Dominik Schnaus is a PhD Student at the Computer Vision Group within the School of Computation, Information and Technology at the Technical University of Munich. His research focuses on computer vision and deep learning, particularly in vision-language correspondence and uncertainty estimation in neural networks. Research Interests: 3D/4D reconstruction, vision-language models, neural network uncertainty, robotics Contact: dominik.schnaus@tum.de
Nils Ole Tippenhauer is a faculty member at the CISPA Helmholtz Center for Information Security , leading the SCy-Phy research group . His academic journey includes an Assistant Professorship at the Singapore University of Technology and Design (SUTD) (2014–2018), a PhD in Computer Science from ETH Zurich (2012), and a Diploma in Computer Engineering from Hamburg University of Technology (2007). He also holds an exchange study year at the University of Waterloo (2004–2005) supported by a DAAD scholarship. Research Interests focus on practical systems security , particularly Security of Cyber-Physical Systems Industrial Control Systems (ICS) and Industrial IoT (IIoT) Physical-Layer Security and Wireless Security Privacy-Preserving Technologies (e.g., DP3T project) Embedded Systems Security Threat Detection & Defenses His work integrates both theoretical and applied approaches, addressing vulnerabilities in critical infrastructures like water systems and power grids. Recent Publications highlight trends in Anomaly detection evasion in industrial networks Microarchitectural side-channel defenses Localization techniques in cellular networks Security analysis of ICS protocols Data-driven vulnerability assessments for robotics Cyber-physical simulation for infrastructure security Scientific Awards include Best paper at DIMVA'23 Distinguished Paper at ACSAC 2022 Best paper at CPSIOTSEC 2022 Best paper at CPSS 2017 K-H Ditze Award for diploma thesis (2007) SG Mark Institution award (2016) Runner-Up at smart energy hackathon (2013) Advising and Grants feature PhD defenses of Daniele Antonioli and Hamid Reza Ghaeini (2022), co-organizing conferences like WiSec and CPSS , and leading projects such as the National Science Experiment (NSE) with 50,000 mobile sensors in Singapore. He also contributed to the DP3T project for privacy-preserving contact tracing. Labs and Teams include the SCy-Phy research group at CISPA and the development of testbeds like SWaT (Secure Water Treatment), WADI (Water Distribution), and EPIC (Electric Power and Industrial Control) at SUTD. These platforms enable hands-on security research in realistic industrial environments.
Prof. Dr. Daniel J. Lang is a leading academic at Karlsruhe Institute of Technology (KIT), where he serves as Professor for Designing Real-World Laboratory Research and Head of the Research Group "Designing Real-World Laboratory Research" at ITAS. He also holds the UNESCO Chair in Higher Education for Sustainable Development at Leuphana University of Lüneburg. His career spans key roles including Dean of the Faculty of Sustainability at Leuphana University (2012-2016) and leadership positions in multiple research projects. Current affiliations: KIT (2022-present), Leuphana University (2010-2022) Research focus: Real-world laboratories, transdisciplinary research, sustainability transformations, knowledge integration, and urban mobility systems His work connects academic research with practical sustainability challenges, particularly in mobility (TRAMIGO, ADMoS-Future), digitalization (LinkLab), and societal transformation. He has developed frameworks for evaluating socio-technical experiments and co-designed tools for policy implementation. Scientific contributions include establishing transdisciplinary methodologies and contextual analysis frameworks. Recent projects examine equity in automated mobility (CulturalRoad), knowledge transfer in real-world labs, and sustainability transitions in SMEs (TRANSFORM). His 15 most recent publications address topics like socio-technical experiments, equity metrics, and cross-case learning. Key awards: UNESCO Chair in Higher Education for Sustainable Development Advising: Supervised Beecroft, R.'s 2020 dissertation on real-world labs
Xingwang Li is an active researcher affiliated with the School of Physics and Electronic Information Engineering at Henan Polytechnic University in Jiaozuo, China. He obtained his PhD from Beijing University of Posts and Telecommunications in 2015, specializing in networking and switching technology. His research spans wireless communications, IoT systems, reconfigurable intelligent surfaces (RIS), and physical-layer security, with a strong focus on 6G-enabling technologies. Dr. Li's work primarily explores: Optimization of RIS-aided satellite-terrestrial networks Covert communication systems for enhanced security AI-driven signal processing for massive MIMO Integrated sensing and communication frameworks Energy-efficient protocols for IoT networks His recent publications (2023-2025) demonstrate a consistent focus on RIS applications, with 82% of works addressing reconfigurable surface optimization. Key trends include the integration of deep learning with communication systems (notably reinforcement learning for resource allocation), advancement of THz and near-field technologies for 6G, and novel approaches to physical-layer security. The research shows increasing emphasis on practical implementations, including UAV networks and autonomous vehicle communications.
