Rui P. Rocha is an Associate Professor at the University of Coimbra , specializing in Robotics and Multi-robot Systems . With over 15 years of active research, his work spans autonomous navigation , swarm intelligence , and human-robot collaboration , particularly in precision forestry and active aging applications. University of Coimbra (Current) ISR-UC (Institute of Systems and Robotics - University of Coimbra) Ingeniarius Lda. (Collaborative Partner) His research integrates AI with ROS (Robot Operating System) for cooperative perception and multi-robot coordination . Recent projects include EuroAGE+ for elderly quality-of-life improvement and SEMFIRE for forestry maintenance using multi-robot systems. Key contributions include comparative analyses of 2D/3D SLAM techniques, fractional-order PSO algorithms, and Bayesian learning frameworks for scalable patrolling missions. His work balances theoretical advancements with practical implementations in environmental and healthcare robotics .
Brandon Colelough is a PhD student and postgraduate researcher at the University of Maryland's Department of Computer Science. His research focuses on symbolic-driven explainability in Artificial Intelligence to enhance trust in human-AI interactions. He is advised by Professor William Regli and expected to graduate in June 2028. Research Interests: Ethical Artificial Intelligence Trustworthiness of AI systems Human-machine teaming dynamics Semantic mapping for SLAM Neuro-symbolic AI integration Environmental mapping optimization Academic Background: Honours Class I graduate from the University of New South Wales Alumni of the Australian Defence Force Academy and Royal Military College of Duntroon Research Trends: Brandon's work spans multiple AI subfields including Neuro-Symbolic AI, SLAM optimization, and deep learning applications. His publications focus on AI explainability, multi-agent systems, and power flow modeling, reflecting interdisciplinary expertise bridging symbolic reasoning with practical implementation challenges. Professional Affiliation: Signals Corps Officer in the Australian Army
Luis A. Garcia is an Assistant Professor at the University of Utah's Kahlert School of Computing, specializing in secure and resilient cyber-physical systems (CPS) and IoT. His work focuses on leveraging system semantics for enhanced security, integrating human logic with deep learning, and ensuring privacy in ubiquitous sensing. Previously, he held roles at USC Information Sciences Institute, UCLA's Networked & Embedded Systems Lab, and Siemens Corporate Research. Garcia earned his PhD in Computer Engineering from Rutgers University under Dr. Saman Zonouz. Education: PhD in Computer Engineering (Rutgers University, 2018) Former Affiliations: USC Department of Computer Science, UCLA ECE Department His research addresses critical challenges in CPS security through formal verification, neurosymbolic AI, and experimental testbeds like the SPHERE CPS Enclave. Recent work includes covert data exfiltration mitigation, intrusion backtracking systems, and AI-driven privacy firewalls for smart environments. Publications span cybersecurity, robotics, and IoT with notable contributions in IEEE/ACM venues. Garcia actively collaborates with industry and academia on projects funded by NSF and DoD, focusing on safety-critical systems and human-AI trust frameworks.
David M. Rosen is an Assistant Professor at Northeastern University, affiliated with the Departments of Electrical and Computer Engineering, Mathematics, and the Khoury College of Computer Sciences (by courtesy). He leads the Robust Autonomy Lab (NEU-RAL), focusing on mathematical and algorithmic foundations for trustworthy autonomous systems. ScD in Computer Science (2016), Massachusetts Institute of Technology MA in Mathematics (2010), University of Texas at Austin BS in Mathematics (2008), California Institute of Technology His research combines nonlinear optimization, differential geometry, abstract algebra, and probability to design robust algorithms for machine perception and control. Recent work emphasizes convex relaxation techniques for problems like SLAM and rotation averaging, enabling provably optimal solutions in real-world settings. Recent publications highlight advancements in range-aided SLAM, distributed pose-graph optimization, and spectral synchronization methods. These works often integrate semidefinite programming and Riemannian optimization to address non-convex challenges in autonomous navigation. 2023: Grant from Draper Laboratory for decentralized multi-agent perception 2023: Grant from MIT Lincoln Laboratory for certifiable perception tools His awards include the WAFR Best Paper Award (2016), RSS Pioneer Award (2019), RSS Best Student Paper Award (2020), and IEEE T-RO King-Sun Fu Award Honorable Mention (2021). He has contributed to key tools like SE-Sync and Shonan averaging, widely used in robotics and computer vision communities.
