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Daniel StrüberView profile

Associate Professor

• Model-Driven Engineering
• AI Engineering
• Software Quality Assurance
• +7 more

Daniel Strüber is an Associate Professor in Software Engineering at Chalmers University of Technology and the University of Gothenburg, Sweden, and an affiliate of the Department of Software Science at Radboud University Nijmegen, Netherlands. His research focuses on model-driven engineering, AI engineering, and empirical software engineering, addressing challenges in software quality, variability management, and security. He holds a PhD (summa cum laude) from Philipps University Marburg and has held academic positions including Assistant Professor at Chalmers and postdoctoral roles at King's College London and Philipps University Marburg. Education: PhD in Computer Science, Philipps University Marburg (2011–2016) Diplom (M.Sc. equivalent) in Computer Science, Philipps University Marburg (2005–2011) Research Interests: Strüber's work spans model-driven engineering, AI integration in software systems, and empirical studies. Key areas include: Model-based languages and tools for software quality assurance Machine learning-enabled systems and their engineering principles Robotics software engineering and variability management Formal methods for consistency and security Search-based optimization in model-driven development Recent Contributions: Recent work includes studies on LLM-assisted reverse engineering, empirical analysis of manual abstraction in software systems, and model-driven approaches for edge deployment of ML models. His research bridges theory with practical implementations in domains like robotics and web-based systems. Awards: Best Reviewer Award (SoSyM 2025) Distinguished Reviewer Awards (GPCE 2024, SPLC 2023) Transformation Tool Contest Awards (2020, 2016) EASST Best Paper Awards (ICGT 2020, ICGT 2017) Grants & Leadership: Co-editor of a JSS special issue on Product Line Engineering (2024) and program co-chair of SPLC 2024. Active in conferences like ICSE, ICGT, and MODELS. Leads grants such as the VR SEMLA project (2022–2024) and the DFG EUphORia fellowship (2019). Teaching: Courses include 'Software Engineering for AI Systems' at Chalmers and 'Software Product Lines' at Radboud University. Involves students in real-world projects via GiPHouse, a student-run company.

• Educational Technology
• Collaborative Learning
• Computer-Supported Learning
• +3 more

Markus Kuhn is a researcher at the University of Cambridge with a diverse academic career spanning over two decades, evidenced by publications from 2003 to 2023. His work bridges educational technology, computer science, and autonomous systems, demonstrating significant interdisciplinary reach across multiple domains. Dr. Kuhn's research has evolved through distinct phases. Initially focused on educational technology (2003-2007), he published extensively on classroom scenarios, collaborative learning, and media integration. His work then broadened to include physics computation (2008) and process support for inquiry learning (2010). More recently (2016-2023), his research has shifted toward autonomous systems, with publications on self-localization technologies and map generation for autonomous vehicles. This publication trajectory reveals a researcher who has successfully adapted his expertise from educational applications to more technical domains while maintaining connections to his foundational work in learning systems. His research demonstrates consistent innovation in applying computational approaches to solve practical problems across different contexts. Dr. Kuhn has maintained long-term collaborations with researchers including Heinz Ulrich Hoppe, Andreas Harrer, and Andreas Lingnau, indicating strong interdisciplinary networks. His work has been published in diverse venues ranging from IEEE Access and Microelectronics Reliability to Research and Practice in Technology Enhanced Learning and International Conferences on Computer-Supported Collaborative Learning.

