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Associate Professor Dr. Marcus Stoffel is a faculty member at RWTH Aachen University, serving within the Faculty of Mechanical Engineering as head of the Chair and Institute of General Mechanics. His research integrates advanced computational mechanics with cutting-edge machine-learning techniques, focusing on viscoplastic material modeling, neural network surrogates, biomechanical systems, and neuromorphic computing. Education: While explicit degrees are not listed in the text, his title of Dr. and rank of Associate Professor at a leading German technical university imply doctoral and post-doctoral qualifications in mechanical engineering or a closely related discipline. Research Interests: Computational viscoplasticity and damage mechanics Physics-informed neural networks and spiking neural architectures Machine-learning-enhanced finite element methods Crashworthiness and structural optimization Biomechanics of musculoskeletal tissues and medical devices Sustainable neuromorphic computing for engineering applications Publication Trends: Across 2023–2025, Stoffel’s articles reveal a rapid pivot toward AI-accelerated engineering, coupling neuromorphic processors with traditional solid-mechanics problems such as viscoplastic beam response and membrane instabilities. Recurrent themes include surrogate modeling via spiking neural networks, generative learning for design optimization, and data-driven biomechanical analyses of spinal instrumentation and cartilage. Scientific Awards: No specific awards are mentioned in the provided text. Funding & Teams: While individual grants are not detailed, the extensive publication output and involvement in interdisciplinary projects (e.g., neuromorphic computing, biomechanics, crashworthiness) indicate robust external funding. He leads the Institute of General Mechanics, supervising a research group that bridges theoretical mechanics, numerical methods, and experimental biomechanics. Laboratories & Facilities: Stoffel’s team operates within the Institute of General Mechanics at RWTH Aachen, equipped with advanced finite-element computing resources, experimental biomechanical rigs (e.g., spine test rigs and bioreactors), and neuromorphic hardware platforms for sustainable AI research.

• Computational Mechanics
• Viscoplasticity
• Neural Networks in Engineering
• +6 more

Thomas Brox is a Professor for Pattern Recognition and Image Processing at the University of Freiburg , where he has headed the Computer Vision Group since 2010. He holds a PhD in computer science from Saarland University (2005) and has held postdoctoral positions at UC Berkeley, TU Dresden, and University of Bonn. Research Interests Education Publications & Awards Teaching & Leadership Roles His research focuses on computer vision and deep learning , particularly visual representation learning , video analysis , and training deep networks without manual supervision . He investigates how deep networks learn capabilities and applies these insights to action-perception cycles in robotics . Recent publications highlight trends in vision-language models , diffusion-based 3D generation , and anomaly detection . His work includes U-Net (seminal biomedical image segmentation) and FlowNet (optical flow estimation). Koenderink Prize (2014) ERC Starting Grant (2011) Longuet-Higgins Best Paper Award (2004) Multiple GCPR/ICCV/CVPR Best Paper Honors He teaches courses in Optimization , Statistical Pattern Recognition , and Computer Vision . He has served as Dean of Studies , Director of Computer Science Department , and will become Dean in 2026. His lab mentors 25+ PhD/MSc students and collaborates with institutions like Amazon (2020-2024).