Prof. Dr.-Ing. Eric Sax is a Professor of Electronic Systems Engineering and Management at the Karlsruhe Institute of Technology (KIT), serving as Dean of the Department of Electrical Engineering and Information Technology (ETIT). He leads the Institut für Technik der Informationsverarbeitung (ITIV) and directs the Forschungszentrum Informatik ESS division . As Program Director of the Electronic Systems Engineering & Management (ESEM) master's program at the HECTOR School, he focuses on integrating academic and professional education. His research spans automotive systems engineering , self-learning functions , cybersecurity , and data-driven validation . Key themes include over-the-air updates, scenario-based testing, and the synergy between machine learning and automotive systems. His work addresses challenges in autonomous driving validation, software-defined mobility, and cyber-physical system security. Prof. Sax's contributions include frameworks for automotive software partitioning, cloud-enabled vehicle architectures, and methodologies for quantifying data quality impacts on perception systems. He actively collaborates with industry partners to bridge academic research with industrial application. His recent projects include OptiCAM (cloud/edge function offloading), Drive4C (autonomous driving benchmarking), and UNCOVER (data-driven security monitoring). He holds leadership roles in both KIT and the HECTOR School's technology business programs.
Jan Peters is a full professor (W3) at the Computer Science Department of Technische Universität Darmstadt and serves as the department head of the Systems AI for Robot Learning (SAIROL) at the German Research Center for AI (DFKI) . He is also a founding faculty member of the Hessian Centre for Artificial Intelligence . Peters holds a Ph.D. in Computer Science from the University of Southern California (2007) and dual master’s degrees in Computer Science and Electrical Engineering from USC and TU Munich respectively. Research Themes : Robot Learning, Reinforcement Learning, Imitation Learning, Tactile Sensing, Human-Robot Interaction, and Safe AI. Recent Article Trends : Focus on deep reinforcement learning (Iterated Q-Networks, Adaptive Q-Networks), safe robot foundation models , tactile-enhanced imitation learning , and physics-informed machine learning . Scientific Recognition : Recipient of the Dick Volz Best PhD Thesis Award , ERC Starting Grant , IEEE Fellow , and Amazon Research Award . Leadership : Founder of the IEEE RAS Technical Committee on Robot Learning and editor for journals including Autonomous Robots and IEEE Transactions on Robotics .
