Yuze He is a Research Fellow in the Computer Science Department at Carnegie Mellon University, specializing in infrastructure-supported autonomous driving systems. His research integrates LiDAR technology, real-time mapping, and edge computing to enhance perception and localization for autonomous vehicles. Recent work focuses on high-resolution panoramic LiDAR systems, roadside infrastructure coordination, and efficient 3D mapping solutions. He develops federated learning frameworks for distributed edge platforms and contributes benchmark datasets for traffic analysis.
Stephany Berrio Perez is a Research Fellow at the Australian Centre for Robotics, University of Sydney. Her research focuses on perception and mapping for autonomous vehicles, with expertise in sensor fusion, SLAM, and V2X cooperative perception. She holds a PhD from the University of Sydney (2021) and a Master's from Universidad del Valle (Colombia). Her work addresses challenges in real-time data alignment, bandwidth-efficient V2X communication, and domain adaptation for autonomous systems. Research Interests: Stephany's research spans autonomous vehicle perception, multi-sensor fusion (LiDAR, cameras), and cooperative V2X systems. She has developed frameworks for robust map maintenance, edge case testing, and safety protocols for autonomous navigation. Her international collaborations include projects with France's LS2N laboratory and Cornell University's Co-Sense initiative. Key Research Themes: 3D object detection and domain adaptation Latency-resilient V2X data fusion Human-robot interaction in urban environments Autonomous vehicle safety validation Student Supervision: Stephany advises research students on topics including human-machine interfaces, 3D occupancy prediction, and rural autonomous navigation. Lab Affiliation: Australian Centre for Robotics (ACFR), where her team focuses on real-world deployment of perception systems in complex urban scenarios.
Xin Zhao is a prominent professor at Renmin University of China's School of Information, Department of Computer Science, with an extensive publication record spanning from 1997 to 2026. His research demonstrates significant contributions across multiple disciplines including artificial intelligence, natural language processing, computer vision, and interdisciplinary applications in ecology, medicine, and business. Dr. Zhao's research interests are remarkably diverse, focusing primarily on artificial intelligence and its applications. His work spans natural language processing, large language models, information retrieval, machine learning, and computer vision. Recent publications reveal a strong emphasis on practical AI applications in healthcare (dental implant failure prediction), environmental science (kelp bed dynamics), finance (green finance impact), and industrial systems (composite curing process monitoring). His research often combines theoretical advancements with real-world problem solving, demonstrating both academic rigor and practical relevance. Analysis of his recent publications (2024-2026) shows a clear trend toward interdisciplinary AI applications, with increasing focus on multimodal learning, robustness in dynamic environments, and practical implementations across various sectors. His work bridges theoretical computer science with domain-specific challenges in medicine, ecology, finance, and industrial engineering. The publications demonstrate sophisticated methodological approaches including deep learning architectures, mathematical modeling, and novel algorithmic solutions to complex problems. Dr. Zhao has made significant contributions to the academic community through numerous publications in high-impact journals and conferences including IEEE Transactions, ACL, AAAI, and CVPR. His collaborative work spans international boundaries, with co-authors from institutions worldwide, indicating strong research networks and interdisciplinary collaborations. His research group appears to be actively engaged in cutting-edge AI research, with particular strengths in large language models, multimodal learning, and practical AI applications. The group's work on projects like C-3PO (Compact Plug-and-Play Proxy Optimization) and RMoA (Optimizing Mixture-of-Agents) demonstrates leadership in emerging AI methodologies. Current research directions include enhancing LLM capabilities, improving multimodal understanding, and developing robust AI systems for real-world applications across diverse domains.
Dr. Holger Caesar is an Assistant Professor in the Intelligent Vehicles group at TU Delft. His research focuses on scalable learning and annotation approaches for autonomous vehicle perception and prediction. He previously led Motional's Data Annotation, Autolabeling, and Data Mining teams. He holds a PhD in Computer Vision from the University of Edinburgh. Education: PhD in Computer Vision (University of Edinburgh), studies at KIT Karlsruhe, EPFL Lausanne, ETH Zurich Research emphasizes datasets like nuScenes/nuPlan and methods like PointPillars. His work addresses safety, sensor fusion, and closed-loop testing in autonomous systems. Collaborations include projects on 2050 mobility scenarios and photorealistic safety testing frameworks.