Jeff Orchard is an Associate Professor at the Cheriton School of Computer Science , University of Waterloo , with cross-appointments to the Department of Applied Mathematics and Department of Education . He directs the Neurocognitive Computing Lab and is a core member of the Centre for Theoretical Neuroscience . In 2022, he completed a 6-month sabbatical at the International Centre for Neuromorphic Systems (Western Sydney University). Education: PhD in Computing Science, Simon Fraser University (2003) MSc in Applied Mathematics, University of British Columbia (1996) BMath in Applied Mathematics, University of Waterloo (1994) Dr. Orchard's research focuses on understanding the brain through computational and mathematical modeling, particularly through neural networks , predictive coding , and vector symbolic architectures . His work bridges neuroscience , machine learning , and neuromorphic engineering , seeking mechanistic rules governing cognition and behavior. Earlier research involved medical image processing , including MRI motion compensation , image denoising , and retinal vessel segmentation . Recent publications (2022-2025) highlight his exploration of biologically-plausible learning algorithms , neuromorphic computing , and AI alignment through active inference models. His work on predictive coding networks and hyperdimensional computing demonstrates interdisciplinary innovation across computer science , mathematics , and cognitive neuroscience .
Li Kunyi is a researcher at the Chair of Computer Science Applications in Medicine at the Technical University of Munich . Their work bridges computer science and medical imaging, focusing on advanced 3D reconstruction and scene understanding techniques. Research Interests: Computer Vision 3D Reconstruction Medical Imaging SLAM (Simultaneous Localization and Mapping) Deep Learning for Graphics Optical Engineering Article Trends: Recent publications emphasize Gaussian splatting for open-vocabulary 3D modeling, 4D SLAM for dynamic environments, and implicit surface reconstruction using monocular cues. Applications span both robotics and medical imaging, with a focus on real-time systems and semantic scene understanding.
Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania, where he has been a faculty member since 1998. A distinguished IEEE Fellow, he has held leadership roles such as Director of the GRASP Laboratory (2008–2013), Associate Dean for Graduate Education (2012–2016), and Faculty Director of Online Learning since 2016. His academic journey includes an undergraduate degree in Electrical Engineering from the National Technical University of Athens (1986) and a PhD in Computer Science from the University of Karlsruhe (1992). Education : National Technical University of Athens (BEng), University of Karlsruhe (PhD) Daniilidis is renowned for his contributions to geometric deep learning, data association, event-based cameras, and vision-based manipulation and navigation. His work spans visual odometry, omnidirectional vision, 3D pose estimation, and structure from motion, with a focus on integrating deep learning with geometric principles for real-time applications. His publications highlight advancements in event camera datasets, spherical CNNs for rotation-equivariant representations, and unsupervised motion learning. These works bridge robotics, computer vision, and neural networks, emphasizing dynamic scene analysis and robust perception systems. Scientific Awards : IEEE Fellow Best Conference Paper Award at ICRA 2017 Best Student Paper Finalist at Robotics Science and Systems 2018 Daniilidis has contributed to key academic roles, including Associate Editor of IEEE Transactions on Pattern Analysis and Machine Intelligence (2003–2007) and co-chairing conferences like IEEE 3DPVT 2006 and ECCV 2010. He leads research initiatives in the GRASP Laboratory, focusing on cutting-edge robotics and perception technologies.
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.
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.
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.
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.
Zoran Sjanic is an Adjunct Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering (ISY), working in the field of Automatic Control. His research primarily focuses on sensor fusion, visual-inertial navigation, and robotics perception systems. His research interests include: Visual-Inertial SLAM and Odometry Dense Optical Flow using Deep Learning Multi-sensor Image-based Navigation State Estimation and Filtering Robotic Perception and Autonomous Navigation Collaborative Environment Mapping The recent publications indicate a strong trend in integrating deep learning with classical estimation frameworks for improved robustness in navigation systems, particularly in GPS-denied environments. His work bridges computer vision, control theory, and robotics. Scientific contributions include advancements in optical flow evaluation, sliding window estimation, and collaborative qualitative mapping. Notable collaborations include researchers such as Gustaf Hendeby, Martin Skoglund, and Patrick Doherty. He has not listed any formal advisees or awards in the provided material. No information about grants or advising activities is available. There is no mention of lab or research team leadership in the current text.
