Mixup and data augmentation strategies Knowledge distillation mechanisms Visual recognition and object detection Key publication trends (2020-2024) show expertise in: Developing teacher-student learning frameworks Designing probability space alignment methods Improving model robustness through balanced training Advancing few-shot representation learning

• Artificial Intelligence
• Multi-Agent Systems
• Evolutionary Computation
• +۴ مورد دیگر

Jörg Denzinger is a Professor at the University of Calgary, Canada, specializing in artificial intelligence, multi-agent systems, and automated testing. His work bridges evolutionary computation, cybersecurity, and game-based simulations, with a focus on emergent behavior and cooperative strategies. Key Research Areas: Multi-Agent Systems, Evolutionary Algorithms, Automated Theorem Proving, Game AI, Data Mining, Cybersecurity, Distributed Systems. Recent Trends: He has concentrated on applying exploratory evolutionary testing to security policy optimization, using deep learning for medical imaging, and modeling emergent coordination in self-organizing systems. Scientific Contributions: Developed frameworks for combining security mechanisms and testing multi-agent systems. Explored decentralized control in water distribution networks and adaptive risk management. Contributed to code reuse tools like Jigsaw and structural correspondence analysis.

• Software Engineering
• Code Readability
• API Compatibility
• +۵ مورد دیگر

Rocco Oliveto is a prominent researcher in software engineering with extensive contributions across multiple domains including code quality assessment, smart contracts, Docker configuration analysis, and healthcare applications of AI. His collaborative work spans numerous institutions, with frequent co-authorship with researchers such as Simone Scalabrino, Gabriele Bavota, and Emanuela Guglielmi. Dr. Oliveto's research interests focus on practical software engineering challenges with emphasis on code readability, API compatibility, bug prediction, and smart contract maintenance. His work bridges theoretical research with practical applications, particularly evident in recent projects applying machine learning to healthcare diagnostics and video game quality analysis. His research demonstrates a consistent trajectory toward addressing real-world software engineering problems with innovative methodological approaches. Analysis of his recent publications reveals a strong trend toward interdisciplinary research, particularly at the intersection of software engineering and healthcare applications. His work shows increasing focus on practical applications of AI in medical diagnostics, rehabilitation technology, and patient monitoring systems, while maintaining strong contributions to core software engineering topics like code quality and developer productivity. The diversity of publication venues—from top software engineering journals like Empirical Software Engineering and ACM TOSEM to healthcare conferences like BIOSTEC—demonstrates the breadth of his research impact. Dr. Oliveto has demonstrated significant research leadership through numerous collaborative projects, particularly evident in his participation in the QualAI project focused on continuous quality improvement of AI-based systems. His work shows consistent funding support through collaborative research initiatives that bridge academic and practical software engineering concerns.

Zicong (Alex) Fanمشاهده پروفایل

پژوهشگر

• Computer Vision
• hand pose estimation
• human pose estimation
• +۴ مورد دیگر

Zicong (Alex) Fan is a doctoral student at ETH Zurich supervised by Dr. Michael J. Black at the Max Planck Institute for Intelligent Systems (MPI-IS) and Prof. Otmar Hilliges at ETH. He holds the position of Guest Scientist within the Perceiving Systems department at MPI-IS. His academic foundation includes a B.Sc. (2018) and M.Sc. (2020) in Computer Science from the University of British Columbia, Vancouver. During his Master's studies, he conducted research in vision-language problems such as visual grounding and visual commonsense reasoning under Professors Leonid Sigal and Jim Little. Mr. Fan's research specializes in hand and human pose estimation with emphasis on modeling human-object and human-scene interactions . His work extends to egocentric computer vision , focusing on first-person perspective analysis through wearable devices. These areas represent cutting-edge applications of deep learning in embodied AI systems. He operates within the Perceiving Systems research group at MPI-IS, leveraging interdisciplinary collaborations between ETH Zurich and the Max Planck Society to advance human-centric computer vision methodologies.

