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Andreas ReichView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Augmented Reality
• +3 more

Andreas Reich is a Researcher at the University of Hohenheim's Department of Communication Science, focusing on media research and media use. He works on the 'DeLLFi' project and holds a Master's degree from Furtwangen University. His research interests include artificial intelligence, machine learning, augmented reality, and network analysis applied to media and communication contexts. He has received notable awards for his master's thesis on automated programming using node trees and an AR smart mirror project in interaction design. Key research contributions include developing benchmark datasets for video-slide alignment, studying AI-driven conversational agents' ethical frameworks, analyzing protest movements' digital organization on Telegram, and exploring onboarding strategies for educational chatbots. His technical work spans program synthesis via reinforcement learning and neural networks, as well as 3D object generation using node-based systems. Awards: Media Prize (2021) - Furtwangen University Master's Prize (2020) - Furtwangen University Expertise Areas: Program synthesis, augmented reality applications, network analysis, educational chatbots, multimodal data alignment, and generative AI. Consultation hours are available by appointment via email.

Jan C. SchuhrView profile

Professor

• Criminal Law
• Medical Criminal Law
• Legal Philosophy
• +5 more

Prof. Dr. Jan C. Schuhr is a Professor and Chair of Criminal Law and Criminal Procedure Law, Medical Law, and Legal Philosophy at Heidelberg University's Faculty of Law. He leads the Institute for German, European and International Criminal Law and Criminal Procedure Law, and co-directs the IMGB Institute for Medical Law. His academic career includes habilitation (2015), roles as acting professor (2015–2017), and directorships since 2017. He holds a doctorate from Friedrich-Alexander University of Erlangen-Nuremberg (2005) and has been a practicing lawyer (part-time, 2005–2017). Research focuses on legal certainty, medical criminal law, AI ethics in law, and legal philosophy. Notable awards include the Doctoral Prize (2006) and Staedtler Prize (2005). He co-authored major textbooks like Criminal Law, Special Part 2: Property Crimes and edited volumes on legal theory and digital law. His recent work addresses automation, robotics, and ethical dilemmas in law. Prof. Schuhr advises doctoral students like Philipp Weng and contributes to the Digital Law Doctoral Program. His teaching spans criminal law, medical law, and legal philosophy seminars. Key roles include Dean of Studies (2018–2024) and Research Council member.

Franz Christopher KunzeView profile

Researcher

• Industrial Automation
• Alarm Management Systems
• Causal Analysis
• +3 more

Franz Christopher Kunze is a Researcher at the Department of Automation Technology, Faculty of Electrical Engineering and Information Technology, Ruhr-Universität Bochum. His work focuses on developing advanced alarm management systems for industrial processes using causal directed graphs and machine learning techniques. He collaborates with TU Munich on the DFG-funded CausAlITI project, aiming to improve root-cause analysis during industrial alarm floods. Research interests include causal analysis of alarm patterns, adaptive system modeling, and operator-centric visualization. He teaches courses like Process Automation and Programming for ETIT/ITE students, and supervises bachelor's theses in alarm system development. His publications (2023–2025) address alarm flood dynamics, causal graph applications, and automated model generation using LLMs. Current work emphasizes real-time alarm processing and uncertainty management in industrial systems.

Paul BossauerView profile

Researcher

• Mobility Research
• Data Markets
• Innovation and Information Management
• +4 more

Paul Bossauer is a Researcher and Managing Director at the Institute for Consumer Informatics at the University of Applied Sciences Bonn-Rhein-Sieg. His work spans Mobility Research, Data Markets, Innovation and Information Management, Usability & User Experience, Blockchain & Cryptocurrencies, and Platform Economy. Research Focus Mobility Research: Sustainable transport systems, shared mobility, autonomous vehicles. Data Markets: Data governance, privacy, value creation through consumer data. Blockchain: Applications in sharing economy and trust mechanisms. User Experience: Design of digital interfaces, usability in emerging technologies. Publication Trends His recent work emphasizes machine learning applications in mobility, blockchain for decentralized systems, and user acceptance of autonomous vehicles. Collaborative projects focus on open-source mobility platforms like MIAAS and trust frameworks for peer-to-peer carsharing. Scientific Contributions CHI '19 Honorable Mention for co-design studies on autonomous vehicles Best Paper Award (2017) for autonomous public transport research Co-editor of "Verbraucherinformatik: Grundlagen und Anwendungsfelder der digitalen Konsumgesellschaft" (2024)

