Andreas Herten is a Researcher at the Jülich Supercomputing Centre (JSC) within Research Center Jülich GmbH. He serves as Co-Lead of the Novel System Architecture Design division and heads the ATML Accelerating Devices group, focusing on GPU programming, parallel computing, and high-performance computing (HPC) optimizations. Key Expertise: GPU programming, parallel algorithms, HPC systems, benchmarking, Python, LaTeX Research Focus: Accelerating scientific applications on GPUs, European exascale initiatives, AI workload evaluation, and heterogeneous computing architectures His recent publications highlight advancements in GPU-accelerated materials science, AI training on HPC systems, and exascale benchmarking. Herten contributes to projects like OpenGPT-X, JUPITER, and CARAML, with work spanning computational physics, environmental science, and machine learning applications.
Stefan Geisler is a Professor at Ruhr West University of Applied Sciences since 2010. He holds the professorship for Applied Computer Science / Human-Machine Interaction and serves as Program Director for the Human-Technology Interaction program. Since 2016, he has led the Positive Computing Research Institute , contributes to the Intelligent Mobility research program , and directs the Media and Interaction Department in the North Rhine-Westphalian doctoral program. Research Focus: Human-Machine Interaction (HMI), Natural User Interfaces (NUI), Usability Engineering, Automotive HMI, and Intelligent Mobility. Projects: Golden Rules in Automotive HMI, PARCURA (smart glasses in hospitals), UsAHome (home HMI systems), BMBF-funded AHA project (safety-critical interfaces), and intercultural/intergenerational innovation development. Collaborations: FH Struktur, IQPC Conference, CAMO network, and North Rhine-Westphalian doctoral program. Teaching: Covers the entire usability engineering lifecycle, emphasizing user testing integration and intuitive interface design. Publication Trends: Focus on automotive HMI optimization, gesture-based interaction, user experience in mobility systems, and human-centered design for safety-critical applications. Subfields include driver assistance systems, natural user interfaces in automated vehicles, and usability testing methodologies. Leadership: Directs the Positive Computing Research Institute and the Media and Interaction Department. Supervises doctoral research in intercultural innovation and automotive NUI development.
Prof. Dr. Stephan Krusche is a faculty member at the Technical University of Munich (TUM) with the Professorship of Applied Education Technologies in the TUM School of Computation, Information and Technology . He conducts research at the intersection of educational technologies, software engineering, human-computer interaction, and artificial intelligence. Born: 1986 Email: krusche@tum.de Research Interests Education Technologies: Artemis platform, Iris chatbot Software Engineering: Agile development, DevOps, Continuous Delivery Human-Computer Interaction: User experience, Interactive systems AI Applications: Automated feedback, Learning analytics Recent Publications focus on AI-driven educational tools, interactive learning methodologies, and software engineering pedagogy. He organizes international summer schools and serves on the board of directors of the European Society for Engineering Education (SEFI). Scientific Honors Angela Molitoris Diversity Award (2024) Best practitioner report award (2023) Ernst Otto Fischer Teaching Award (2021) Ars Legendi Prize (2020) Pandemic teaching award (2020) Advising covers 60+ theses on topics including Artemis platform development, collaborative systems, and mobile education technologies. He actively promotes diversity through initiatives like ExploreTUM , she.codes , and HackerSchool .