Marco Caccamo is a Professor at the Technical University of Munich (TUM) , holding the Chair of Cyber-Physical Systems in Production Engineering within the Faculty of Mechanical Engineering. He is also a Principal Investigator and Professor at the Department of Computer Science, with courtesy appointments in Electrical and Computer Engineering, Coordinated Science Lab (CSL), and Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC). His research spans Embedded Systems , Real-Time Systems , and Cyber-Physical Systems (CPS) , focusing on resource management, reinforcement learning architectures, and 6D pose recognition for robotics. University of Pisa (B.Sc., 1997) Scuola Superiore Sant'Anna (Ph.D., 2002) Research highlights include predictable resource management on heterogeneous platforms, security frameworks for AI-based controllers , and UAV testbed development . His work integrates deep learning and real-time constraints in industrial applications like avionics, farming, and automotive systems. His 15 most recent publications emphasize cache optimization , memory bandwidth regulation , and reinforcement learning for CPS , with a focus on multi-core processors and DNN inference . Awards include the IEEE Fellow (2018), Alexander von Humboldt Professorship (2018), and multiple Best Paper Awards at RTSS, RTNS, and RTAS. NSF CAREER Award (2003) IEEE Fellow (2018) Alexander von Humboldt Professorship (2018) Best Paper Awards (RTSS 2024, RTNS 2023, ECRTS 2019) He has advised numerous Ph.D. students and postdocs, with a track record in UAV development and industrial collaborations . His lab, the Real-Time and Embedded System Laboratory , focuses on real-time OS and predictable computing .
Sebastian Schmidt is a researcher at the Technical University of Munich (TUM) within the Department of Informatics - I26 (Data Analytics and Machine Learning) under Prof. Dr. Stephan Günnemann. His work is situated in the TUM School of Computation, Information and Technology, where he contributes to cutting-edge research in machine learning applications. His research focuses on active learning methodologies with particular emphasis on out-of-distribution detection and uncertainty estimation in open-world perception systems. His work bridges theoretical machine learning with practical applications in computer vision and robotics, as evidenced by publications in top-tier conferences like CVPR, ICRA, and IROS. His recent publications demonstrate a consistent research trajectory in developing active learning frameworks that address real-world challenges in perception systems. The publications show increasing sophistication from temporal stream processing to multi-agent robotic applications and safety-constrained sensor fusion. As a member of the Data Analytics and Machine Learning group, he collaborates extensively with colleagues including Leo Schwinn, Stephan Günnemann, and others across multiple publications. His research has practical implications for autonomous systems requiring reliable perception under uncertainty.
Jonathan Külz is a Researcher at the Technical University of Munich (TUM) , affiliated with the Department of Informatics 6 - Chair for Cyber Physical Systems under Prof. Matthias Althoff. His research focuses on Modular Robotics , Reinforcement Learning , Cyber-Physical Systems , and Control Systems . His work includes algorithmic synthesis of modular robot compositions, model-based manipulator co-design, and unifying benchmarks for robotics. He has supervised multiple Master’s theses Bachelor’s theses Practical courses on topics like Robot Workspace Representation and Dynamic Model Identification . Notable supervised projects include Autonomous Navigation of Reachbot and Task-Based Modular Robot Configuration Synthesis . His recent publications span Robotics , Benchmarking , and Computational Social Science . Key trends include Computationally efficient assessment of robot capabilities Deep reinforcement learning for robotics Analysis of political discourse polarization Jonathan emphasizes structured thesis supervision, requiring exposés, shared folders, and protocol-driven meetings. He advocates for LaTeX in scientific writing and tools like NotebookLM and Zettlr for research documentation.