Alexander AsterothView profile

Professor

• Machine Learning
• Stochastic and Probabilistic Learning Methods
• Surrogate Modelling
• +5 more

Prof. Dr. Alexander Asteroth is a Professor of Computer Science at the Department of Computer Science , Hochschule Bonn-Rhein-Sieg (H-BRS). His work bridges Machine Learning , Surrogate Modeling , and Aerodynamic Analysis through interdisciplinary collaborations with the Institute of Technology, Resource Conservation, and Energy Efficiency (TREE) . Project leadership roles in GARRULUS (drone-based reforestation), eTa (sustainable mobility), and ELaBoR (EV charging infrastructure). Research themes: Quality Diversity Algorithms for design exploration, Bayesian Optimization , and AI in Sports Science . His publications (2017–2025) demonstrate expertise in evolutionary computation , generative models , and human-AI co-creativity . Collaborations with industry partners like GKN Driveline and academic peers (Houben, Sebastian; Hagg, Alexander) underscore his applied research focus on energy-efficient systems and sports performance modeling.

Olga MoskatovaView profile

Professor

• Media Theory
• Visual Culture
• Media Aesthetics
• +12 more

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.

Paul TschisgaleView profile

Researcher

• Physics Education
• Machine Learning in Science Education
• Problem Solving Assessment
• +1 more

Paul Tschisgale is a postdoctoral researcher in Physics Education at the Leibniz Institute for Science and Mathematics Education (IPN) , Kiel. He holds a doctorate from Kiel University (2024) and advanced degrees in Education from University of Potsdam (M.Ed. 2020, B.Ed. 2018). Current projects: WasP (web app for physics problem-solving feedback) and IPhO (International Physics Olympiad) Research focus: AI-based feedback systems, machine learning in science education, and assessment of problem-solving abilities His recent publications (2024-2025) analyze physics problem-solving sequences, compare machine learning models for concept assessment, and explore personalized support for science competition participants. Key subfields include process mining, sequence analysis, and educational data mining. Active in methodological research, he applies computational approaches to understand student cognition in physics. Contact: tschisgale@leibniz-ipn.de

Boris KoldehofeView profile

Professor

• Distributed Systems
• Computer Networks
• Software-defined Networking
• +4 more

Prof. Dr. Boris Koldehofe is a Full Professor of Distributed and Operating Systems at the Technical University of Ilmenau, Germany since 2023. Previously, he held Full Professor of Computer Networks at the University of Groningen (2020–2023) and served as a Senior Researcher/Lecturer at Technical University of Darmstadt (2014–2020). He is also an Adjunct Professor at TU Darmstadt since 2020. Research Focus: Distributed Systems, Software-defined Networking, Complex Event Processing (CEP), Fog Computing, Privacy Engineering Education: Habilitation in Communications and Distributed Systems (2019), PhD in Computer Science (2005) Research Trends : His 2024–2023 publications emphasize analog/memristor-based network computing , privacy-preserving CEP , and time-sensitive networking . Key themes include programmable data planes , AI-driven infrastructure , and energy-efficient systems . Recent work explores federated learning in vehicular networks and privacy engineering for IoT analytics . Projects & Leadership : He leads the DFG CRC 1053 MAKI (2014–2024) and the Parrot: Privacy Engineering for IoT project (2021–). He serves as principal investigator for dynamic Industry 4.0 communication networks and has managed collaborations with institutions like EPFL, University of Stuttgart, and University of Heidelberg.

• Machine Learning Security
• Adversarial Machine Learning
• AI for Healthcare
• +5 more

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.

• Machine Learning
• Computer Science
• Artificial Intelligence
• +3 more

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
• +4 more

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.