• Computer Vision
• Deep Learning
• Visual Representation Learning
• +4 more

Dr. Frank Hannig is a Professor at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU), Germany. He serves as the Head of the Architecture and Compiler Design Group within the Hardware-Software-Co-Design department (Department 12). With a career spanning over two decades at FAU since 2003, he has established himself as a leading researcher in hardware-software co-design, compiler design, and embedded systems. Dr. Hannig received his Diploma degree in Electrical Engineering/Computer Science from the University of Paderborn in 2000, followed by his Dr.-Ing. degree in Computer Science from FAU in 2009 with a thesis on "Scheduling Techniques for High-Throughput Loop Accelerators." He completed his habilitation (Dr.-Ing. habil.) in 2018 with a thesis titled "Domain-specific and Resource-aware Computing," which qualifies him for a full professorship in the German academic system. His research focuses on hardware-software co-design, compiler design for embedded systems, reconfigurable computing, parallel systems, and machine learning acceleration. Dr. Hannig has made significant contributions to domain-specific and resource-aware computing, with applications in image processing, automotive systems, and edge AI. His work bridges the gap between high-level programming models and efficient hardware implementations, particularly for resource-constrained environments. Dr. Hannig's recent publications reveal a strong trend toward efficient machine learning deployment on embedded devices and microcontrollers, with particular emphasis on memory optimization, hardware acceleration, and low-precision computing. His research spans multiple domains including computer architecture, machine learning, and embedded systems, with a focus on practical implementations for real-world applications. Dr. Hannig serves as an Associate Editor for IEEE Embedded Systems Letters and the Journal of Real-Time Image Processing. He has organized numerous prestigious conferences including SLOHA 2021, ARC 2021, and Euro-Par 2021, demonstrating his leadership in the academic community. As an educator, Dr. Hannig teaches courses on Domain-Specific and Resource-Aware Computing on Multicore Architectures, Parallel Systems, and Embedded Systems. He has supervised numerous students through lectures, exercises, and seminars covering electronic system level design and multi-core architectures. Dr. Hannig leads several significant research projects including InvasIC (DFG Transregional Collaborative Research Centre), ExaStencils (Advanced Stencil-Code Engineering), and HBS (DFG Research Training Group on Heterogeneous Image Systems). His work with the HIPAcc open-source project has contributed to domain-specific language and compiler development for image processing applications.

• Hardware-Software Co-Design
• Compiler Design
• Embedded Systems
• +5 more

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.

• Human-Machine Interaction
• Natural User Interfaces
• Usability Engineering
• +6 more

Scott Thiebes is a Postdoctoral Research Associate and team lead of the Digital Health work group at the Critical Information Infrastructures Research Group at the Karlsruhe Institute of Technology (KIT). He holds a Ph.D. in Information Systems from KIT (Summa cum laude, 2022) and previously completed his academic training at the University of Cologne. His research focuses on digital transformation in healthcare, with particular expertise in digital health, information systems, blockchain technology, and artificial intelligence applications in healthcare settings. Karlsruhe Institute of Technology (KIT) - Institute for Applied Computer Science and Formal Description Methods Chair of Information Infrastructures under Prof. Ali Sunyaev Team lead of Digital Health work group Technical University of Munich affiliation via Campus Heilbronn Thiebes specializes in the intersection of information systems and healthcare, with research interests spanning digital health infrastructure, trustworthy AI in medical contexts, blockchain applications for health data management, and gamification for medical image annotation. His work employs behavioral and design-oriented information systems methods alongside medical informatics and computer science approaches. He has developed significant expertise in genetic data sharing, privacy considerations in health informatics, and the application of distributed ledger technology in healthcare settings. His recent publications demonstrate a strong focus on explainable AI for medical applications, blockchain-based health data management systems, and gamification techniques to improve medical image annotation quality. The research shows a clear trajectory toward creating more transparent, trustworthy, and effective digital health solutions that address both technical and human factors in healthcare transformation. Nominated by KIT for the Helmholtz Dissertation Award 2022 Best Theory Paper on Health IS Track at ICIS 2020 Multiple Best Reviewer Awards (ICIS 2019, WISP 2019, WISP 2017) Doctoral Fellow of HICSS 2017 Multiple Best Paper Nominations (PACIS 2016, ECIS 2014) Dean's Award from University of Cologne (twice) Thiebes serves as a reviewer for numerous top-tier information systems conferences and journals including ICIS, ECIS, HICSS, and WISP. He holds editorial and track chair positions for Business & Information Systems Engineering (BISE) and various conference tracks. His research is supported by multiple projects including GaMeIT (gamification for medical image annotation), BloG3 (blockchain-based health data management), Digital Transformation in Healthcare, ReDiBlock (resource conservation through blockchain), and XAIOmics (explainable AI for omics data), funded by organizations including the Helmholtz Association, German Federal Ministry of Education and Research, and German Research Foundation.