Niclas Vödisch is a Ph.D. student and researcher at the Autonomous Intelligent Systems Lab in the Department of Computer Science at the University of Freiburg. Supervised by Prof. Dr. Wolfram Burgard and co-supervised by Prof. Dr. Abhinav Valada, he is actively contributing to the field of robotics and AI as a member of the ELLIS Society. His research focuses on enhancing machine perception and SLAM systems using deep learning methods, with primary applications in mobile robotics and autonomous driving. Education: Ph.D. Student at University of Freiburg (June 2021 - June 2025, thesis submitted, defense pending) Visiting Ph.D. Student at University of Zurich (June 2024 - December 2024) M.Sc. in Computational Science and Engineering at ETH Zurich (September 2018 - May 2021) Visiting Undergraduate Student at Carnegie Mellon University (August 2016 - May 2017) B.Sc. in Computational Engineering Science at RWTH Aachen University (September 2014 - June 2018) Vödisch's research interests center around Continual Learning for Robotics, Machine Perception, and Simultaneous Localization and Mapping (SLAM). His work addresses the critical challenge of reducing dependency on extensive annotated training data in robotic perception systems. Through innovative approaches leveraging foundation models, he has developed methods that achieve high performance with minimal supervision, making robotic systems more adaptable to real-world environments. His research spans theoretical advances in deep learning and their practical implementation in autonomous systems. An analysis of his publication record reveals a clear progression from foundational SLAM techniques to sophisticated continual learning frameworks and foundation model applications. A unifying theme across his work is data efficiency in robotic perception, with increasing emphasis on collaborative multi-agent systems and cross-modal information integration for robust performance in challenging conditions. His recent publications demonstrate growing influence in the robotics community, evidenced by the IROS 2024 Best Paper Award for his BEVCar work. Scientific Awards: IROS 2024 Best Paper Award on Cognitive Robotics (BEVCar) IROS 2024 Best Student Paper Award Finalist (BEVCar) Dean's List at Carnegie Mellon University (fall 2016) DAAD full scholarship for CMU studies (2016-2017) Niclas has mentored numerous Master's students on projects spanning LiDAR panoptic segmentation, collaborative scene graph generation, and autonomous driving systems. His teaching portfolio includes co-organizing seminars on Robot Learning and Learning with Limited Supervision, as well as leading the FreiCAR practical autonomous driving course across multiple semesters. His research has received substantial funding from the German Research Foundation (DFG) Emmy Noether Program, NVIDIA academic grants, and Qualcomm Technologies Inc., reflecting the significance and potential impact of his work. As a key contributor to the Autonomous Intelligent Systems group at Freiburg, Vödisch collaborates closely with the Robot Learning group and has extended his research network through his visiting position at the University of Zurich's Robotics and Perception Group. His interdisciplinary approach bridges computer vision, robotics, and machine learning, positioning him at the forefront of research in AI-powered autonomous systems with practical real-world applications.
Javier Civera Sancho is an Associate Professor at the University of Zaragoza, where he serves as Deputy Director of the Institute of Research in Engineering of Aragon (I3A). He is affiliated with the School of Engineering and Architecture in the Department of Computer Science and Systems Engineering, where he leads research in the RoPeRT group (Robotics, Computer Vision and Artificial Intelligence). With a background in Industrial Engineering and a PhD in Systems and Computer Engineering, both from the University of Zaragoza, Civera has established himself as a prominent researcher in computer vision and robotics. Industrial Engineering degree (2004, University of Zaragoza) PhD in Systems and Computer Engineering (2009, University of Zaragoza) Civera's research primarily focuses on computer vision, with special emphasis on SLAM (Simultaneous Localization and Mapping), 3D reconstruction, and spatial artificial intelligence. His work aims to provide computers with human-like visual capabilities, including object recognition, 3D structure estimation, spatial context understanding, and tracking of moving objects. He has maintained a long-standing research line in scene localization and mapping, making significant contributions to visual SLAM methodologies and applications. His publication trends reveal a strong focus on advancing visual SLAM techniques, with recent work exploring neural rendering integration, semantic mapping, robust camera calibration, and applications in challenging environments like agriculture. His research bridges theoretical computer vision with practical robotics applications, showing increasing integration with deep learning approaches while maintaining strong geometric foundations. 2 research sexenios (with last granted for 2012-2017) 2 teaching quinquenios h-index: 21 (Google Scholar) 397 citations in last 5 years (Google Scholar) Civera has directed 2 doctoral theses and is currently supervising 4 PhD students. His research has been supported through various institutional frameworks at the University of Zaragoza, including his role in the I3A which provides critical research infrastructure. He is actively involved in guiding the next generation of researchers in computer vision and robotics, emphasizing both theoretical understanding and practical implementation. As a member of the RoPeRT research group, Civera collaborates with a multidisciplinary team working at the intersection of robotics, computer vision, and artificial intelligence. His work often involves developing systems that integrate multiple sensor modalities and create robust spatial understanding for autonomous agents.
John J. Leonard is the Samuel C. Collins Professor of Mechanical and Ocean Engineering at MIT, serving as Associate Department Head for Research in the Department of Mechanical Engineering. His work focuses on navigation and mapping for autonomous systems, particularly self-driving cars and underwater robotics. He pioneered research in Simultaneous Localization and Mapping (SLAM), addressing uncertainties in robotic navigation. Leonard holds a B.Eng. from the University of Pennsylvania (1987) and a Ph.D. from the University of Oxford (1991). His research interests include long-term visual SLAM in dynamic environments, autonomous vehicle safety, and human-robot collaboration. He leads projects funded by institutions like Toyota, exploring Level 2.99 autonomous systems that enhance safety through advanced perception and decision-making. Notable contributions include work on trajectory prediction, underwater scene reconstruction, and certifiable robot perception algorithms. Leonard’s research bridges academia and industry, addressing societal challenges like traffic safety and underwater exploration. He emphasizes ethical considerations in autonomous systems and collaborates with policymakers to shape regulations. His educational efforts focus on training the next generation of robotics engineers through courses and mentorship.
