Prof. Dr. Peer Neubert is a Professor and Head of the Intelligent Autonomous Systems (INTAS) Working Group at the University of Koblenz. He is affiliated with the Department of Computer Visualistics (Department 4) and serves as Head of the Institute of Computational Visualistics. His academic roles include membership in the Department Council and Library Committee of FB4. His research focuses on robotics, computer vision, and autonomous systems, with particular emphasis on visual place recognition, SLAM, hyperdimensional computing, and adaptive navigation in dynamic environments. Neubert's work spans theoretical advancements and practical implementations, such as benchmarking systems (e.g., HS3-Bench), efficient algorithms (FETCH, LocalSPED), and sensor-based solutions (HealthWalk). His team addresses challenges in long-term navigation, environmental adaptation, and data-efficient learning. Collaborations include external PhD students like Kenny Schlegel and projects like MineSweeper, demonstrating interdisciplinary impact. His contributions are reflected in over 50 publications since 2007, covering topics from hyperspectral segmentation to biomedical applications. While no formal educational details are provided, his extensive research output and leadership roles underscore his expertise in computational visualistics and autonomous systems.
Dr. Christian Schulz is a Postdoc at the High Frequency Systems research group within the Faculty of Electrical Engineering and Information Technology at Ruhr University Bochum. His work focuses on microwave and terahertz systems for industrial, biomedical, and environmental applications. Specializes in plasma diagnostics using radar-based sensors Develops terahertz waveguide sensors for fluid and material analysis Active in millimeterwave radar for combustion and smoke detection Contributor to radar test environments and beam steering antennas His recent publications highlight advancements in THz spectroscopy , radar SLAM algorithms , and overmoded waveguide junctions . Key trends include dielectric waveguide innovations , FMCW radar applications , and machine learning integration for sensor data augmentation. He collaborates with international institutions such as Purdue University (USA), Nagoya University (Japan), and European Microwave Conference networks.
Professor Hartmut Surmann is a leading academic in robotics at Westphalian University of Applied Sciences, where he heads the Teaching and Research Area for Autonomous Systems and the Robotics Laboratory. With expertise spanning autonomous mobile robots, rescue robotics, machine learning, and 3D computer vision, he has established himself as a key figure in applying robotics to real-world emergency response scenarios. Westphalian University of Applied Sciences, Department of Computer Science and Communication Head of Robotics Laboratory Member of German Rescue Robotics Center (DRZ) Active participant in EU-funded robotics projects Prof. Surmann holds a Diplom in Computer Science from the University of Dortmund (1989) and completed his PhD work on fuzzy systems using genetic algorithms and neural networks. His research interests focus on autonomous systems with particular emphasis on rescue robotics, machine learning, sensor data processing, and 3D computer vision. His work bridges theoretical advancements with practical applications, especially in disaster response scenarios where robotic systems can save lives. His publication record demonstrates a consistent focus on multi-robot collaboration for disaster response, with particular expertise in 3D mapping, sensor fusion, and autonomous navigation systems. His research has evolved from foundational work in neural networks and fuzzy logic to practical implementations of drone and ground robot teams working together in real disaster scenarios. The trend in his work shows increasing sophistication in multi-robot coordination, semantic understanding of environments, and practical deployment of systems in actual emergency situations. Best Paper Award at SSRR 2017 for '3D Registration of Aerial and Ground Robots for Disaster Response' Key contributor to EU-funded TRADR project (2014-2018) Co-founder of German Rescue Robotics Center (DRZ) Active participant in multiple EU robotics projects including NIFTI Prof. Surmann has supervised numerous student projects and theses focused on practical robotics applications. His work has attracted significant research funding through EU projects like TRADR and NIFTI, with practical applications demonstrated in real disaster responses including earthquakes in Mirandola (2012) and Amatrice (2016), as well as industrial fires and flood disasters. His collaborations span academic institutions, emergency services, and industry partners across Europe. The Robotics Laboratory under his direction maintains a diverse fleet of robotic platforms including ground robots (TurtleBot, VolksBot, Baxter), aerial drones, and specialized rescue robots. The lab actively collaborates with emergency services through the German Rescue Robotics Center, providing a bridge between academic research and practical implementation in real-world rescue operations. Current work focuses on AI integration for robotic systems through the AI-Arena project.