• Digital Health
• Information Systems
• Blockchain Technology
• +6 more

Sascha Rank is a Ph.D. student and research associate at the Chair of Information Infrastructures, Technical University of Munich (TUM), Heilbronn, working under Prof. Ali Sunyaev. His research bridges machine learning, distributed systems, and healthcare applications with emphasis on ethical AI implementation. His academic background includes: Ph.D. candidate since February 2021 (Karlsruhe Institute of Technology) M.Sc. in Industrial Engineering and Management (2018-2020, Karlsruhe Institute of Technology) B.Sc. in Industrial Engineering and Management (2013-2018, Karlsruhe Institute of Technology) Rank's research centers on Machine Learning and Gamification , with deep expertise in federated learning architectures , collaborative distributed systems , and medical AI applications . His work addresses critical challenges in privacy-preserving computation, healthcare gamification frameworks, and ethical AI deployment, often combining theoretical rigor with practical healthcare use cases through projects like FLAIROP (Federated Learning for Robotic Picking). Publication analysis reveals a strong trajectory toward healthcare-integrated machine learning, evolving from foundational gamification studies (2021) to advanced distributed systems toolboxes (2023) and comprehensive survey work (2024). His research consistently targets real-world implementation barriers in medical AI and intralogistics while developing standardized frameworks for ethical development. No scientific awards were documented in available sources. Current information indicates no formal student advisement or principal investigator roles in research grants. Rank operates within TUM's Critical Information Infrastructures research group, which explores blockchain systems, autonomous driving safety, and collaborative machine learning through projects like FLAIROP. The group actively develops ethically aligned frameworks for decentralized applications and harm mitigation in autonomous systems, with strong industry connections evident through thesis topics and corporate collaborations.

• Machine Learning
• Gamification
• Federated Learning
• +3 more

Andy Witt serves as Professor of Business Informatics with a focus on Digitalization at Hamburg School of Business Administration (HSBA) since September 2023. His academic foundation includes a Computer Engineering degree from Hamburg University of Technology (TU Hamburg) specializing in applied mathematics and physics, followed by doctoral research at the same institution from 2013-2017. His educational background includes: Computer Engineering studies at TU Hamburg with concentration in applied mathematics and applied physics Doctoral research (2013-2017) applying nonlinear Schrödinger equations to model extreme ocean wave phenomena Professor Witt's research spans Nonlinear Dynamics, Cost Engineering, Sustainable Production, Machine Learning, and Big Data Analysis. His work demonstrates a clear evolution from theoretical physics toward industrial applications, particularly in digitalizing production systems and integrating sustainability metrics into cost engineering frameworks. Current research emphasizes AI-driven optimization for sustainable industry practices. Analysis of his publication history reveals a strategic shift from fundamental wave dynamics research (2019-2022) toward applied industrial informatics (2021-2023), with growing emphasis on carbon footprint quantification, fusion energy economics, and sustainable manufacturing systems. This trajectory reflects his dual expertise in physical modeling and business-oriented digital solutions. Professor Witt founded and serves as managing director of CALC4XL GmbH, which develops the namesake software platform for integrated product cost and carbon footprint calculation. This venture directly supports his academic mission by providing industry-tested tools for sustainable product optimization, bridging theoretical research with practical business applications in industrial digitalization.