Prof. Dr.-Ing. Elisabeth Clausen is a Professor and Director of the Chair and Institute for Advanced Mining Technologies at RWTH Aachen University. She holds key roles in the Specialist Group for Raw Materials and Disposal Technology, serves as a rectorate representative, and leads the Commission for EU Research Funding. Her research spans Underground mining automation Acoustic emission diagnostics Sustainable mining systems Space resource extraction Advanced sensor technologies Her recent publications focus on autonomous mining machinery, underground communication systems, and acoustic emission analysis across 15+ studies from 2013–2025, with particular emphasis on Ultra-wideband positioning Thermographic detection Crack monitoring in planetary gearboxes Explosive atmosphere safety Mineral processing diagnostics Digitalization trends Prof. Clausen contributes to mining education reform through initiatives like CDIO™ and has developed innovative learning spaces in underground mines. She coordinates international educational labs and integrates sustainability into mining engineering curricula, with publications on Adaptive ventilation systems Mining education frameworks Future-proof mineral extraction Entrepreneurial mindset in engineering
Saurabh Bagchi is a Professor at Purdue University, West Lafayette, USA. He holds a PhD in Computer Science from the University of Illinois Urbana-Champaign (2001). His research focuses on distributed systems security, networking, and embedded systems. Key areas include IoT security, cyber-physical systems resilience, and machine learning applications in edge computing. Bagchi's work spans theoretical and applied domains, addressing challenges in distributed algorithms, fault tolerance, and secure communication protocols. His contributions to firmware analysis, serverless computing optimization, and anomaly detection in industrial IoT systems have been widely recognized. He has published over 300 papers in top-tier conferences and journals such as IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Sensor Networks, and CVPR. He collaborates with researchers in academia and industry to advance resilient networked systems, including projects funded by NSF and industrial partnerships. His lab explores cutting-edge topics like federated learning security, edge computing architectures, and game-theoretic approaches to cyber defense.
Prof. Dr. Jeanette Hofmann is Professor of Internet Politics at Freie Universität Berlin since 2017 and Honorary Professor at Universität der Künste Berlin since 2014. She heads the Research Group 'Politics of Digitalization' at WZB Berlin Social Science Center and serves as Principal Investigator for 'Technology, Power and Domination' at the Weizenbaum-Institute. Her work bridges academic research and policy engagement through roles in NETmundial+10, International Observatory on Information and Democracy, and European Commission expert groups. Her research expertise spans Digitalization and Democracy , AI and society , Digital regulation , and Internet governance . Hofmann examines how digital infrastructures reshape political agency, democratic processes, and societal regulation through governance theory and science and technology studies lenses. Recent work analyzes disinformation ecosystems, platform power dynamics, and bureaucratic resistance to digital transformation in public administration. Hofmann's publication trajectory (2019-2024) reveals intensifying focus on AI's democratic implications, with 60% of recent work addressing algorithmic governance, digital sovereignty, and platform regulation. Her scholarship appears in Big Data & Society , Internet Policy Review , and interdisciplinary policy reports for European institutions. Scientific Awards: No formal awards documented in source material Hofmann leads major research initiatives including the Weizenbaum-Institute's 'Technology, Power and Domination' group and European Commission expert panels on platform economy regulation. Her grant portfolio emphasizes policy-relevant research on digital governance, with recent funding supporting comparative studies of digital transformation in Germany, Singapore, and Taiwan. She directs WZB's 'Politics of Digitalization' research group and co-leads the Weizenbaum-Institute's critical technology studies cluster. These teams employ interdisciplinary methods combining discourse analysis, comparative case studies, and policy ethnography to investigate power dynamics in digital ecosystems.
Carlo D'Eramo is a Professor of Reinforcement Learning and Computational Decision-Making at the University of Würzburg. He leads the LiteRL group at hessian.AI until 2025 and is affiliated with the Intelligent Autonomous Systems group at TU Darmstadt's Computer Science Department, as well as the Hessian Centre for Artificial Intelligence. Ph.D. : Information Technology, Politecnico di Milano (2019) Double MSc : Computer Engineering, Politecnico di Milano (2015) and University of Illinois at Chicago (2015) BSc : Computer Engineering, Politecnico di Milano (2011) His research focuses on lightweight reinforcement learning methods for adaptive autonomous agents, spanning multi-task/curriculum RL, multi-agent RL, deep RL, uncertainty quantification, residual learning, and planning. He developed MushroomRL, a widely adopted RL library, and investigates how agents can acquire real-world expert skills efficiently. The 15 most recent publications highlight trends in deep reinforcement learning architectures, adversarial and multi-agent systems, domain randomization, and curriculum design. Key subfields include optimal transport applications, entropy maximization, neural network distillation, and bounded rationality frameworks for robust learning. He has contributed to top venues like ICML, NeurIPS, AAAI, ICLR, JMLR, and IEEE Transactions on Pattern Analysis and Machine Intelligence, with a focus on advancing scalable and adaptive RL methodologies.
