Prof. Dr. Tobias Weiser is a Professor at the Faculty of Electrical Engineering of Kempten University of Applied Sciences, where he also serves as the Program Coordinator for the Robotics (Bachelor) program. Additionally, he is the Scientific Director of the Institute for Applied AI and Robotics in Marktoberdorf, leading cutting-edge research in robotics and artificial intelligence. His research expertise lies in the mechatronic development of robot systems, which involves the synergistic integration of mechanical engineering, electronics, and computer science to design and build intelligent robotic systems. His work particularly emphasizes the application of artificial intelligence to solve complex problems in robotics, with a focus on practical, industry-relevant solutions. This research spans areas such as autonomous navigation, human-robot interaction, and the development of adaptive control systems for robotic applications. As Program Coordinator for the Robotics Bachelor program, Prof. Weiser is actively involved in mentoring students and shaping the educational experience. He oversees the curriculum to ensure it meets industry standards and prepares graduates for careers in robotics. His leadership at the Institute for Applied AI and Robotics fosters collaboration between academia and industry, driving innovation through applied research projects. The Institute for Applied AI and Robotics, under his direction, serves as a hub for interdisciplinary research, bringing together experts from various fields to tackle challenges in robotics and artificial intelligence. The institute's facilities in Marktoberdorf provide a state-of-the-art environment for prototyping and testing robotic systems.
Professor Udo Frese is a faculty member at the University of Bremen, holding the position of Professor of Multisensory Interactive Systems since March 2014. He is associated with the German Research Center for Artificial Intelligence (DFKI) in the Cyber-Physical Systems department and is a member of the research focus 'Minds, Media, Machines'. University of Paderborn (1993-1997): Computer Science Friedrich-Alexander University Erlangen-Nuremberg (2004): Doctorate Professor Frese's research focuses on computer vision, sensor fusion through probabilistic modeling, and algorithms for safety functions with applications in robotics and interaction. His work spans multiple domains including assistive robotics for people with mobility limitations, SLAM (Simultaneous Localization and Mapping) techniques, and human-robot interaction systems. His research group collaborates with DFKI to operate the robot soccer team B-Human, which has achieved remarkable success with multiple world championships. His recent publications demonstrate a strong focus on assistive robotics, with particular emphasis on adaptive control systems for users with limited mobility. His work integrates computer vision, machine learning, and human-centered design principles to develop practical assistive technologies. The research spans from theoretical foundations in probabilistic modeling to practical implementations in real-world assistive applications. Best Technical Paper award at PETRA '24 for 'Probabilistic Combination of Heuristic Behaviors for Shared Assistive Robot Control' Best paper award at EICS 2024 for 'AdaptiX – A Transitional XR Framework for Development and Evaluation of Shared Control Applications in Assistive Robotics' Multiple world championships with robot soccer team B-Human Professor Frese advises several doctoral and master's students including Felix Goldau, Max Pascher, and Moritz Schneider, who contribute significantly to his research in assistive robotics and computer vision. His research is supported through collaborations with DFKI and various projects focused on assistive technologies and robotics. His laboratory at the University of Bremen collaborates closely with DFKI's Cyber-Physical Systems department, operating the successful robot soccer team B-Human. The research group maintains strong connections with both academic and industry partners to advance research in robotics and human-computer interaction.