Prof. Amel BOUZEGHOUB is a Professor at Telecom SudParis, affiliated with the SAMOVAR research center. Her work focuses on AI, IoT, and data-driven systems with applications in smart environments, robotics, and education. She has contributed to over 50 peer-reviewed publications spanning machine learning, reinforcement learning, and semantic data processing. Research Interests: Her research bridges theoretical advances in machine learning with practical applications in smart homes, autonomous systems, and educational technology. She explores topics like human activity recognition, anomaly detection in social networks, and real-time data stream processing. Recent Trends: Her 2023-2024 work emphasizes explainable AI, reinforcement learning for autonomous systems, and multi-agent frameworks for stream reasoning. Earlier contributions include IoT-based supply chain traceability and distributed human activity recognition models. Grants & Projects: Key contributions include the ANR INCOME project on multi-scale context management for IoT systems and ACMES initiatives in educational technology. Labs/Teams: Active within the SAMOVAR lab at Telecom SudParis, collaborating with international teams in AI and robotics research.
Liding Zhang is a Ph.D. candidate and researcher at the Chair of Robotics, Artificial Intelligence and Real-Time Systems at the Technical University of Munich (TUM), supervised by Prof. Alois Knoll. His work focuses on advanced robotics with emphasis on motion planning and multi-robot systems. His educational background includes: Master’s degree in Mechanical Engineering and Automation Technology from Technical University of Clausthal (2022) Bachelor’s degree in Mechanical Engineering from Rhine-Waal University of Applied Sciences (2020) Zhang's research centers on sampling-based asymptotically optimal motion planning, high-DOF multi-robot manipulation, coordinated control of mobile robot fleets, and real-time performance optimization. His work explores geometric reasoning in complex robotic systems, addressing challenges in manipulation, navigation, and control through innovative algorithmic approaches that balance computational efficiency with solution quality. Key contributions include adaptive sampling techniques and optimization frameworks for dynamic environments. His publication record (2023-2025) demonstrates consistent contributions to top robotics venues including ICRA, IROS, and IEEE Transactions. The body of work reveals a cohesive research trajectory advancing sampling-based planning through innovations in informed search strategies, adaptive batch processing, and physics-inspired optimization. Recurring themes include handling high-dimensional configuration spaces, ensuring real-time performance, and developing robust solutions for multi-robot coordination and deformable object manipulation. Zhang actively contributes to the robotics community as a reviewer for major conferences (ICRA, IROS, Humanoids) and journals (RA-L, T-ASE, T-Mech), and chaired the Robot Motion Planning IV session at IROS 2024. As a researcher, he participates in nationally and EU-funded projects including Bavarian State Project KI.FABRIK. He mentors bachelor's and master's students on topics spanning LLM-based risk mapping, task and motion planning, and multi-robot coordination, with several theses resulting in peer-reviewed publications. He operates within TUM's Robotics, AI and Real-Time Systems group, which pioneers real-time control systems and advanced planning algorithms for industrial and service robotics applications, maintaining strong industry collaborations and state-of-the-art experimental facilities.
Prof. Andreas Birk is a Professor of Electrical Engineering & Computer Science at Constructor University Bremen gGmbH, leading the Robotics Research Group. His work bridges basic research in artificial intelligence and applied robotics, with a focus on underwater systems, disaster response, and autonomous navigation. He holds a PhD from Universität des Saarlandes and has held visiting professorships at Vrije Universiteit Brussel and other institutions. Education: Dr. rer. nat. (1995) – Universität des Saarlandes, Saarbrücken MSc (Diplom) – Computer Science (1993) BSc (Vordiplom) – Computer Science (1991) Research Interests: Birk’s dual focus spans theoretical intelligence modeling and engineering applications like underwater robotics, SLAM (Simultaneous Localization and Mapping), and human-robot interaction. His projects include underwater cultural heritage mapping, autonomous container unloading, and deep-sea exploration. Key Projects: CADDY: Cognitive Autonomous Diving Buddy (EU-funded HRI research) MORPH: Multimodal Underwater Surveys with UUVs FloodEvac: Robotic assessment of flood risks in infrastructure Awards: Recognized for innovation in robotics competitions (e.g., RoboCup, ICRA), and leadership in IEEE TC Safety, Security, and Rescue Robotics. Received the Ernst-Lange-Prize and Faulhaber Award. Teaching & Outreach: Pioneered online robotics education during the pandemic, emphasizing hands-on labs. Co-founded Constructor’s Robotics program and mentors through Entrepreneurship and Innovation initiatives. Labs & Teams: Leads the Robotics Research Group, collaborating with teams on underwater vision, simulation-in-the-loop validation, and multi-robot systems.