Dr. Balázs Sonkoly is a researcher specializing in edge computing, serverless applications, and 5G network optimization. His work focuses on latency-sensitive systems, multi-user augmented reality coordination, and cloud-native microservice management across distributed architectures. Key research themes include network function virtualization (NFV), software-defined networking (SDN), and programmable packet-optical networks Recent publications analyze AI-driven autoscaling, P4-assisted serverless migration, and multi-domain service orchestration Article trends reveal expertise in mixed reality frameworks, federated learning for vehicular networks, and edge-cloud convergence. His collaborations span institutions like IEEE, SIGCOMM, and the UNIFY project. Contributions to conferences like NOMS, ISMAR, and GLOBECOM highlight practical implementations of edge cloud platforms, SLAM integration, and latency-constrained resource allocation.
Dr.-Ing. Alexander Braun serves as Senior Vice President Digitalization and IT Systems and Chief Information Officer (CIO) at the Technical University of Munich (TUM), while also leading the Digital Twinning in the Built Environment research group. He is affiliated with the Chair of Computing in Civil and Building Engineering within the Department of Civil, Geo and Environmental Engineering at TUM's School of Engineering. Dr. Braun's research focuses on the intersection of construction engineering, digital technologies, and artificial intelligence. His primary areas of expertise include Digital Twinning, Building Information Modeling (BIM), construction progress monitoring, point cloud processing, and computer vision applications in construction. His work bridges the gap between theoretical research and practical implementation in the construction industry, with a strong emphasis on automation and data-driven approaches. His extensive publication record demonstrates a consistent focus on advancing digital construction technologies. Recent research has centered on AI-enhanced digital twinning, semantic modeling from point clouds, construction process analysis through computer vision, and knowledge representation for construction sites. These efforts have resulted in numerous high-impact publications in top journals including Automation in Construction and Advanced Engineering Informatics . As an educator, Dr. Braun has taught courses such as "Bau- und Umweltinformatik 1" and "Bau- und Umweltinformatik Ergänzungsmodul" at TUM, contributing to the development of future construction informatics professionals. His supervisory role extends to numerous Bachelor's and Master's theses, reflecting his commitment to academic mentorship. Senior Vice President Digitalization and IT Systems at TUM Chief Information Officer (CIO) at TUM Group Lead for Digital Twinning in the Built Environment Researcher in Construction Informatics since 2013 Dr. Braun actively contributes to the academic community as a reviewer for prestigious journals including Automation in Construction , Advanced Engineering Informatics , and Visualization in Engineering . His international research collaborations include a visiting researcher position at CyberBuild (University of Edinburgh) with Dr. Frédéric Bosché in May 2019.
Le Chen is a Doctoral Researcher at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems and ETH Zurich , advised by Prof. Bernhard Schölkopf and Prof. Dieter Büchler. Previously, he obtained his M.S. in Electrical Engineering and Information Technology from ETH Zurich and gained research experience at Microsoft Mixed Reality & AI Lab, Tencent AI Lab, and Tencent Robotics X Lab. Research Interests: Le Chen focuses on the intersection of robotics and machine learning, with a specific emphasis on reinforcement learning for dexterous manipulation, visual-inertial calibration, and uncertainty-aware robotic perception. His work spans dynamic motion control, policy gradient subspaces, and novel algorithms for 3D/4D reconstruction. Key Contributions: He co-developed the RP1M dataset for bimanual piano manipulation and contributed to tendon-driven robot design ( Safe & Accurate ) and LEAP-VO for robust visual odometry. His research also includes Gaussian splatting dynamics for 4D content creation ( GaussianFlow ). Scientific Awards: Best Systems Paper Finalist at RSS 2024 for the RP1M dataset
Yiming Zhou is a researcher at Saarland University of Applied Sciences (htw saar) in Saarbrücken, Germany. Their work spans multiple engineering domains with emphasis on artificial intelligence integration, 3D scene reconstruction, and advanced imaging techniques. Contact: yiming.zhou@htwsaar.de Location: Goebenstraße 40, 66117 Saarbrücken Research Focus Zhou's research explores cutting-edge technologies in: AI applications for non-destructive evaluation Dynamic SLAM systems for robotics Next-generation 3D reconstruction methods Multimodal deception detection frameworks Novel Gaussian splatting techniques Semantic encoding for spatial data Recent Publications Trends Their scholarly output reveals a trajectory toward Real-time spatial mapping solutions Hybrid neural-implicit representations CAD-integrated building documentation AI-enhanced image translation pipelines Signal processing innovations Cross-modal data fusion Laboratory Affiliation Zhou contributes to the faculty laboratories at htw saar, focusing on engineering research through practical implementations and algorithm development.