• Nonlinear Dynamics
• Cost Engineering
• Sustainable Production
• +4 more

Roee Sarel is a Junior Professor of Private Law and Law & Economics at the Institute of Law & Economics, University of Hamburg. He holds a dual academic background with a doctorate in Economics from the Frankfurt School of Finance & Management and law/business degrees from the Hebrew University of Jerusalem. His position at the University of Hamburg's Faculty of Law began in September 2021, and he also serves as an External Lecturer at the Kühne Logistic University, Haifa University, and Northern Institute of Technology. His educational background includes: Doctorate in Economics (Dr. rer. pol; Summa Cum Laude), Frankfurt School of Finance & Management, 2018 M.B.A. degree (specialization in Finance & Banking; Magna Cum Laude), Hebrew University of Jerusalem, 2012 LL.B. degree (Magna Cum Laude), Hebrew University of Jerusalem, 2011 Erasmus Mundus Exchange student, VUB, Brussels, Belgium, 2008-2009 Professor Sarel's research spans multiple interdisciplinary areas at the intersection of law and economics. His primary focus is on Law & Economics using theoretical, experimental, empirical, and behavioral approaches. He specializes in the regulation of financial markets and technology , particularly Blockchain and Cryptocurrencies. His work also addresses private law, product markets, and consumer protection , examining how legal frameworks affect market efficiency and consumer welfare. In the criminal justice domain, he investigates crime deterrence and the determinants of pro-social and anti-social behavior . A significant portion of his research analyzes judicial decision-making processes and how they're influenced by economic incentives and behavioral factors. His recent publications demonstrate a strong focus on emerging technologies and their legal implications, particularly in the areas of blockchain, cryptocurrencies, and AI in judicial systems. There's also a clear trend addressing how crises (particularly the COVID-19 pandemic) affect legal and economic systems. His work consistently applies economic methodologies to legal questions, bridging theoretical frameworks with empirical analysis across multiple legal domains including criminal law, civil law, and financial regulation. His notable awards and grants include: Principal Investigator on "Climate Change as Unjust Enrichment" (2022), funded by the German-Israeli Foundation for Scientific Research and Development Best paper award at SIDE-ISLE conference (2020) Best paper presented by a young scholar award at GLEA conference (2019) Professor Sarel has secured competitive research funding, most notably as Principal Investigator for the German-Israeli Foundation project on Climate Change as Unjust Enrichment. His teaching responsibilities include the "Introduction to Law & Microeconomics" course at the law school, as well as courses on "Empirical Legal Studies," "Concepts & Methods," and "Private Law & Economics" in the European Master of Law & Economics (EMLE) and European Doctorate of Law & Economics (EDLE) programs. His academic journey includes previous positions as a Post-doctoral research associate at the University of Hamburg's Institute of Law & Economics (2019-2021), Lecturer & Lab Manager at Frankfurt School of Finance & Management (2017-2019), and professional experience as an Associate Lawyer in the litigation & banking department at Yigal Arnon & Co. in Jerusalem, Israel (2011-2013).

• Law & Economics
• Regulation of financial markets and technology
• Blockchain and Cryptocurrencies
• +8 more

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.

• Formal Methods
• Automated Reasoning
• Verification
• +7 more

Dr. Mahsa Varshosaz is an Associate Professor in the Software Quality Research group at the IT University of Copenhagen , Denmark. Her work bridges theoretical and practical aspects of software quality assurance, with a focus on model-based testing , formal verification , and automatic program repair for complex systems. Her research spans software product lines , autonomous systems , and cyber-physical systems , including projects like REMARO (testing of underwater robotic systems) and Linux kernel program repair. She employs formal methods to address challenges in system safety, reliability, and variability. Selected publications reveal a trajectory in hybrid testing techniques combining symbolic execution and reinforcement learning, safety analysis of autonomous underwater vehicles, and formal verification of probabilistic systems. Her work intersects software testing formal methods AI-based verification robotics safety product line engineering concurrent system analysis . She actively contributes to academia as Co-Chair of Doctoral Symposium in SPLC conferences Editorial Board member of Science of Computer Programming Program Committee member across testing/verification workshops like A-MOST, ITEQS, and ECOOP . Her 2023 invited talk series at Trustworthy Autonomous Systems Verifiability Node and participation in Dagstuhl/Shonan seminars highlight her influence in unifying formal methods with AI-based autonomous systems . Projects include REMARO (co-coordinator) and INSIGHT.

• Software Testing
• Formal Verification
• Software Safety Analysis
• +6 more

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.