• Human-Computer Interaction
• Wearable Technology
• Artificial Intelligence
• +4 more

Friederike Fröbel is a Researcher at the German Research Center for Artificial Intelligence (DFKI) within the Design Research eXplorations group, affiliated with the interdisciplinary Design Research Lab network spanning Berlin University of the Arts, DFKI, Weizenbaum Institute, and Einsteincenter Digital Future. Her work focuses on socially and ecologically sustainable tools at the intersection of technology, materials, and social practices to advance digital inclusion through participatory design. She completed her Master of Science degree in 2018 with a thesis titled 'Smart Fashion as Mobile User Interface' under Dr. Katharina Bredies' supervision, building foundational expertise in wearable interfaces. Fröbel's research centers on human-computer interaction with emphasis on wearable technology, sustainable digital solutions, and community empowerment. She pioneers low-cost hardware and free/libre open source software (FLOS) applications to create accessible interfaces, particularly for minoritized groups. Her methodology prioritizes co-creation workshops and grassroots innovation to address real-world challenges in local development and networked societies. Analysis of her 2019-2023 publications reveals consistent integration of machine learning with textile-based interfaces (e.g., MYOW, UPLINX projects) and community-driven sustainable development (MINODU, Miadé initiatives in Togo). A dominant trend is adapting technology for pandemic-era constraints while maintaining participatory design principles, demonstrating resilience in digital inclusion efforts across diverse contexts. As a core member of the Design Research Lab, Fröbel contributes to projects including DDAI (Design-driven AI), MYOW (wearable creation platforms), and Minodu. The lab operates as a dynamic network of designers, researchers, and critical thinkers committed to ecological sustainability, gender-inclusive design, and material interactions, hosting events like virtual colloquia on AI ethics and digital sovereignty.

• Natural Language Processing
• Symbolic AI
• Autonomous Driving
• +1 more

André Schamschurko is a Researcher at the Technical University of Munich's Chair of Robotics, Artificial Intelligence, and Real-time Systems, having joined in July 2024. His work bridges artificial intelligence and autonomous systems development, with a focus on practical applications in robotics and real-time computing environments. He earned a Diploma in Computer Science from TU Dresden, specializing in Natural Language Processing and Symbolic AI during his academic training. This foundational expertise shapes his current research trajectory in cognitive systems and AI-driven solutions. His research concentrates on Natural Language Processing, Symbolic AI, Autonomous Driving, and Large Language Models, with particular emphasis on leveraging LLMs for safety-critical validation in autonomous vehicle systems. This includes developing novel testing frameworks that generate complex driving scenarios through language model interactions. His publication on LLM-driven testing demonstrates a clear research trend toward integrating language models with robotics for enhanced scenario validation, reflecting broader industry movements toward AI-assisted development in autonomous systems. This work addresses critical gaps in safety verification for self-driving technologies. As part of Prof. Alois Knoll's research team, he contributes to the Chair's mission of advancing real-time AI systems, robotics perception, and computational intelligence. The group maintains strong industry partnerships focused on autonomous vehicle development and validation frameworks. Current teaching responsibilities include assisting in 'Cognitive Systems' and 'Advanced Foundation and Perception Models in Autonomous Driving' courses, though no student advisement or grant information is publicly documented.