Prof. Sen Cheng is a Professor at the Institute of Neuroinformatics (INI), part of the Faculty of Computer Science at Ruhr-Universität Bochum. His research focuses on computational neuroscience, particularly the neural mechanisms underlying learning, memory, and spatial navigation. He leads a lab investigating hippocampal dynamics, combining mathematical modeling with optogenetic and electrophysiological data analysis. Collaborations span neuroscientists in Germany and internationally. Research interests include hippocampal replay mechanisms, episodic memory functions, and the interplay between sensory inputs and neural networks. His work bridges computational models (e.g., spiking neural networks) with experimental data from rodents and humans. Recent projects explore deep reinforcement learning agents for spatial navigation and the role of grid cells in cognitive mapping. Publications span Neuron , Current Biology , and eLife , addressing topics like hippocampal network stabilizations, spatial coding efficiency, and cerebellar contributions to fear extinction. Teaching includes courses on computational neuroscience, artificial neural networks, and mathematical psychology. Lab activities involve interdisciplinary research through colloquia like Brains in Space and supervision of master’s and bachelor’s theses in areas like reinforcement learning algorithms and embodied associative networks. The INI’s mission drives his work, linking biological insights to artificial cognitive systems design.
Markus Hahn is a Professor of Electrical Engineering at Technische Hochschule Ulm (THU). He serves as a Member of the Senate and Head of the Internship Office within the Faculty of Electrical Engineering. His expertise spans AI-driven edge computing, automated driving systems, radar scene understanding, and software engineering. Hahn holds a Dr.-Ing. from Bielefeld University and has extensive industry experience with Bosch, Daimler, and Continental. His research focuses on safe autonomous systems, sensor fusion, and distributed algorithms. He has supervised four PhD students and led grants such as the BMBF-funded MIKE project. Notable awards include the DAGM Young Talent Award (2007) and CMIM 2018 Best Paper Awards. Education: PhD in Computer Science, Bielefeld University (2010) Diploma in Cognitive Robotics, Ilmenau University of Technology (2007) Research Interests: Combining edge AI with radar technology to enable real-time environmental perception for autonomous systems. Specializes in sensor fusion, radar data interpretation, and distributed software architectures. Grants: Ongoing: MIKE - Enhancing Inclusion via Edge AI Completed: AdaptIVe (EU), interactIVe (EU), UR:BAN (BMWi) Awards: Multiple best-paper recognitions at IEEE conferences and industry-driven research excellence awards.
Dr. Giang T. Nguyen is an academic researcher affiliated with Dresden University of Technology (Germany), specializing in advanced networking technologies. His primary affiliation is with the College of Computer Science and the Department of Networking and Communication Systems. He collaborates extensively with Prof. Frank H. P. Fitzek and other researchers on cutting-edge projects. Research Interests: Giang's work focuses on Network Functions Virtualization (NFV) , Edge Computing , Time-Sensitive Networking (TSN) , 5G/6G Technologies , and In-Network Computing (COIN) . He explores applications in industrial automation, tactile internet, and immersive media delivery, with a strong emphasis on latency reduction and network reliability. Publications: His recent work includes advancements in programmable network coding, TSN testbed development, and latency-optimized architectures for XR and IoT systems. Over 100+ publications since 2013 reflect his contributions to edge computing frameworks, network simulation tools (e.g., ns-3, OMNeT++), and collaborative SLAM systems. Key Projects: He leads initiatives like the TSN-FlexTest measurement testbed and the NET Playground heterogeneous network lab. His research bridges theoretical networking concepts with practical implementations in industrial robotics and emergency response systems.
Tayyab Naseer is affiliated with the Albert-Ludwigs-Universität Freiburg, specifically within the Technische Fakultät (Faculty of Engineering) and the Autonomous Intelligent Systems Department. His research focuses on robust visual perception for outdoor robots, deep neural networks, and machine learning. He holds a BSc in Electrical Engineering from the University of Engineering and Technology Lahore (2005–2009) and an MSc in Communication Systems Engineering from the Technical University of Munich (2010–2012). He completed his PhD at the Autonomous Intelligent Systems group under Prof. Dr. Wolfram Burgard at Freiburg. His work emphasizes long-term autonomy in robotics, addressing challenges like seasonal changes in visual localization. Notable achievements include the Best PhD-Student Award at ICVSS 2016 . He has contributed to projects like LifeNav and published extensively in robotics conferences (e.g., ICRA, IROS) and journals like IEEE Transactions on Robotics. His research often intersects with computer vision, SLAM, and deep learning for robotic systems. Teaching roles include lab instructor for Image and Video Compression at TU Munich and teaching assistant for Introduction to Mobile Robotics at Freiburg. He has also delivered invited talks at workshops organized by LUMS and TU Kaiserslautern, focusing on field and assistive robotics.