Rafael Patrick is an Assistant Professor in the Department of Industrial and Systems Engineering at Virginia Polytechnic Institute and State University (Virginia Tech), part of the College of Engineering. His research focuses on human factors psychology, auditory situation awareness, and user-centered design with applications in transportation systems, virtual reality, and wearable technology. He holds a Ph.D. in Industrial & Systems Engineering from North Carolina A&T State University (2018), an M.S. in Human Factors and Systems from Embry-Riddle Aeronautical University (2012), and a B.S. in Human Factors Psychology from the same institution (2008). Dr. Patrick’s professional history includes roles as a graduate research assistant at North Carolina A&T’s Transportation Institute and research supervisor at Embry-Riddle’s McNair Scholars Program. He teaches courses such as Discrete Event Systems Modeling & Simulation and Occupational Biomechanics & Ergonomics. His work has been recognized through awards including the McNair Program’s Outstanding Alumnus Mentor Award and Title III Dissertation Fellow designation. His research trends emphasize improving safety through auditory interface design, with recent studies examining bone conduction technology in VR environments and pedestrian-vehicle communication systems. Collaborations include developing the TESSERACT loudspeaker array for spatial sonification and investigating distractions at campus crosswalks. Professional affiliations include the Human Factors and Ergonomics Society (HFES) and Institute of Industrial and Systems Engineers (IISE). Education: Ph.D., Industrial & Systems Engineering, North Carolina A&T State University (2018) M.S., Human Factors and Systems, Embry-Riddle Aeronautical University (2012) B.S., Human Factors Psychology, Embry-Riddle Aeronautical University (2008) Awards: Dr. Ronald E. McNair Scholars Program: Outstanding Alumnus Mentor Award NC A&T Title III Dissertation Fellow NC A&T ISE Department Awards: Humanitarian, Research, & Teaching Labs/Teams: Engages in interdisciplinary projects involving virtual reality safety systems and wearable auditory interfaces through Virginia Tech’s College of Engineering initiatives.
Lammert Kooistra is a Professor at the Laboratory of Geo-information Science and Remote Sensing, part of Wageningen University & Research. His research focuses on advancing remote sensing technologies and unmanned aerial vehicles (UAVs) for precision agriculture, environmental monitoring, and ecological applications. He leads projects involving UAV-based LiDAR, hyperspectral imaging, and SLAM algorithms for agricultural and ecological studies. Key research areas include crop health monitoring, vegetation dynamics analysis, and developing novel UAV systems for data collection. His work bridges robotics, computer vision, and environmental science, with applications in precision livestock farming, disease detection in crops, and soil health assessment. Kooistra has pioneered methods for body weight estimation in cattle using LiDAR and has contributed to datasets on grassland management and forest phenology. Recent projects involve SLAM (Simultaneous Localization and Mapping) for UAV navigation in vineyards, thermal infrared sensing for stress responses in forests, and deep learning models for soybean yield prediction. He supervises PhD candidates in areas like wetland monitoring, UAV-based disease detection, and semantic localization in woody plants. Collaborations span international institutions, with contributions to open datasets on UAV LiDAR, hyperspectral imagery, and agricultural monitoring. His lab emphasizes practical applications of geoinformatics to address challenges in sustainable agriculture and environmental stewardship.