Reinhard Heil is a scientific Assistant and Researcher at the Institute for Technology Assessment and Systems Analysis (ITAS) at Karlsruhe Institute of Technology (KIT), where he leads the Research Group 'Digital Technologies and Social Change.' His work focuses on the intersection of technology, society, and ethics, with particular emphasis on artificial intelligence, transhumanism, and technology assessment methodologies. Heil holds a Master's degree in Philosophy, Literature and Sociology from TU Darmstadt (completed by 2003) and has been working at ITAS since 2010 as a Research Associate and Project Manager. His academic journey reflects a deep engagement with philosophical questions surrounding emerging technologies. His research interests span the social consequences of artificial intelligence, transhumanism and human enhancement, vision assessment methodologies, and the philosophical dimensions of technology. He has made significant contributions to understanding how AI systems impact society, particularly examining issues of explainability, trust, and the ethical implications of generative AI systems. His work often bridges theoretical philosophical frameworks with practical technology assessment. His recent publications show a clear trend toward analyzing the societal implications of AI, particularly focusing on explainable AI (XAI), the challenges of generative AI systems, and the philosophical questions these technologies raise about human cognition and experience. He has also maintained a consistent research thread on transhumanism and human enhancement, examining historical contexts and contemporary debates. Heil has led and contributed to numerous significant projects including 'Uncontrollable artificial intelligence: An existential risk?', 'Social trust in learning systems', 'Trust through explainability in verifiable online voting systems', 'Interdisciplinary approaches to deepfakes', and 'Deep Genomics – Opportunities and Challenges of the Convergence of Artificial Intelligence, Modern Human Genomics and Genome Editing'. He has also coordinated the 'Assessing Big Data (ABIDA)' project and managed 'Engineering Life'. As part of ITAS, Heil works within the broader context of technology assessment research, contributing to the institute's mission of analyzing emerging technologies from interdisciplinary perspectives. His work often involves collaboration with various research groups focused on digital technologies, sustainable energy, health technologies, and mobility futures. He frequently engages with policymakers and participates in public discourse on technology governance through lectures, workshops, and media appearances.
Dr. Martina Baumann is a Scientific Associate at the Institute for Technology Assessment and Systems Analysis (ITAS) within Karlsruhe Institute of Technology (KIT) and concurrently holds a Research Associate position at Radboud University Nijmegen's Institute for Science in Society. Her multidisciplinary background includes a Master's in Molecular Biotechnology and Philosophy of Science from the Technical University of Munich, where she explored the intersection of art, science communication, and technology ethics. Research Focus Baumann's research centers on ethical and societal implications of emerging technologies, with emphasis on: Health Technology Assessment : Evaluating prosthetic care accessibility and policy frameworks in Germany Applied Ethics : Investigating bioethical challenges in genomics, AI, and medical technologies Consumer Health Technologies : Analyzing user behavior, data privacy, and societal trends in digital health Responsible Innovation : Developing ethical guidelines for autonomous systems and bio-manufacturing Project Leadership She actively contributes to EU and national initiatives including: STIMULUS (current) – Ethical frameworks for emerging tech IANUS (current) – Societal aspects of novel technologies DaDuHealth (completed) – Managed project on health data governance FUTUREBODY/VI-DAS (completed) – Autonomous vehicle ethics Publication Trends Her 15 most recent publications (2023-2025) demonstrate: Dominant focus on prosthetic care ethics and health technology policy Emerging work on consumer health data privacy and self-tracking behaviors Sustained interest in technology governance for AI, robotics, and bioengineering Methodological emphasis on empirical ethics and qualitative stakeholder analysis Affiliations She leads research within ITAS's Health and the Technization of Life group, collaborating across institutions like Radboud University. Professional networks include active participation in European TA conferences and health technology assessment forums.
Dr. Leonie Seng is a Research Fellow at the Leibniz Science Campus, focusing on the Ethics of Artificial Intelligence , Media Ethics , and the Philosophy of Technology . Her work bridges interdisciplinary perspectives on digital transformation, policy advice, and societal impacts of emerging technologies. Key Research Themes: AI ethics, digital culture, philosophical frameworks for automation Recent Publications: Explore ethical implications of AI, digital transformation in research, and media depictions of technology Her research emphasizes ethical reflection in technological development, analyzing concepts like moral agency in AI systems and the role of science fiction in shaping ethical discourse. She critiques traditional vs. digital pedagogy and examines public perceptions of robotics. Notably, her work addresses policy challenges in the digital era, emphasizing the need for adaptive technology assessment methods. Current projects focus on interdisciplinary collaboration and institutional adaptation to digitalization.