Prof. Dr. Stefan Wagner is a Full Professor of Software Engineering at the Technical University of Munich (TUM), where he leads the Chair of Software Engineering within the TUM School of Computation, Information and Technology. Based at the TUM Campus Heilbronn, Prof. Wagner joined TUM in 2024 after serving as Professor of Empirical Software Engineering at the University of Stuttgart since 2011. His research has significant practical relevance, often conducted in collaboration with industry partners, particularly in automotive and AI-based software domains. Prof. Wagner's educational background spans multiple disciplines: Computer Science studies at Augsburg and Edinburgh Psychology studies at Hagen Doctorate in Computer Science from TUM (2007) His primary research interests focus on software engineering with particular emphasis on software quality, human factors in development processes, AI-supported engineering methods, and empirical studies. Prof. Wagner's work bridges theoretical foundations with practical applications, especially in automotive software systems and AI-based domains. His interdisciplinary approach, combining computer science with psychology, enables unique insights into developer behavior and software quality assessment. The research conducted at his chair addresses critical challenges in modern software development, including quality assurance in complex systems and the integration of artificial intelligence into engineering processes. Prof. Wagner's extensive publication record demonstrates evolving research trends from traditional software quality models toward increasingly sophisticated integration of AI and human factors in software development. His recent work shows a growing emphasis on empirical studies of developer behavior, AI-assisted programming, virtual reality applications in software engineering, and the psychological aspects of technical debt. The publications reveal a consistent focus on practical applicability while maintaining scientific rigor, with strong industry collaboration evident throughout his career. Prof. Wagner has received numerous prestigious awards recognizing his contributions to software engineering: Class of IEEE Computer Society Distinguished Contributors (2022) Best Full Paper Award, ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (2020) IEEE Computer Society TCSE Distinguished Paper Award, IEEE International Conference on Software Maintenance and Evolution (2019) Most Influential Paper Award, IEEE International Conference on Software Maintenance and Evolution (2017) Google Research Award (2009) Prof. Wagner actively mentors students and researchers, with several PhD candidates and master's students working under his supervision. His research is supported by various grants, including industry collaborations and competitive research funding. He serves on multiple editorial boards including IEEE Software and Empirical Software Engineering, contributing significantly to the academic community. At TUM, he chairs the aptitude assessment committee for the M.Sc. Information Engineering program and serves on the scientific board for the TUM Global Postdoc Fellowship. The Chair of Software Engineering at TUM Heilbronn maintains strong connections with industry partners, particularly in the automotive sector. The research group actively participates in multiple collaborative projects focusing on AI in software development, software quality assessment, and empirical studies of development practices. The team combines expertise in software engineering, psychology, and artificial intelligence to address complex challenges in modern software systems.
Sivajeet Chand is a Researcher at the Technical University of Munich , affiliated with the Chair of Software & Systems Engineering . He began his PhD in July 2024 under the supervision of Prof. Dr. Alexander Pretschner . His research focuses on Generative AI and Large Language Models (LLMs) for code migration and modernization. He holds a master's degree in Software Engineering and Technology from Chalmers University of Technology, Sweden , and has prior industry experience as a Data Engineer at Volvo Group and as a student engineer at Aptiv and Good Solutions. His research interests span Generative AI , LLMs , Software Engineering , and Code Migration . Recent publications highlight his work on design pattern recognition using LLMs (2023), automating requirements review in the automotive sector (2024), and empirical evaluations of LLMs in code migration (2025). His projects often intersect with automotive industry applications , code refactoring , and AI-assisted software development .
Benedikt Feldotto is a Researcher at the Technical University of Munich, affiliated with the Department of Computer Science, Chair of Robotics, Artificial Intelligence and Real-Time Systems led by Prof. Knoll. He works on the Neurorobotics Platform as part of the European Human Brain Project and serves as lead developer of the NRP Robot Designer since 2017. His educational background includes a Bachelor of Engineering in Mechatronics from Baden-Wuerttemberg Cooperative State University (DHBW) with industry experience in automation pre-development, including a development stay in the USA. He further specialized with a Master of Science in "Robotics, Cognition, Intelligence" at Technical University of Munich. Feldotto's research focuses on biomimetic learning in neurorobotic systems, with particular emphasis on spiking neural networks for embodied cognition. His work bridges cognitive neuroscience and robotics, exploring how robots can interact naturally with humans and environments through biologically-inspired learning mechanisms. Key areas include musculoskeletal modeling, human-robot interaction, and the ethical implications of learning robots in society. He actively develops simulation tools to enable large-scale neurorobotic experiments and has contributed significantly to the Human Brain Project's Neurorobotics Platform infrastructure. His publication record shows a clear progression from foundational platform development toward increasingly sophisticated embodied simulations using high-performance computing. Recent work emphasizes scaling spiking neural networks for robotic control, analyzing neural network architectures for specific motor tasks, and validating biomechanical models against human movement data. The research consistently connects theoretical neuroscience with practical robotics applications. His scientific recognition includes: Best Poster Award from the Graduate School of Bioengineering (2018) As an educator, Feldotto has taught Cognitive Systems course exercises from Spring Semester 2018 through Spring Semester 2022. He regularly offers thesis and project opportunities in biomimetic robotics and neural network learning, though no current openings are listed. His teaching and research are supported through the European Human Brain Project funding framework. Feldotto leads development of the NRP Robot Designer, a critical tool within the Neurorobotics Platform ecosystem. He has presented this work internationally through workshops at major conferences including the European Robotics Forum and Japanese Neural Network Society meetings. His outreach extends to public exhibitions at the Deutsches Museum and participation in ethics discussions on robot stereotypes, demonstrating commitment to both technical advancement and societal implications of neurorobotics research.