Dr. Fengjun Yan is an Associate Professor in the Department of Mechanical Engineering at McMaster University. His research focuses on modeling, estimation, and control of dynamic systems, particularly in automotive applications such as internal combustion engines, hybrid electric vehicles, and powertrains. He also explores robotics, mechatronics, and thermal-fluid sciences. Dr. Yan teaches courses like Robotics (MECH ENG 4K03/6K03), Internal Combustion Engines (MECH ENG 4Y03), and Advanced Control on Internal Combustion Engines (MECH ENG 755). He holds a B.Sc. from Harbin Institute of Technology (2004), M.Sc. from Tsinghua University (2006), and Ph.D. from Ohio State University (2012). His research interests span advanced control strategies for automotive systems, battery management, data-driven modeling, and autonomous vehicle technologies. Notable contributions include work on diesel engine after-treatment systems, lithium-ion battery health estimation, and path-following control for autonomous vehicles. Dr. Yan actively mentors graduate students and has published extensively in top-tier journals and conferences. His articles highlight advancements in control systems for hybrid vehicles, thermal management in data centers, and machine learning applications for traffic prediction. While no specific awards are listed, his prolific research output underscores his expertise in mechanical and energy systems. He maintains a lab focused on practical applications of robotics and mechatronics, integrating theory with real-world engineering challenges.
Prof Jochen Trumpf is a Professor in the School of Engineering at the Australian National University (ANU). He holds an ORCID identifier and has an h-index of 22 with over 2,500 citations. His research focuses on control theory, observer theory, optimization on manifolds, and applications in robotics, computer vision, and wireless communication. He completed his PhD in Mathematics at the University of Würzburg (2002) and held postdoctoral positions at Ben-Gurion University of the Negev and the University of Notre Dame prior to joining ANU in 2003. His research interests emphasize geometric approaches to nonlinear systems, including equivariant filter design, attitude estimation, and SLAM (Simultaneous Localization and Mapping). He has led or co-investigated multiple projects, including the National Facility for Electricity Grid Security and Resilience Research (2023–2025) and studies on distributed collaborative localization and control. He has collaborated widely, with notable contributions to sensor fusion, inertial navigation, and observer-based control strategies. Prof Trumpf’s work integrates mathematical rigor with practical engineering challenges, with over 90 publications in peer-reviewed journals and conferences. His projects often involve cross-disciplinary teams addressing issues in autonomous systems, navigation, and sensor technology. Despite no explicit awards listed, his citation metrics and project leadership reflect significant academic impact. He supervises research students and has been involved in doctoral training programs, such as the Defence Staff PhD Agreement with Joyce Mau (2018–2022). His research extends to applications in robotics, environmental sensing, and smart grid systems, reflecting a balance between theoretical innovation and real-world problem-solving.
Marcelo H. Ang Jr. is a Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), where he also serves as the Director of the Advanced Robotics Centre. With expertise spanning robotics, control systems, and intelligent automation, he has made significant contributions to mobile manipulation, compliant control, and multi-robot systems. His work bridges theoretical foundations with practical applications in manufacturing, surveillance, and human-robot interaction. Research Interests: Robust Mobile Manipulation in Unstructured Environments Distributed Mobile Robotic Systems Man-Machine User Interface Control of Dynamic Behavior of Robot Manipulators Passive Compliance and Flexible Robots Mobile Robotics Intelligent Control using Neural Networks and Fuzzy Reasoning His research spans fundamental robotics concepts like impedance control and compliant manipulation to cutting-edge applications in multi-robot systems, autonomous navigation, and soft robotics. Recent work focuses on mobility-enhanced sensor networks, deep learning for perception, and autonomous vehicles. Scientific Awards: Awards for Excellence 2000, for Most Outstanding Paper in 1999 Volume for "A Walk-Through Programmed Robot for Welding in Shipyards" Research Activities: Professor Ang has led multiple funded projects including "Integration of Solid Modeling Systems and Robot Controller Architectures" (1990-1995), "Management of Manufacturing Technologies" (1993-1995), and "Research and Development of a Ship-Welding Robot" (1994-1997). He has supervised numerous students, including Ph.D. candidate Zheng Liu who worked on multi-robot surveillance systems. His laboratory at NUS develops advanced robotic systems for applications ranging from ship welding to autonomous vehicles.