Mario GünzelView profile

Researcher

• Real-Time Systems
• Embedded Systems
• Self-Suspension
• +4 more

Mario Günzel is a Researcher at the Department of Computer Science, Faculty of Computer Science at Technical University of Dortmund, where he works in the Design Automation for Embedded Systems research group under Prof. Dr. Jian-Jia Chen. Having completed his PhD in 2024 with a dissertation on property-based timing analysis of distributed real-time systems, he has established himself as a leading researcher in real-time systems with over 30 publications and multiple prestigious awards including Best Paper Awards at ECRTS 2023 and EMSOFT 2024, as well as an Outstanding Paper Award at RTSS 2024. PhD in Computer Science, Technical University of Dortmund (2024) M.Sc. in Mathematics, University of Duisburg-Essen (2019) B.Sc. in Mathematics, University of Duisburg-Essen (2017) Günzel's research focuses on the theoretical and practical aspects of real-time systems, particularly in self-suspending tasks and end-to-end latency analysis of cause-effect chains. His work bridges formal methods with practical applications in embedded systems, automotive systems, and robotics. He has developed evaluation frameworks like SSSEvaluation and E2EEvaluation that have become important tools in the real-time systems community. His research demonstrates exceptional depth in both theoretical foundations and practical implementations, with significant contributions to scheduling algorithms, timing analysis, and real-time operating systems. The recent publication trend shows Günzel's expanding research scope from core real-time scheduling problems to applications in automotive systems, electric vehicle scheduling, and ROS 2 integration. His work consistently addresses fundamental challenges in real-time systems while developing practical solutions with real-world applicability. The publications reveal a strong focus on eliminating timing anomalies, optimizing priority assignments, and developing distribution-agnostic analysis methods that advance the theoretical foundations of real-time systems. Outstanding Paper Award at IEEE RTSS (2024) Best Paper Award at ACM EMSOFT (2024) Best Paper Award at ECRTS (2023) Outstanding Bachelor Thesis Award (2017) UDE Scholarship (2014-2019) Günzel actively supervises bachelor's and master's theses, with recent completed works including 'Odometry and IMU Fusion for Pose Estimation' and 'Evaluation Framework for End-to-End Analysis'. He has served in various organizational roles for major conferences including Publicity Co-Chair for RTSS 2025 and Publicity Chair for ECRTS 2025. His service to the community extends to editorial roles for journals including Real-Time Systems Journal and ACM Transactions on Embedded Computing Systems. Under Günzel's supervision, the research group maintains important open-source tools including SSSEvaluation (Evaluation Framework for Schedulability of Self-Suspending Tasks) and E2EEvaluation (Evaluation Framework for End-to-End Latency of Cause-Effect Chains), which have been adopted by researchers worldwide. He is also involved in collaborative projects with institutions including Scuola Superiore Sant'Anna in Pisa (planned research stay Feb-Apr 2025) and Eindhoven University of Technology.

• Software Engineering
• Formal Methods
• Model-Driven Development
• +3 more

Alexander Raschke is a Professor at the University of Ulm, affiliated with the Institute of Software Engineering and Compiler Construction. His research focuses on formal methods, model-driven engineering, software safety and security, and abstract state machines. He has authored/co-authored over 50 publications, including works on collaborative modeling frameworks, security analysis techniques, and programming by demonstration. He holds a PhD in Computer Science from the University of Ulm (2009). As an academic, he has organized and chaired conferences such as ABZ (2020, 2021), demonstrating leadership in the formal methods community. His work bridges theoretical foundations with practical applications in software systems, emphasizing safety-critical and self-adaptive systems. Notable contributions include the development of EditQL—a textual query language for evolving models—and frameworks for attack-tree generation in cybersecurity.

• Computer Science
• Electrical Engineering
• Robotics
• +7 more

Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.

Maria A. WimmerView profile

Professor

• E-Government
• Digital Transformation
• Public Sector Innovation
• +2 more

Prof. Dr. Maria A. Wimmer leads the E-Government research group at the University of Koblenz's Institute for Business and Administrative Information Systems. Her research examines digital transformation in public sector contexts, focusing on interoperability frameworks, smart city initiatives, public procurement modernization, and data governance models. Current projects address regulatory compliance in EU public procurement, explainable AI for policy analysis, pandemic response evaluation using Bayesian methods, and citizen engagement strategies for smart cities. Her work emphasizes semantic modeling for government architectures, data integration across procurement lifecycles, and overcoming barriers to SME participation in digital procurement systems. Recent publications demonstrate interdisciplinary approaches combining public administration theory with technical implementations in AI, data science, and information systems to address governance challenges in digital transformation.