• Database Systems
• Natural Language Processing
• Data Mining
• +4 more

Hongrae Lee is a researcher specializing in database systems, natural language processing, and data mining. His work bridges structured data management with language models, focusing on tasks like natural language to SQL translation, similarity joins, and efficient data processing. His research interests include: Database query optimization and similarity search Language model applications for text generation and hallucination correction Web data curation and structured data ecosystems Cloud storage optimization and distributed systems Hongrae Lee's recent publications (2022-2023) highlight trends in large language models (LLMs) for dialogue applications, attributed text generation, and acronym disambiguation with weak supervision. Earlier work (2016-2007) established foundational techniques in database scalability, LSH-based similarity estimation, and geographical data thinning. He has collaborated extensively with researchers at institutions like Google, Seoul National University, and University of British Columbia on projects such as WebTables, LaMDA, and Google Fusion Tables. His contributions span both theoretical advancements (e.g., variance-aware query optimization) and practical systems (e.g., CloudRAMSort, T5-based disambiguation).

• Empirical Software Engineering
• Software Process Improvement
• Requirements Engineering
• +7 more

Andreas Jedlitschka is a researcher at Fraunhofer IESE in Kaiserslautern, Germany, affiliated with the University of Kaiserslautern. He has made significant contributions to empirical software engineering, software process improvement, and experience management. His research focuses on evidence-based decision support, technology transfer, and quality in agile and AI-driven software development. PhD, University of Kaiserslautern (2009) His research interests include empirical software engineering, requirements engineering, software quality, experience management, and AI in software development. He investigates how to improve software processes through data-driven methods, empirical validation, and knowledge reuse. His work often bridges industry and academia, emphasizing practical applicability. The recent articles highlight a growing focus on AI-enabled systems, sustainability in software engineering, and data-driven technical debt management. His work spans both theoretical frameworks and industrial case studies, particularly in agile environments and safety-critical systems. He has co-organized workshops and edited conference proceedings, indicating active participation in the research community. He mentors researchers through collaborative projects and has contributed to doctoral research frameworks. His work is supported by collaborations across Europe, particularly in EU-funded projects related to software quality and AI. He is involved in initiatives like the Q-Rapids project, which supports decision-makers in managing quality in rapid software development, and contributes to building AI innovation labs with industry partners.

• Mathematics Education
• Educational Didactics
• Diagnostic Tools in Education
• +4 more

Prof. Dr. Michael Kleine is a Professor of Mathematics Education at the Faculty of Mathematics, Bielefeld University since 2012. He specializes in mathematics didactics, focusing on developing diagnostic tools for identifying at-risk students, integrating origami into mathematical teaching, and exploring linguistic factors in mathematical task design. Research interests include enactive learning, blended learning environments, and mathematical modeling Actively involved in international collaborations (KMTUT Bangkok, Peking University) and ERASMUS+ projects Previously served as Professor at Weingarten University of Education (2008-2012) Recent publications emphasize experimental mathematics, geometric reasoning through origami, and linguistic complexity in mathematical tasks. He developed the DITOM diagnostic framework for early identification of mathematical learning challenges. Prof. Kleine combines theoretical research with practical teacher training, particularly for secondary education. His work bridges cognitive psychology, educational technology, and curriculum innovation.

Horst LichterView profile

Professor

• Software Engineering
• Software Architecture
• Test Automation
• +4 more

Horst Lichter is a Professor at RWTH Aachen University in the Department of Software Construction , focusing on software engineering, architecture, and process improvement. He actively participates in international conferences like ENASE and APSEC , contributing to workshops on quantitative approaches to software quality (QuASoQ) and continuous software engineering. Email: lichter@swc.rwth-aachen.de Office: Room 4104, Building E1, RWTH Aachen Recent Conference Memberships: PC Member for QuASoQ 2024 , APSEC-ERA 2025 , IWCT 2025 His research spans software architecture reconstruction , test automation , enterprise architecture debt , and model-driven engineering . Recent publications address challenges in software modernization, security design patterns for ML systems, and carbon footprint optimization in cloud environments. Key article trends include: 2025 studies on architecture debt prioritization , security pattern recommendation , and ML prototype tooling 2024 work on enterprise architecture debt frameworks , cloud cost optimization , and integration testing 2023-2020 publications on combinatorial testing , architecture evolution , and technical debt analytics Horst Lichter leads the Software Construction Research Group , mentoring students and collaborating on projects like automated test generation, enterprise architecture modeling, and agile process improvement. His team develops tools for metric systems, model libraries, and continuous delivery pipelines.