Prof. Dr. Jan Bender holds a professorship in Computer Animation at RWTH Aachen University's College of Engineering. As a leading researcher in physics-based simulation methods, his work focuses on developing advanced numerical techniques for fluid dynamics, deformable solids, and multi-physics interactions through Smoothed Particle Hydrodynamics (SPH) and Finite Element Methods (FEM). Key Contributions: Invented PF-FLIP for two-phase flows, developed SymX symbolic framework for energy-based simulations, created STARK unified solver for robotics applications, and introduced implicit boundary handling for SPH Methodologies: Specializes in hybrid Eulerian/Lagrangian approaches, differentiable physics, adaptive discretization, and machine learning integration for simulation acceleration Research Impact: 2023 & 2024 Best Paper Awards in VMV and SCA conferences. His work enables billion-particle fluid simulations and realistic multi-body interactions for robotics, with applications in welding, thermal spraying, and soft robotics. Collaborations: Works extensively with robotics institutes (Gazebo Fluids extension) and materials science departments (TIG welding, thermal spray modeling). Maintains open-source code repositories for simulation frameworks.
Dr. Alon Peled-Cohen is a senior lecturer at the School of Electrical Engineering - Systems within The Iby and Aladar Fleischman Faculty of Engineering at Tel Aviv University, and a researcher at Google Research Tel-Aviv. Dr. Peled-Cohen received his PhD in Machine Learning from the faculty of Industrial Engineering & Management at the Technion - Israel Institute of Technology under the supervision of Professor Tamir Hazan. Dr. Peled-Cohen's research focuses on the intersection of statistical learning, online learning, and decision-making under uncertainty. His work particularly emphasizes the connections between online learning and reinforcement learning, with applications in control theory and optimization. His research addresses fundamental challenges in learning from sequential data, making decisions with limited information, and developing algorithms with provable performance guarantees. He has made significant contributions to understanding regret bounds in various learning settings and has developed novel algorithms for challenging control and optimization problems. Analysis of Dr. Peled-Cohen's publication record reveals a strong focus on theoretical machine learning, particularly in online learning, reinforcement learning, and control theory. His work consistently addresses fundamental questions about algorithmic performance, often establishing tight regret bounds for various learning settings. A notable theme throughout his research is the application of optimization techniques to challenging learning problems, with several papers focusing on linear quadratic control and Markov decision processes. His collaborations span multiple institutions and include prominent researchers in the machine learning community. Dr. Peled-Cohen has published in top-tier machine learning conferences including ICML, NeurIPS, UAI, and AAAI. His research has addressed important problems in online learning, reinforcement learning, and control theory, contributing both theoretical insights and practical algorithms. As a senior lecturer at Tel Aviv University and researcher at Google Research Tel-Aviv, Dr. Peled-Cohen bridges academic research with practical applications. His work has implications for various domains requiring sequential decision-making under uncertainty, including robotics, recommendation systems, and autonomous control systems. While specific grant information is not provided in the available materials, his publications in top venues suggest successful research funding. Dr. Peled-Cohen is actively involved in both academic and industrial research, contributing to the advancement of machine learning theory while maintaining strong connections with practical applications through his position at Google Research.
Basak Gülecyüz is a Ph.D. researcher at the Chair of Media Technology (Lehrstuhl für Medientechnik) at the Technical University of Munich (TUM), affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI). She holds a B.Sc. in Electrical and Electronics Engineering from Middle East Technical University (2016) and an M.Sc. in Communications Engineering from TUM (2019). Research Interests: Haptic communication systems Learning-oriented haptic codecs Teleoperated robot teaching Skill transfer via network-aware teleoperation Vibrotactile signal processing Publications Analysis: Her work focuses on optimizing haptic data transmission for teleoperation under network constraints, developing adaptive sampling strategies, deadband techniques, and perceptual compression algorithms. Key projects include applications in the Tactile Internet and 5G teleoperation systems. Teaching Roles: She served as a MATLAB tutor for Digital Signal Processing in 2022 and supervises student theses on topics like: Network-aware shared control Learning-based human-robot autonomy