Daniel GöhringView profile

Professor

• Autonomous Vehicles
• Mobile Robotics
• Computer Vision
• +6 more

Prof. Daniel Göhring is a professor in the Department of Computer Science at the Free University of Berlin, leading the Autonomous Cars Lab and part of the Dahlem Center for Machine Learning and Robotics. His research emphasizes robotic perception, object tracking, and real-time planning under computational constraints, with a focus on autonomous vehicles and cooperative systems. Education: Bachelor's/Master's in Robotics (exact program unspecified) PhD in Computer Science at Humboldt University Berlin Postdoctoral Research at International Computer Science Institute (ICSI), Berkeley, CA Research Interests: Daniel's work integrates machine learning and sensor technologies like LiDAR and cameras to address challenges in autonomous driving. Key areas include SLAM algorithms, trajectory prediction, cooperative perception, and real-time systems. He explores how limited sensor data and computational resources can be optimized for dynamic traffic environments. Grants and Projects: Leader of the Autonomous Cars Lab Involved in EU-funded projects such as H2020 HIVEOPOLIS and KIS-M (AI-based mobility systems) Past projects include CRTX (recycling optimization), Open.Make (open hardware), RoboFish (biological swarm analysis), and SAFARI Awards: Best Poster Award at IAAS Workshop 2024 Best Paper Award at ICAIR-CACRE 2019 Teaching: He has taught courses such as Image Processing, Robotics, and Advanced Robotics. Recent semesters include modules on self-supervised learning, autonomous vehicle research, and continuous learning software projects. Labs and Teams: Daniel heads the Autonomous Cars Lab and collaborates with the BioRobotics Lab, focusing on interdisciplinary projects like 'Robots Communicating with Fish' and 'Open Hardware for FAIR Robotics.'

• Virtual Reality
• Augmented Reality
• Educational Technology
• +7 more

Roghayeh Barmaki is a researcher with extensive publications in virtual reality, augmented reality, and educational technology. Her work spans rehabilitation systems, autism therapy, and neuroimaging analysis using fNIRS. She collaborates with institutions including Georgia Institute of Technology and University of Florida. Key Collaborators : Anjana Bhat, Jicheng Li, Shayla Sharmin, Vuthea Chheang Research Themes : Wearable sensors, LSL framework, movement synchrony, multimodal learning analytics Research Interests She focuses on immersive technologies for education and therapy, with expertise in: Functional Near-Infrared Spectroscopy (fNIRS) for cognitive engagement Wearable sensor integration in rehabilitation Gender perspectives in AR/VR education Multimodal affect analysis for autism Privacy-preserving behavioral analytics 3D visualization for anatomy training Publication Trends Recent work emphasizes fNIRS applications in gaming environments and hybrid deep learning models for cognitive analysis. Earlier studies focused on gesture recognition, movement synchrony estimation, and autism intervention datasets. Impactful Projects She developed the MMASD dataset for autism research and contributed to VR therapy systems. Her collaborations include: Georgia Tech's Autism Technology Research University of Florida's TeachLivE virtual classroom Germany's AIxVR conference contributions International collaborations on sensor systems

• Autonomous Systems
• Computer Vision
• Robotics
• +3 more

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems and Director of the Institute for Intelligent Systems at Esslingen University of Applied Sciences, Germany, within the Department of Computer Science and Engineering. His leadership role positions him at the forefront of intelligent systems research in applied academic settings. His research spans autonomous systems, computer vision, and robotics with emphasis on visual-inertial SLAM, semantic segmentation, and collective perception. Key contributions address real-world challenges in unstructured environments like agricultural fields and urban settings through efficient perception systems. Recent work focuses on lightweight monocular solutions, sensor fusion techniques, and computational efficiency optimization for autonomous vehicles. Analysis of his 2024-2025 publications reveals strong trends in collective perception infrastructure, NeRF/Gaussian Splatting integration for SLAM, and multi-sensor dataset development. His research consistently benchmarks computational costs against accuracy improvements while creating valuable resources like the OPNV public transportation dataset and Rover multi-season SLAM corpus. As Director of the Institute for Intelligent Systems, Enzweiler leads initiatives advancing autonomous driving technologies through practical implementations and industry-relevant research frameworks.

Emanuel KitzelmannView profile

Professor

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +4 more

Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.

