Luca Carlone is the Boeing Career Development Associate Professor in the Department of Aeronautics and Astronautics at MIT and a Principal Investigator at the Laboratory for Information & Decision Systems (LIDS) . He leads the SPARK Lab , focusing on developing certifiable perception algorithms for autonomous systems. PhD in Mechatronics (Polytechnic University of Turin, 2012) Research spans robotics, computer vision, and optimization Research Interests : Certifiable Perception algorithms for high-integrity systems High-level Perception (geometric, semantic, physical understanding) Efficient Perception methods for resource-constrained robots Scientific Contributions include: 2024 Outstanding Systems Paper Award (RSS) 2023 IEEE Transactions on Robotics King-Sun Fu Award 2021 NSF CAREER Award 2020 AIAA Advising Award 2019 Amazon Research Award Advising : Teaches graduate courses like Visual Navigation for Autonomous Vehicles and Robotics: Science and Systems . Collaborates with institutions including JPL, Caltech, and KAIST through the DARPA SubT Challenge.
Michael Kaess is an Associate Professor at the Robotics Institute, Carnegie Mellon University (CMU), within the School of Computer Science. He leads the Robot Perception Lab (RPL) and contributes to the Field Robotics Center (FRC) and Computer Vision Group (CV). His research focuses on efficient perception algorithms for mobile robots, particularly in 3D mapping, SLAM, and sensor fusion using vision, LiDAR, inertial, and sonar data. Kaess holds a PhD in Computer Science from Georgia Tech and was a postdoc at MIT's Marine Robotics Lab. Education: Georgia Institute of Technology, PhD in Computer Science (2008) MIT, Postdoctoral Associate (2008–2010) Research Interests: Kaess develops algorithms for robust and efficient inference in robotics, emphasizing factor graphs and linear algebra. His work spans underwater robotics, aerial systems, tactile SLAM, and multi-sensor integration. Key areas include SLAM with planes/lines, imaging sonar reconstruction, and neural field methods for LiDAR-visual fusion. Publications: Over 145 papers, including work on EDPLVO (visual odometry), HoloOcean (underwater simulation), and neural radiance fields with LiDAR. Recent trends focus on robust incremental smoothing, acoustic-optical fusion, and real-time volumetric mapping. Awards: Recognized with the RSS Test of Time Award (2020), Outstanding Associate Editor (2022), and paper awards at ICRA/ICRA. Active in conference organization (IROS/ICRA program committees). Advising & Grants: Supervises 10+ current PhD/MSc students, with past advisees contributing to CoRL/ICRA work. Manages grants in perception, autonomy, and marine robotics. Teaches courses like Robot Localization and Mapping (16-833). Labs/Teams: Directs RPL, collaborates with FRC on field robotics. Develops open-source tools like GTSAM (GNU Toolkit for Smoothing and Mapping).
Xingxing Zuo is an Assistant Professor (tenure-track) in the Robotics Department at MBZUAI. He holds a PhD from Zhejiang University (2021) and a Bachelor’s from UESTC (2016). Previously, he was a Postdoctoral Scholar at Caltech (2024–2025), a Postdoc at ETH Zurich (2019–2021), and held visiting roles at TU Munich, University of Delaware, and University of Technology Sydney. His research focuses on robotics, 3D computer vision, and embodied AI, with emphasis on robot-human collaboration, state estimation, and sensor fusion. Educations: PhD in Robotics, Zhejiang University (2021, with honors) Bachelor’s in Computer Science, University of Electronic Science and Technology of China (2016, with honors) Research Highlights: Develops novel methods for LiDAR-camera-inertial fusion, neural radiance fields, and radar-cameras systems Pioneered techniques like Flying Co-Stereo (long-range aerial mapping) and FMGS (vision-language embedded 3D splatting) Focuses on real-time SLAM, robust depth estimation, and photorealistic scene reconstruction Awards & Recognition: Best Paper Finalist at ICRA 2021 (CodeVIO) Oral Presentation at ICCV 2021 (MBA-VO) Recipient of Google Visiting Faculty Researcher (2023) Grants & Labs: Organized Thermal Infrared in Robotics workshop at ICRA 2025 Leads research on embodied AI and multi-sensor SLAM systems Develops open-source tools like LIC-Fusion and Coco-LIC frameworks