• Supply Chain Management
• Information Systems
• Digital Twins
• +۶ مورد دیگر

Professor Dr. Tobias Engel is affiliated with Neu-Ulm University of Applied Sciences (HNU) as a faculty member in the School of Information Management , specializing in Supply Chain Management . His work bridges academic research with practical applications in digital transformation and sustainability. PhD from Technische Universität München (2015) Active in international conferences (AMCIS, MWAIS, POMS) since 2010 Key research areas: Supply Chain Analytics, Digital Twins, RFID Systems, Lean Management Recent research focuses on merging digitalization with supply chain sustainability through AI-based verification systems , large language models , and digital twin frameworks . His 2025 work on sustainability maturity models and multilingual manufacturing support demonstrates ongoing innovation. Article trends show consistent emphasis on data-driven optimization (2011-2025), with recent shifts toward AI integration (2024-2025) and sustainable practices (2025). Awards: Best Paper Award in Digital Health (2025) Engel contributes to supply chain pedagogy through simulation game methodologies (2023) and collaborates with researchers like Gökhan Cenk and Benjamin Hofmann. His work spans both academic publications and practitioner-focused guides like "Supply Chain Strategy: From Strategy to Operational Excellence" (2020). Current thesis topics include Industrial Metaverse , Digital Twins , and Procurement Innovations , indicating future research directions that align with Industry 4.0 advancements.

Felix Gnisaمشاهده پروفایل

پژوهشگر

• Digitalization and mechanization of the world of work
• Democratization of work and economy
• Political economy of digitalization
• +۱۰ مورد دیگر

Felix Gnisa serves as a Scientific Assistant at the Research Group 'Digital Technologies and Social Change' within the Institute for Technology Assessment and Systems Analysis (ITAS) at Karlsruhe Institute of Technology, while simultaneously pursuing his doctorate at the Department of Labor, Industrial and Economic Sociology at Friedrich Schiller University Jena under Prof. Klaus Dörre's supervision. His dual institutional affiliation bridges empirical technology assessment with critical sociological theory. His educational trajectory includes a BA in History from the University of Leipzig (2010-2014) and an MA in Social Theory from Friedrich Schiller University Jena (2015-2018). This foundation supports his reconstructive-hermeneutic approach to analyzing digital transformation's impact on labor relations, workplace democracy, and technological design processes. Gnisa's research program critically examines how digital platform technologies reshape labor processes, with particular focus on worker perspectives in technology design. His publications reveal a consistent thread connecting Frankfurt School critical theory with contemporary analyses of digital capitalism, platform cooperatives, and strategic co-determination mechanisms. The recurring themes across his work include the subsumption of communication by digital platforms, class positioning of tech workers, and historical continuities between 1970s industrial alternative movements and current digital transformation initiatives. His scholarly contributions demonstrate methodological versatility across conceptual framework development, empirical case studies of platform cooperatives like CoopCycle, and critical policy analysis of technology governance. The interdisciplinary nature of his work positions him at the intersection of sociology of work, science and technology studies, and critical political economy. Key recognition includes: Rosa-Luxemburg-Stiftung doctoral fellowship (2020-present) Membership in the Center for Emancipatory Technology Research Gnisa actively contributes to academic discourse through conference presentations at venues including the International Labour Process Conference and STS conferences. His collaborative work with Philipp Frey and Linda Nierling has produced influential analyses of democratic technology design in workplace contexts. While currently focused on completing his dissertation on 'socio-structural aspects in the technological visions of artificial intelligence developers,' his research program consistently advocates for worker-centered approaches to technological development. His institutional engagement spans multiple research ecosystems, from the Weizenbaum Institute for the Networked Society to the DFG Research Group Post-Growth Societies, demonstrating commitment to both theoretical innovation and practical applications for workplace democracy in the digital age.

• Information Systems
• Digital Transformation
• Business Process Management
• +۶ مورد دیگر

Prof. Dr. Martin Gersch is a faculty member at the Department of Information Systems, School of Business & Economics, Freie Universität Berlin. His research spans digital transformation, business process management, service engineering, and e-health, with a focus on institutional logics and technology-driven change. He has secured significant external funding, including the Einstein Center Digital Future (ECDF) and Junior Professorships in Digital Transformation. Appointed to habilitation at Ruhr University Bochum (2006) Founded Competence Center E-Commerce at Ruhr University Bochum (2000) Visiting Professorial Fellow at University of Sydney, UNSW, and QUT (2012) His research explores digital transformation , ICT in integrated care , business model innovation , and entrepreneurial hubris . Recent work includes agent-based simulations for healthcare information sharing, API management in digital service systems, and institutional tensions in health data spaces. Key trends in his publications include health informatics , service blueprinting , and digital education . He has contributed to entrepreneurship education and blended learning frameworks , with a focus on medical device education and cross-sector collaboration.