Francesco Bianchin is affiliated with the Chair of Information-oriented Control (ITR) at the Technical University of Munich (TUM). His research focuses on networked control systems, data-driven control, and human-centered control, with applications in hybrid exoskeletons, clinical decision-support systems, motion modeling, human-robot interaction (HRI), bio-inspired design, and cooperative robotics. Research Interests: Networked Control Data-driven Control Human-centered Control Bio-inspired Design Clinical Decision-support
Furkan Kaynar is a researcher at the Chair of Media Technology, Technical University of Munich, affiliated with the Munich School of Robotics and Machine Intelligence (MSRM) and the Munich Institute of Robotics and Machine Intelligence (MIRMI). He holds a B.Sc. in Electrical and Electronics Engineering (2016) from Bogazici University and an M.Sc. in Electrical Engineering and Information Technology (2019) from TUM. His research focuses on Machine learning for task-oriented robotic grasping Computer vision and interactive segmentation Haptic communication systems Human-robot interfaces for teleassistance Recent work includes few-shot learning methods for grasp area segmentation via remote demonstrations. Project involvement includes Centre for Tactile Internet with Human-in-the-Loop (CeTI) , Teleoperation over 5G , and IEEE P1918.1.1 Haptic Codecs . Collaborative efforts appear in publications at international conferences. Teaching activities include: Organizing the Seminar on Topics in Signal Processing (WS19/20) Conducting tutorials for Image and Video Compression (SS20)
Kuo-Yi Chao is a Researcher at the Chair for Robotics, Artificial Intelligence, and Real-Time Systems at the Technical University of Munich (TUM), focusing on multimodal sensor fusion for Vehicle-to-Everything (V2X) applications. He received his B.Sc. and M.Sc. in Electrical Engineering and Computer Technology from TUM. His expertise spans multi-agent systems, visual language models, real-time communication, and digital twin technologies. His 2022 publication in the Journal of NeuroEngineering and Rehabilitation highlights his work on intuitive control systems for robotic prostheses, emphasizing sensor fusion and human-robot interaction. He contributes to teaching through courses like 'Einführung in die digitale Signalverarbeitung (IN2061)' and offers thesis topics in collaborative camera perception, real-time V2X data transmission, and object list generation for autonomous systems.
Professor Jörg Ott holds the Chair for Connected Mobility at Technische Universität München (TUM) in the Faculty of Informatics since August 2015. He is also an Adjunct Professor at Aalto University, where he previously served as Professor for Networking Technology from 2005 through 2015. His academic career includes positions as Assistant Professor at Universität Bremen (1997-2005) and research staff with teaching responsibilities at TU Berlin (1992-1997). His research spans network architectures, protocol design, and networked systems , with current focus areas including network and system architectures, robust networking, mobile networked systems, adaptive real-time communication, and network measurements. He has made significant contributions to delay-tolerant networking, edge computing, and internet protocols. Professor Ott has served the networking community extensively, including as co-chair of IETF working groups (MMUSIC, SIP), co-chair of IRTF DTNRG, Treasurer of ACM SIGCOMM, Vice-chair of IEEE Comsoc TCCC, and General Co-Chair of major conferences including ACM SIGCOMM 2012, ACM MobiSys 2018, and ACM CoNEXT 2021. He is currently chair of the Steering Committee of the ACM CoNEXT conference and member of TUM Ethics Board for non-medical sciences. Best Paper Award at ACM ICN conference (2015) Best Student Paper Award at Packet Video Workshop (2012) Professor Ott has supervised numerous students and researchers, with current members of his research group including Wolfgang Wörndl, Ljubica Kärkkäinen, and Leonardo Tonetto. He has co-founded multiple technology companies including Tellique Kommunikationstechnik GmbH, Lysatiq GmbH, Spacetime Networks Oy, and NeMu Dialogue Systems Oy (callstats.io). His teaching portfolio includes courses on Connected Mobility Basics, Edge Computing and the Internet of Things, and Wireless Internet Communication.
Julius Durmann is a Ph.D. student and Researcher at the Department of Computer Science, Technical University of Munich, working under Prof. Martin Bichler's research group since July 2023. His work bridges Algorithmic Game Theory , Market Design , and Machine Learning , focusing on algorithmic collusion in Bertrand settings and equilibrium learning in games. Education : M.Sc. in Robotics, Cognition, Intelligence (2020-2023), B.Sc. in Maschinenwesen (2017-2020), and a semester abroad at ETH Zürich (2021-2022) His research explores collusion of algorithms through models like Online Optimization Algorithms and Agentic Markets , with publications in journals and conferences such as at - Automatisierungstechnik and the European Control Conference . He has contributed to teaching courses like Business Analytics and Machine Learning and Learning in Games , mentoring B.Sc. and M.Sc. students on topics including Learning to Optimize and Electricity Price Forecasting . His methodological interests include Reinforcement Learning and Game Theory applications in computational markets.