Jonathan P. How holds the title of Richard Cockburn Maclaurin Professor in the Department of Aeronautics and Astronautics at MIT. He leads the Aerospace Controls Laboratory and is a prominent figure in autonomous systems, robotics, and control systems research. His work emphasizes multi-agent planning, trajectory optimization, and SLAM (Simultaneous Localization and Mapping) in complex environments. He has contributed to advancements in aerospace robotics, guidance systems for spacecraft, and resilient control algorithms for autonomous vehicles. Education: B.A.Sc. in Engineering Science (Aerospace) from the University of Toronto (1983–1987), S.M. (1987–1990) and Ph.D. (1990–1993) in Aeronautics and Astronautics from MIT. His research spans topics like decentralized planning, neural network control verification, and deep learning for terrain navigation. Research Interests: Focused on autonomous systems, including multi-agent coordination, robust control design, and AI-driven navigation. Key areas include safe autonomy for uncrewed vehicles, socially compliant robot navigation, and space mission planning. Publications: Over 150 peer-reviewed articles in journals like IEEE Transactions on Robotics and conferences such as ICRA and IROS. Recent work addresses global localization on planetary surfaces, physics-informed learning for terrain traversal, and resilient neural feedback systems. Awards: Elected to the National Academy of Engineering (2021), IEEE Fellow (2018), and numerous best paper awards. Recognized for contributions to control systems education and building community within MIT's A&A department. Advising & Grants: Advised numerous graduate students and led projects funded by NASA, DARPA, and the U.S. Air Force. Current grants focus on multi-agent SLAM, safe autonomous systems, and AI for space exploration. Labs & Teams: Directs the Aerospace Controls Laboratory, collaborating with industry partners like NASA and Boeing. Active in IEEE Control Systems Society, including editorial roles and conference organization.
Peter Gaskell is a Lecturer at the University of Michigan, specializing in Robotics and interdisciplinary engineering education. His work focuses on autonomous systems, robotics platforms for teaching, and advanced materials research. He holds an office in FMCRB 3268 and is active in both academic and applied research domains. Research interests include: robotics education frameworks, navigation systems for unmanned aerial vehicles (UAVs), safe autonomous vehicle design, battery chemistry innovations using graphene composites, and acoustic transducer development. His recent work bridges theoretical physics with practical engineering applications like mixed reality calibration for robotics experiments. Key technical contributions span UAV control systems for embedded platforms (2021), graphene-based battery anodes (2017), and foundational studies in graphene material properties (2010-2013). His educational projects emphasize project-based learning using small unmanned aircraft systems. Publications demonstrate cross-disciplinary collaboration across robotics, materials science, and electrical engineering. His work frequently appears in top journals focusing on applied physics and engineering systems.
DUPUIS Yohan is a Research Director at CESI, leading the Engineering and Numerical Tools research team. His work focuses on perception and mapping for cyber-physical systems, intelligent robotics, and transportation systems. He holds an HDR from the University of Rouen Normandy (2019) and a PhD in Electrical Engineering (2012). He coordinates major national projects including the France 2030 CAIRE project (AI sector), ASTRID Robotics SCOPES, and Battery School initiatives. Education includes an Engineering Diploma from ESIGELEC (2009), MSc in Electrical Engineering from Union Graduate College (USA), and a PhD focused on omnidirectional vision for biometrics. His research spans semantic mapping, autonomous systems, and sensor fusion with applications in smart cities, industrial automation, and maritime networks. Key research interests include: Multi-agent cooperative perception SLAM algorithms for low-texture environments Autonomous vehicle localization Human-robot interaction in industrial contexts LiDAR and radar sensor systems He advises 8 active PhD students working on topics like human-robot affordance modeling, object pose estimation, and mobility prediction in smart cities. Notable contributions include the SynWoodScape autonomous driving dataset and innovative digital twin methodologies for industrial workstations. Led projects include: France 2030 CAIRE project (AI sector coordination) ASTRID Robotics SCOPES (2022-2025) Battery School initiative (2022-2027) ConfluenceS Excellence program (2024-2032) His publications span over 50 peer-reviewed articles, with recent focus on multimodal perception systems and AI-driven urban mobility solutions.