Dr. Michael Johannes Barz is an Associated Member at the Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) in Saarbrücken, Germany, where he conducts research within the Ubiquitous Media Technology Lab (UMTL). His work bridges human-computer interaction with artificial intelligence, focusing on gaze-based interaction, eye tracking, and interactive machine learning systems. Dr. Barz has established himself as a significant contributor to the field with over 50 publications spanning from 2015 to 2024. His research interests center on gaze-based interaction , mobile eye tracking , user modeling , and interactive machine learning , with recent work expanding into cognitive load measurement using digital pen technology and mixed reality applications for industrial training. Dr. Barz has developed influential tools like IMETA for eye tracking annotation and pEncode for visualizing pen signals, demonstrating his commitment to creating practical research methodologies. Dr. Barz's publication record shows consistent contributions to top-tier conferences including ACM ETRA, IEEE VR, and the International Conference on Intelligent User Interfaces. His 2022-2024 work reveals increasing focus on making AI systems more transparent and interactive, with publications on explaining machine learning model explanations and interactive deep learning frameworks. His research often involves interdisciplinary collaboration across computer science, cognitive science, and educational technology. Special recognition for an outstanding review at ACM ETRA 2020 Active conference reviewer for IJCAI, ACM IUI, KI, ACM ETRA, and IEEE VR Journal reviewer for Journal of Eye Movement Research Dr. Barz has contributed to teaching as an assistant for 'Intelligent User Interfaces' at TU Kaiserslautern and 'Artificial Intelligence' at Saarland University. His current research suggests strong engagement with both theoretical advancements in interactive machine learning and practical applications in educational and industrial contexts, particularly through the MASTER-XR project for mixed reality manufacturing training. The Ubiquitous Media Technology Lab provides the collaborative environment where Dr. Barz develops his innovative approaches to human-AI interaction.
Thomas Vogel is a postdoctoral researcher at the Software Engineering Group within the Institute of Computer Science at Humboldt-Universität zu Berlin . From October 2021 to September 2022, he served as a stand-in professor for Empirical Software Engineering at Paderborn University. He earned his Ph.D. summa cum laude in 2018 from the University of Potsdam under the Hasso Plattner Institute, specializing in model-driven engineering of self-adaptive systems. He graduated with distinction in Information Systems from the University of Bamberg .
Reinhard Schütte is a Professor holding the Chair for Business Informatics and Integrated Information Systems at the University of Duisburg-Essen since October 2015. His academic career spans multiple prestigious institutions including Zeppelin University Friedrichshafen, the University of Essen (before its merger), University of Koblenz-Landau, and Westfälische Wilhelms-Universität Münster. He has developed a distinguished career bridging theoretical academic work with practical business applications, particularly in retail and enterprise information systems. Professor Schütte's research interests focus on Enterprise Systems, IS architectures, Digitization of institutions, Information modeling, and scientific-theoretical problems of business informatics. His work demonstrates a consistent pattern of exploring the intersection between business administration and information technology, with particular emphasis on practical applications in retail environments. His approach combines theoretical insights with practical experience, emphasizing interdisciplinary thinking in business informatics. His recent publications reveal a strong trend toward examining digital transformation in various sectors including retail, construction, and healthcare. A significant portion of his work investigates how AI and advanced technologies can solve industry-specific challenges, from retail pricing algorithms to enterprise resource planning systems in the cloud era. His research often employs innovative methodologies including neuroimaging, multimethod approaches, and real-world case studies. Best Paper Award for Scene Responsiveness for Visuotactile Illusions in Mixed Reality Best Paper Award for SoundsRide: Affordance-Synchronized Music Mixing for In-Car Audio Augmented Reality Professor Schütte's work has significant practical implications for businesses navigating digital transformation. His research on retail information systems, ERP evolution, and digital marketplaces provides valuable insights for organizations seeking to optimize their technology investments. He has contributed extensively to understanding the business value of IT systems and the paradoxes that arise in their implementation. His teaching covers Enterprise Systems, Enterprise Transformation, Impact and cost-effectiveness of IT systems, Retail Enterprise Systems, and Management of Large Enterprise Systems.