M.Sc. Daniel Darnstaedt is a researcher at Martin Luther University Halle-Wittenberg, affiliated with the Faculty of Philosophy I and the Department of Psychology. His work focuses on cognitive control mechanisms in multitasking environments. Research Interests Mechanisms of task-order control in dual-task situations Training of task-order control Multitasking in driving simulations Cognitive modeling Notable Publications 2024: Frontiers in Psychology - Examined equivalence of lab vs. online training for task-order coordination 2022: Zeitschrift für Arbeitswissenschaft - Analyzed expert knowledge representation during industrial robot teach-in Darnstaedt collaborates with Prof. Dr. Torsten Schubert and colleagues on cognitive performance optimization, with applications in both laboratory and real-world environments.
Dr. Mathew Garnett is a Group Leader in Translational Cancer Genomics at the Wellcome Sanger Institute, where he was appointed to the Faculty in 2014. His research focuses on understanding how genetic alterations in cancer cells impact responses to anti-cancer therapies, with the goal of developing more precise cancer treatments. He leads the Garnett Group within the Cancer, Ageing and Somatic Mutation Programme and is a key member of the Cancer Dependency Map initiative. Dr. Garnett's educational background includes: BSc. in Biochemistry (Hons.) from the University of British Columbia, Canada (1999) PhD from The Institute of Cancer Research, London, UK (2005), where he worked on BRAF as a human cancer gene Postdoctoral research at the University of Cambridge with Prof. Ashok Venkitaraman, supported by a Canadian Institute of Health Research fellowship Dr. Garnett's research spans four complementary areas: the genomics of drug sensitivity, synthetic-lethal dependency mapping, organoid cancer models, and tumor-immune cell interactions. His lab performs high-throughput drug sensitivity screens across >1000 cancer cell models, genome-wide CRISPR-Cas9 screens to identify new drug targets, and develops next-generation organoid models that better capture tumor heterogeneity. His work integrates molecular cell biology, high-throughput screening, and cancer genomics to identify biomarkers that predict drug response and discover new therapeutic targets. His team has developed three major public resources: the Genomics of Drug Sensitivity in Cancer (GDSC), Project Score database, and Cell Model Passports. Analysis of Dr. Garnett's recent publications (2024-2025) reveals a strong focus on precision cancer medicine through genomic approaches. His work spans cancer dependency mapping using CRISPR screens, development of advanced cancer models including organoids, and identification of novel therapeutic targets and drug combinations. Key themes include synthetic lethality in microsatellite unstable cancers (particularly targeting WRN helicase), mechanisms of drug resistance, tumor-immune interactions, and computational approaches to integrate multi-omic data for precision oncology. Dr. Garnett's research has generated widely used reference datasets for the scientific community and has directly contributed to the development and testing of new cancer therapies. His work on identifying Werner Syndrome helicase as a synthetic-lethal target in microsatellite unstable cancers has led to the development of novel WRN Helicase Inhibitors. His team's databases (GDSC, Project Score, and Cell Model Passports) serve as critical resources for cancer researchers worldwide. Dr. Garnett leads a multidisciplinary team of researchers and has fostered numerous collaborations, including with the Open Targets partnership, Cancer Research UK, and the Human Cancer Models Initiative. His lab has developed innovative methods for cancer modeling and drug screening that have advanced the field of precision oncology. He is also a member of the scientific leadership team for Open Targets and the Cancer Research UK drug discovery small molecule expert review panel. The Garnett Lab maintains state-of-the-art facilities for robotics, acoustic dispensing, high-content microscopy, and CRISPR screening, enabling high-throughput approaches to cancer research. Through international collaborations like the Human Cancer Models Initiative, his team is generating and characterizing new patient-derived cancer models that better capture tumor heterogeneity for therapeutic development.