• Secure programming
• Programming Languages
• Static Analysis
• +5 more

Maren Bennewitz is a Professor at the University of Bonn specializing in humanoid robots. She serves as Vice Rector for Digitalization and leads research at the Lamarr Institute for Machine Learning and Artificial Intelligence . As a principal investigator in national and European projects, she contributes to the executive board of the Cluster of Excellence PhenoRob and co-founded the Center for Robotics at Bonn. Key affiliations: University of Bonn, Lamarr Institute, PhenoRob Cluster, Center for Robotics Her research focuses on robot navigation , active perception , intelligent manipulation , and personalized human-robot interaction . Recent work explores privacy-preserving navigation, agricultural robotics, and neuromorphic obstacle avoidance. Publications highlight applications in crop monitoring, multi-agent safety, and VR-based explainability systems. Notable trends in her research include agricultural robotics (AID4Crops, PhenoRob), multi-objective reinforcement learning , and human-swarm interaction . The Lab at University of Bonn develops solutions for dynamic environments, balancing technical innovation with ethical considerations in robotics.

• Robot Navigation
• Active Perception
• Intelligent Manipulation
• +4 more

Tobias Lehrer is a Doctoral Candidate at the Chair of Computational Solid Mechanics at the Technical University of Munich since 2023. His research focuses on integrating machine learning with computational mechanics for manufacturing optimization. 2023 - Present: Doctoral Candidate, Chair of Computational Solid Mechanics, TU Munich 2020 - 2023: Doctoral Candidate, Laboratory of Finite-Element-Method, OTH Regensburg 2018 - 2020: M.Sc. in Mechanical Engineering, OTH Regensburg His research interests span machine learning, surrogate modeling, data augmentation, synthetic data, generative AI, sheet metal forming, and sensitivity analysis. His publications emphasize deep drawing processes , parametric CAD modeling , and surrogate-based optimization for manufacturability assessment. Key projects include AMEDEO and eEgO, aiming to enhance early-stage prediction of sheet metal part feasibility through simulation-driven machine learning frameworks. In teaching, Tobias has led courses on structural optimization , computational mechanics for car body design , and structural mechanics modeling . His work bridges computational methods with industrial applications, particularly in automotive manufacturing.

• Machine Learning
• Surrogate Modeling
• Data Augmentation
• +5 more

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.

• Digitalization of Work
• Human Factors Engineering
• Sociotechnical Systems
• +3 more

Kai Rewitz serves as Senior Engineer and Team Leader for User Behavior and Comfort at the Chair of Building and Room Climate Technology (EBC) within the E.ON Energy Research Center at RWTH Aachen University. He joined the institution in 2012 as a scientific assistant after completing his Mechanical Engineering diploma, was promoted to Oberingenieur in 2021 following his distinguished PhD completion, and has led the User Behavior and Comfort team since 2019. Education: Diploma in Mechanical Engineering, RWTH Aachen (2012) PhD (Dr.-Ing.) in Thermal Comfort Modeling, RWTH Aachen (2020) His research centers on energy-efficient climate control strategies for buildings and vehicle cabins (especially electric vehicles), thermal comfort modeling through human subject studies, cloud-connected sensor systems for indoor climate monitoring, user-centered building optimization via feedback/feedforward interfaces, and indoor air quality analysis regarding human health and olfactory perception. This interdisciplinary work bridges mechanical engineering, environmental science, and human factors to create sustainable, health-focused built environments. Analysis of his 15 most recent publications (2024-2025) reveals dominant trends in personalized environmental control systems (PECS), vehicle cabin climate dynamics, and health-performance impacts of indoor environments. Key cross-cutting themes include thermal comfort optimization, indoor air quality enhancement, energy-efficient ventilation strategies, and AI-driven sensor technologies for real-time environmental monitoring. Scientific recognition: Borchers Plakette, RWTH Aachen University (2020) Dr. Rewitz supervises the course "Grundoperationen der Energietechnik" (Basic Operations of Energy Technology) and leads a 10+ member research team. His work is funded by major projects including DataFEE (user-centered energy-efficient building operation through advanced analytics) and EnModuS (optimization of modular shopping centers using energy data), with strong industry collaboration for practical implementation. He directs development of innovative measurement systems like semi-wireless skin temperature monitors and infrared occupant detection solutions, while contributing to national guidelines such as the "Klassenräume SMART sanieren" framework for school renovations and pandemic-era ventilation standards for infection control in public spaces.

• Building Energy Efficiency
• Indoor Environmental Quality
• Thermal Comfort
• +4 more