Prof. Dr.-Ing. Darius Burschka is a Professor of Robotics, Artificial Intelligence, and Embedded Systems at Technische Universität München (TUM), specifically within the TUM School of Computation, Information and Technology. He leads the Professorship of Robotics, Artificial Intelligence and Embedded Systems and collaborates closely with the German Aerospace Center (DLR). His academic career includes postdoctoral research at Yale University (1998), associate research scientist and assistant research professor roles at Johns Hopkins University (1999-2005), and has been at TUM since 2005. His research focuses on sensor systems in robotics, human-machine interfaces, video-based navigation, and 3D reconstruction. Notable contributions include advancements in laser-based map generation, monocular navigation algorithms, and endoscopic image registration for medical applications. Prof. Burschka has received awards such as the Airtec 2010 Silver Award and best paper awards from prominent conferences like IROS and MICCAI. His work spans robotics, autonomous systems, computer vision, and medical imaging, with a strong emphasis on real-world applications in automotive, healthcare, and industrial automation. Education: Bachelor/Master in Electrical Engineering at TUM Doctorate (Dr.-Ing.) in Electrical Engineering, TUM (1998) His research interests include: Autonomous navigation and control Multi-sensor fusion and 3D reconstruction Human-robot interaction and haptic systems Robotic perception in dynamic environments Recent articles highlight advancements in graph neural networks for action segmentation, hybrid tracking systems, and latency modeling in industrial robotics, reflecting his ongoing contributions to cutting-edge robotics and AI.
Charles E. Thorpe is a Professor at Carnegie Mellon University, affiliated with the Robotics Department within the College of Engineering. His work focuses on robotics, autonomous systems, computer vision, and sensor-based navigation. He has contributed significantly to projects like the NavLab, an early autonomous vehicle initiative, and has pioneered research in simultaneous localization and mapping (SLAM), obstacle detection, and machine learning applications in robotics. His collaborations span across academia and industry, with notable partnerships in autonomous vehicle development and robotics education. Key Research Areas: Autonomous Vehicles, SLAM, Computer Vision, Robotics Algorithms, Human-Robot Interaction. Major Projects: NavLab, Urban Navigation Systems, Laser-Based Perception. Thorpe's publications emphasize practical robotics solutions, from theoretical algorithms to real-world implementations in urban environments. His work bridges robotics theory with applications in healthcare, transportation, and education.
Dr. Heiko Bülow is a Researcher at Jacobs University Bremen, affiliated with the Department of Electrical Engineering & Computer Science. His work focuses on multidimensional signal processing, autonomous systems, and robotics, with emphasis on underwater mapping and spectral registration techniques. He holds a PhD from Jacobs University Bremen and has over 8 years of industrial R&D experience in sonar signal processing. Education: PhD in Electrical Engineering & Computer Science, Jacobs University Bremen M.Phil, University of Glamorgan Diapoma (FH), University of Applied Sciences Braunschweig/Wolfenbüttel Research interests include spectral registration algorithms, machine vision, and pattern recognition applied to robotics. He contributes to EU projects like Co3-AUVs and MORPH, advancing underwater and aerial robotic systems. His publications emphasize robust data registration methods for noisy environments and autonomous navigation. Publications reflect strong focus on 3D mapping (e.g., spectral registration of sonar/range data), UAV photomapping, and gesture recognition for human-robot interfaces. His work bridges theoretical signal processing with practical robotic applications in underwater and aerial domains. Advising/grants: Not explicitly listed in available texts, but his involvement in EU projects indicates collaborative research funding. Labs/teams: Works within robotics and autonomous systems groups at Jacobs University, likely contributing to underwater robotics and SLAM systems.
Mathias Wien is a Professor and Head of the Chair of Image Generation and Image Processing at RWTH Aachen University. He specializes in video and image communication, with a focus on compression standards, 3D video technology, and perceptual quality metrics. His work spans medical imaging applications, adaptive coding techniques, and algorithm optimization for real-time video processing. Research interests include video coding algorithms, immersive media standards (e.g., VVC), point cloud quality assessment, and efficient template matching methods for reference picture padding. He actively contributes to MPEG and IEEE initiatives, co-authoring standards and reviewing emerging technologies. Recent publications (2021–2025) emphasize advancements in template-based video coding, medical image segmentation using deep learning, and viewer training protocols for visual assessment. He leads a research team addressing challenges in scalable compression, dynamic mesh coding, and 3D LiDAR odometry. His team collaborates with institutions like the RWTH Aachen University Medical Department and industry partners, focusing on clinical applications of imaging technology.