Clark Barrett is a Professor in the Department of Computer Science at Stanford University, where he conducts research in formal methods, automated reasoning, and verification. He is affiliated with several research centers including the Stanford Center for Automated Reasoning, Stanford Center for AI Safety, Stanford Agile Hardware Project, and Stanford Center for Blockchain Research. His research focuses on developing formal methods and tools for verifying complex systems, with particular emphasis on satisfiability modulo theories (SMT), verification of neural networks, hardware design verification, and security. His work bridges theoretical foundations with practical applications across multiple domains. Over the past decade, Barrett's research has evolved from foundational work in SMT solving to increasingly diverse applications including neural network verification, hardware verification, and AI safety. His recent publications demonstrate a strong focus on practical verification techniques for real-world systems, particularly in the areas of hardware design, neural networks, and programming languages. The trend shows an expansion from core verification techniques to broader applications in AI safety and secure systems design. 2021 CAV (Computer Aided Verification) Award Barrett leads several major research initiatives and collaborates extensively with industry partners. His work on the Marabou neural network verification framework, SMT-LIB standard, and Symbolic QED verification methodology have had significant impact in both academic and industrial settings. He has supervised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. He is a key member of the Stanford Center for Automated Reasoning, which develops foundational technologies for automated reasoning, and the Stanford Center for AI Safety, where he focuses on formal methods for ensuring the safety and reliability of AI systems. His work on the Stanford Agile Hardware Project aims to revolutionize hardware design through formal methods and verification techniques.
Krishna Narasimhan is a Researcher at the Software Technology Group within Technical University of Darmstadt, Germany, focusing on developer struggles with cryptographic APIs and secure programming practices. He plays a key role in the CogniCrypt framework development, an Eclipse Foundation project designed to help developers use crypto APIs securely. His research spans secure programming, programming languages, static analysis, source code transformation, and domain-specific languages. His work demonstrates a clear progression from foundational research in program transformation during his PhD to practical applications in API security and developer tooling. Recent work shows growing interest in AI-assisted development and machine learning bug detection. His publication record reveals consistent contributions to major software engineering venues (ECOOP, SPLASH, ICSE), with recent papers examining trustworthy AI software development, misuse-resilient APIs, and code generation from test specifications. The research shows strong emphasis on practical tools that address real-world developer challenges rather than purely theoretical contributions. Narasimhan maintains active service in the research community as a committee member for artifact evaluation at numerous conferences including ECOOP, PLDI, ISSTA, and SPLASH, demonstrating recognition of his expertise in experimental methodology and reproducibility. His career path includes industry experience as a Language Engineer at Itemis developing Mbeddr (an embedded DSL platform), followed by return to academia where he now bridges practical tool development with academic research. His PhD work focused on semi-automatic tools for source code evolution tasks like copy-paste abstraction and data representation migration.
Prof. Dr. Dr. h.c. Peter Maaß is a leading academic at the Center for Industrial Mathematics (ZeTeM) at the University of Bremen, Germany. His work bridges Inverse Problems , Machine Learning , and Computational Engineering , with a focus on applications in life sciences and industrial systems . Research Interests: A pioneer in inverse problems and imaging, Maaß’s research spans Signal and image analysis in life sciences Deep learning for geometry generation Hybrid data-driven and model-based simulations Parameter identification in differential equations Recent Publications highlight trends in Deep Learning , Medical Imaging , and Scientific Computing , with subtopics including GAN-based inversion , Neural Network Regularization , and Multiscale Approximation . His work often integrates domain-specific knowledge with modern AI techniques. Scientific Honors: Dr. h.c. (honorary doctorate) Advising and Grants: He has supervised numerous PhD theses on topics like Regularization Theory , 3D Image Analysis , and Invertible Neural Networks . His projects include EU-ROMSOC , AGENS , and DIAMANT , funded by agencies such as DFG , BMBF , and EU . Labs and Collaborations: Leads the Working Group Industrial Mathematics and contributes to ZeTeM ’s interdisciplinary research, including Graduate School π³ and collaborations with institutions like University of Melbourne and Clemson University .