Nour AliView profile

Professor

• Software Engineering
• Software Architecture
• Adaptive Systems
• +3 more

Dr. Nour Ali serves as a Professor and Vice-Dean of Education in the College of Engineering, Design and Physical Sciences at Brunel University London, where she co-heads the Brunel Software Engineering Lab. With a PhD in Software Engineering from Universidad Politecnica de Valencia and a Computer Science degree from Bir-Zeit University, she brings extensive international experience from previous positions at University of Brighton, Lero (Irish Software Engineering Research Centre), and Politecnico di Milano. PhD in Software Engineering, Universidad Politecnica de Valencia, Spain Major in Computer Science, Bir-Zeit University, Palestine PG Certificate in Teaching and Learning in Higher Education, University of Brighton Fellow of the Higher Education Academy (HEA) Her research focuses on developing software architecture techniques for distributed, mobile, and adaptive systems, with particular expertise in microservice architecture recovery and visualization. With over 70 publications spanning two decades, her work bridges theoretical architecture principles with practical implementation challenges. Recent research demonstrates a strong focus on microservice architecture recovery tools like MiSAR and analysis of service granularity adaptation. Dr. Ali has made significant contributions to the software engineering community through editorial roles including Deputy Editor in Chief of IET Software, committee memberships for major conferences like ICSE and ASE, and reviewer positions for EPSRC and other funding bodies. Her publications reveal consistent contributions across architecture consistency, microservice systems, and adaptive requirements engineering, with recent work increasingly focusing on practical tool development for architecture recovery. External Examiner, York St John University (2021-2025) Deputy Editor in Chief, IET Software Committee member for ICSE, ASE, EASE, ICSA, MOBILESoft conferences EPSRC Full College Member (2018-present) Reviewer for Dutch Research Council (NWO), UK UNESCO Newton Prize As an educator, Dr. Ali leads CS3100 Software Project Management and contributes to multiple undergraduate and postgraduate courses. Her teaching philosophy integrates research insights with practical software engineering skills, supported by her PG Certificate in Teaching and Learning and HEA Fellowship. She actively supervises PhD students and contributes to curriculum development as Vice-Dean of Education for her college.

Tobias KlausView profile

Researcher

• Real-Time Systems
• Distributed Systems
• Operating Systems
• +3 more

Tobias Klaus is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His work focuses on real-time systems, distributed systems, and operating systems, with a particular emphasis on embedded real-time control and multicore processing. He has contributed to projects such as qron OS, AORTA, and the RTSC compiler framework. His research addresses challenges in real-time scheduling, job-level dependencies, and quality-aware system design. Education and Background: No explicit education details provided in the text. Research Interests: Klaus’s research spans real-time systems, distributed architectures, and embedded control systems. He explores topics such as deadline-driven scheduling, static analysis for job migration optimization, and tool-supported design of time-triggered systems. His work often bridges theoretical foundations with practical implementation in embedded and robotics domains. Grants and Funding: No specific grants or funding opportunities are mentioned in the text. Awards: No scientific awards explicitly listed. Teaching and Advising: Klaus has been involved in teaching courses such as Real-Time Systems, System Programming, and Distributed Systems. He has advised multiple theses, including projects on distributed real-time systems, WCET analysis, and multicore resource protocols. Notable advisees include Hausmann Jannis, Florian Güthlein, Thomas Reichinger, and Felix Bräunling. Labs and Teams: He contributes to interdisciplinary projects like the I4 Copter quadrocopter system and collaborates on frameworks such as RTSC and AORTA. His work often involves cross-disciplinary efforts in real-time control and embedded systems.

• Software Engineering
• Embedded Systems
• Mobile Systems Development
• +4 more

Prof. Christian Bunse serves as Vice Dean and Professor at the Faculty of Electrical Engineering and Computer Science, Stralsund University of Applied Sciences. His subject area focuses on Software Systems, teaching courses such as Programming Technology I and Laboratory Practical Software. He leads the Labor Management and New Venture lab, specializing in software engineering methodologies, model-driven development for embedded/mobile systems, and energy-efficient solutions. Research interests include UAV/robotics, swarm intelligence, and usability engineering. He offers academic course leadership and maintains an office at Room 306c, House 4. Contact: +49 3831 45 7306.