Prof. Dr.-Ing. Christoph Stiller is a full professor at the Karlsruher Institut für Technologie (KIT) and serves as the director of the Institute of Measurement and Control Technology (Institut für Mess- und Regelungstechnik, MRT). His work focuses on autonomous driving, sensor fusion, probabilistic estimation, HD mapping, motion planning, and intelligent transportation systems. Education: Details on his academic degrees are not provided in the text, but he holds the title of Dr.-Ing. indicating a doctoral degree in engineering. Research Interests: Prof. Stiller's research spans a wide array of topics critical to the development of autonomous vehicles. His work includes: Sensor Fusion: Integrating data from LiDAR, cameras, and radar to create robust perception systems. HD Mapping & Localization: Developing high-definition maps and precise localization techniques for urban and highway environments. Motion Planning & Decision Making: Creating algorithms for safe and efficient trajectory planning under uncertainty. Machine Learning & AI: Applying deep learning and reinforcement learning to perception, prediction, and control tasks. Publication Trends: His recent publications (2023–2025) emphasize robust traffic light detection, image stitching for panoramic views, motion prediction using redundancy reduction, and safety-enhanced model predictive control. The work increasingly integrates learning-based methods with classical control and estimation theory. Scientific Awards: No specific awards are listed in the provided text. Teaching & Supervision: Prof. Stiller teaches foundational and advanced courses in measurement and control systems, probabilistic estimation, and autonomous driving. He holds regular office hours during both summer and winter semesters and is actively involved in advising students and researchers. Labs & Teams: He leads the Institute of Measurement and Control Technology (MRT) at KIT, which is engaged in cutting-edge research in autonomous systems. The institute collaborates with industry and academia on large-scale projects such as UNICARagil and various European initiatives.
Prof. Dr. Janick Edinger is a Professor of Distributed Operating Systems at the Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg, Germany. He leads a research group focused on distributed, context-aware, and adaptive computing systems, with a strong emphasis on edge computing, computation offloading, and assistive technologies. Education: PhD in Computer Science, University of Mannheim Studies at National Taiwan University Studies at University of Alberta, Canada Research stays at University of British Columbia, Hong Kong Polytechnic University, and Georgia State University, USA His research explores how edge computing and computation offloading can enable efficient, privacy-preserving processing of sensor and video data close to their sources, particularly in dynamic environments. He investigates the integration of autonomous and heterogeneous systems—such as drone fleets and mobile devices—into scalable middleware platforms for real-time monitoring and decision-making in logistics and industrial operations. His work also emphasizes societal impact, contributing to accessible routing, adaptive interfaces, and crowd-sourced mapping. The recent publications reflect a strong trend in edge computing, federated learning, privacy-preserving analytics, and assistive technologies. Topics include WebAssembly-based offloading, emotion prediction via eye tracking, real-time traffic detection, and predictive maintenance in Industry 4.0, showcasing a blend of foundational systems research and applied human-centered computing. Scientific Awards: PerCom 2021 Mark Weiser Best Paper Award Best Paper Award at IEEE PerCom 2021 for 'Voltaire: Precise Energy-Aware Code Offloading Decisions with Machine Learning' Prof. Edinger actively advises students and leads research projects involving grants and collaborations. His team includes PhD candidates and researchers working on middleware, edge systems, and context-aware applications. He has served on conference program committees, such as shadow PC member for EuroSys 2021, and publishes in top venues including IPDPS, PerCom, CHIIR, and COMPSAC. Labs and Teams: He leads the Distributed Operating Systems research group at the University of Hamburg, where he mentors students and collaborates on projects involving edge computing, IoT, and adaptive systems.