Caroline Adam is a research associate and doctoral candidate at the Chair of Ergonomics at Technical University of Munich since 2017. She holds a Master’s degree in Human Factors Engineering from TUM after completing her Bachelor’s in Scientific Principles of Sports. Education B.Sc. Scientific Principles of Sports, TUM M.Sc. Ergonomics – Human Factors Engineering, TUM Research Focus Her research examines work transformation in digitalization contexts, particularly work-related mobility and location-independent work environments. Current work emphasizes sociotechnical modeling, human factors in robotics, and automated driving ergonomics. Publication Trends Recent publications focus on pandemic-era workplace adaptation, digital manufacturing acceptance, and creativity training in engineering education. Her work spans interdisciplinary applications of AI-based modeling and FRAMalyse software tool development. Labs & Tools Active in TUM’s Dynamical Mock-Up , Modular Ergonomic Mockup , and Static Driving Simulator facilities, contributing to the Robots for Life and Healthcare research group.
Dr. Daniel Langenkämper is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering and the Center for Biotechnology (CeBiTec). He serves as a key member of the Biodata Mining Group, where he develops and applies advanced computational methods for marine biological data analysis. His office is located at UHG V10-107 with contact number +49 521 106-3678. Langenkämper's research focuses on the intersection of computer science and marine biology, with particular expertise in: Computer vision applications for marine ecosystem monitoring Deep learning approaches for diatom and coral classification Biodata mining from complex marine imagery Digital platform development for environmental monitoring systems Multi-sensor data analysis for marine infrastructure assessment His publication record shows consistent output through 2025, with recent work emphasizing expert-computer vision integration for coral status exploration and self-supervised learning techniques for diatom classification. The research demonstrates strong interdisciplinary collaboration across computer science, marine biology, and engineering disciplines, addressing critical challenges in marine environmental monitoring and infrastructure maintenance. His work contributes significantly to both theoretical advancements in image analysis and practical applications for marine conservation and industrial monitoring. Langenkämper actively participates in marine imaging workshops and contributes to the development of standardized image datasets for marine research. His work with the Biodata Mining Group at CeBiTec supports multiple research initiatives focused on transforming visual data into actionable ecological insights, particularly for deep-sea coral ecosystems and marine infrastructure maintenance.
Mareen Wienand is a researcher at the Chair of Business Informatics and Integrated Information Systems at the University of Duisburg-Essen’s Faculty of Computer Science. She transitioned to a Senior Consultant role at Metapott GmbH in July 2024 after serving as a research assistant and doctoral student since 2019. Her work focused on enterprise systems training, e-learning innovation, and generative AI applications in education. PhD in Business Informatics (2024) from University of Duisburg-Essen M.Sc. in Technical Business Administration with Business Analytics (2019) from University of Duisburg-Essen B.A. in Business Administration with Quantitative Methods (2016) from Münster University of Applied Sciences Research Interests: The implementation of enterprise systems (e.g., SAP S/4HANA) and their training methodologies, with a focus on multimedia eLearning design principles to reduce cognitive load and improve user experience. She investigated how microlearning frameworks can bridge academic and corporate ES competency development, and explored generative AI’s capabilities to address MOOC limitations like delayed feedback and linguistic barriers. Scientific Contributions: Her 2024 dissertation proposed a design science-based eLearning approach for ES training, while her 2024 conference paper introduced a microlearning framework for university lectures. A 2024 Pacific-Asia Conference paper analyzed generative AI’s potential to enhance MOOCs through personalized assistant functions and multilingual support. Advising & Projects: She supervised bachelor theses on topics like AR/VR in e-learning, gamification, and AI-driven change management tools. Her industry experience includes roles at Flender GmbH (Siemens) and Metapott GmbH, alongside academic coordination of the "We Learn in Bits" program at Ruhr Campus Academy.