Sanjiv Singh is a Research Professor at the Robotics Institute within Carnegie Mellon University's School of Computer Science. His academic journey at CMU spans from Systems Scientist (1995-2001), to Senior Research Scientist (2001-2003), Associate Research Professor (2003-2007), and finally Research Professor since 2007. He also holds an adjunct faculty position in Mechanical Engineering since 2009. Singh serves as Editor-in-Chief of the Journal of Field Robotics, demonstrating his leadership in the robotics community. His educational background includes a Ph.D. and M.S. in Robotics from Carnegie Mellon University (1995, 1992), an M.S. in Electrical Engineering from Lehigh University (1985), and a B.S. in Computer Science from the University of Denver (1983). Dr. Singh's research focuses on three primary themes: Autonomous Navigation (developing motion planning and control for ground and air vehicles with applications in agriculture, exploration, and low-flying aircraft), Coordinated Multi-Robots (examining team-based tasks like structure assembly and search/rescue operations), and Forceful Interaction with the world (using physical models to enable robots to handle complex, high-force interactions). His work spans aerial robotics, agricultural and forestry robotics, mining robotics, 3D vision, sensing and perception, visual servoing, motion planning, and field service robotics. Analysis of his recent publications (2016-2020) reveals a strong focus on collision avoidance algorithms, sensor fusion techniques, and real-time navigation systems. His research demonstrates consistent advancement in SLAM (Simultaneous Localization and Mapping) technologies, particularly in GPS-denied environments, with increasing sophistication in handling complex aerial maneuvers and multi-robot coordination. Editor-in-Chief of Journal of Field Robotics Dr. Singh has advised numerous graduate students throughout his career, with current advisees including Matt Aasted (Ph.D), Andrew Chambers (M.S), Hugh Cover (M.S), Michael Dille (Ph.D), and Justin Haines (M.S). His past students include prominent researchers like Sebastian Scherer, Joe Djugash, Fred Heger, Geoff Hollinger, and Ji Zhang who have gone on to make significant contributions in robotics. His research has been supported through various projects including CASC (agricultural applications), Riverine, Transformer, Trestle, and Ember. His laboratory work focuses on developing practical robotic systems capable of operating in challenging real-world environments, with particular emphasis on agricultural applications, search and rescue operations, and coordinated multi-robot teams that can work effectively alongside humans.
Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.
Sarita V Adve is the Richard T. Cheng Professor of Computer Science at the University of Illinois at Urbana-Champaign, where she conducts research spanning hardware, programming languages, operating systems, and applications with a focus on domain-specific systems. Her work bridges theoretical foundations and practical implementations, particularly in extended reality and heterogeneous computing. Her educational background includes a Ph.D. and M.S. in Computer Science from the University of Wisconsin-Madison (1993, 1989) and a B.Tech in Electrical Engineering from the Indian Institute of Technology Bombay (1987). Prior to joining Illinois, she served on the faculty at Rice University from 1993 to 1999. Adve's research centers on generalizable and scalable specialization for domain-specific systems, with current emphasis on extended reality (XR) systems including virtual, augmented, and mixed reality. She chairs the ILLIXR consortium to democratize XR research and developed the first fully open-source XR system (ILLIXR). Her foundational contributions include memory consistency models for C++ and Java programming languages, the Spandex coherence framework for heterogeneous systems, and software-driven approaches for hardware reliability. Her work spans hardware reliability (SWAT and RAMP projects), power management (GRACE system), and instruction-level parallelism. Recent publications reveal a strong focus on energy-efficient XR systems, hardware-software co-design for AI workloads, and resilience analysis. Her team explores rendering offload, visual-inertial odometry optimization, and compositional error injection frameworks, often targeting tradeoffs between energy, latency, and accuracy in mobile and edge environments. Fellow of the American Academy of Arts and Sciences Fellow of the ACM and IEEE ACM/IEEE-CS Ken Kennedy Award Anita Borg Institute Woman of Vision in Innovation Award ACM SIGARCH Maurice Wilkes Award Alfred P. Sloan Research Fellowship UIUC University Scholar University of Illinois Campus Award for Excellence in Graduate Student Mentoring Adve actively mentors students and has received multiple teaching awards. She co-founded the CARES movement to address discrimination in CS research events and chairs CS@Illinois CARES. Her service includes leadership roles in ACM SIGARCH (2015-2019), DARPA/ISAT study group, ACM Council, and Computing Research Association. She has secured significant funding including DARPA initiatives and Google Faculty Research Awards. She leads the ILLIXR consortium and has established collaborative research programs such as the $8.3M DARPA Joint University Microelectronics Program. Her lab focuses on open-source XR development, heterogeneous system architectures, and reliability-aware designs, with strong industry and government partnerships.
Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI), Carnegie Mellon University (CMU), where he leads cutting-edge research in autonomous aerial systems and robotics. His work focuses on enabling unmanned rotorcraft to operate safely and efficiently in cluttered, low-altitude, and extreme environments. Education: Ph.D. in Robotics, Carnegie Mellon University (2010) MS in Robotics, Carnegie Mellon University (2007) BS in Computer Science (Minor in Robotics), Carnegie Mellon University (2004) His research interests span robotics, artificial intelligence, autonomous navigation, obstacle avoidance, SLAM, visual-inertial odometry, energy infrastructure, and public policy . He has made seminal contributions to UAV autonomy, including the first obstacle avoidance for micro aerial vehicles in natural environments (2008) and the first automatic landing zone detection and landing on a full-size helicopter (2010). His recent publications (2023–2025) demonstrate a strong focus on resilient autonomy, multi-robot exploration, foundation models for robotics, and large-scale dataset development. His team has released key datasets like TartanGround , BETTY , and SubT-MRS , and simulation tools like Pegasus Simulator , indicating a systems-level approach to advancing real-world autonomy. The research trends emphasize self-supervised learning, robust perception, risk-aware planning, and multi-modal fusion for off-road and urban environments. Scientific Awards: Popular Science Best of What's New 2010 Award AIAA@Infotech Best Paper Runner-up Award (2010) Siebel Scholar Dr. Scherer has advised numerous students and leads a vibrant research group focused on high-impact robotics applications. He has secured significant grants related to UAV autonomy, energy infrastructure, and urban air mobility. His lab develops experimental infrastructure such as AIrTonomy for testing next-generation autonomous aerial vehicles. He is actively involved in advancing SLAM and localization in extreme environments, notably through participation in the DARPA Subterranean Challenge. His team develops large-scale datasets and benchmarking frameworks to push the boundaries of robustness and generalization in mobile robotics.
Pieter van Goor is a Research Fellow at the Australian National University (ANU), affiliated with the School of Engineering and the Systems Theory and Robotics (STR) group. He holds a PhD in Control Theory (completed 2022) and dual bachelor's degrees (BEng/BSc, 2018). His research focuses on equivariant systems theory, state estimation, and robotics applications. Key contributions include equivariant observer design, Lie group-based control, and geometric data fusion. Education: Bachelor of Engineering (Research & Development) (Honours) in Mechatronics (ANU, 2018) Bachelor of Science in Mathematics (ANU, 2018) PhD in Control Theory (ANU, 2022) Research Interests: Equivariant systems theory, nonlinear control, robotics applications, state estimation on Lie groups, sensor fusion, and geometric control methods. His work emphasizes symmetry exploitation in filter design and observer construction for systems with inherent geometric structures. Grants & Collaborations: Active collaborations include work with Robert Mahony and institutions like the IEEE. Research spans theoretical frameworks (e.g., equivariant filters) and applied systems (e.g., ArduPilot autopilot, event cameras). Labs/Teams: Member of the Systems Theory and Robotics (STR) group at ANU, focusing on advanced control theory and robotics.
Cyrill Stachniss is a Full Professor at the University of Bonn , where he heads the Lab for Photogrammetry and Robotics and is affiliated with the Lamarr Institute for Machine Learning and Artificial Intelligence . He was previously a Visiting Professor in Engineering at the University of Oxford until 2025. His academic journey includes positions at the University of Freiburg, University of Zaragoza, and the Swiss Federal Institute of Technology. University: University of Bonn School: Faculty of Engineering Department: Department of Photogrammetry Academic Rank: Professor His research spans robotics, photogrammetry, SLAM, autonomous navigation, perception systems, agricultural robotics, and unmanned aerial vehicles . He emphasizes probabilistic techniques for mobile robots and has made significant contributions to visual and LiDAR-based localization, scene understanding, and 3D reconstruction. The recent publications highlight a strong trend toward neural implicit representations, agricultural phenotyping, radar-based perception, and active learning . His team develops robust systems for real-world deployment in dynamic and unstructured environments, particularly in precision farming and autonomous vehicles. Scientific Awards: IEEE RAS Early Career Award (2013) Microsoft Research Faculty Fellow (2010) 7th EURON Georges Giralt Award (2008) Multiple Best Paper Awards at ICRA, IROS, RSS, and RAL Faculty Teaching Award, University of Freiburg He has advised numerous students and leads the DFG Cluster of Excellence PhenoRob and Research Unit FOR 1505 Mapping on Demand . His lab has co-founded three startups, reflecting strong industry and societal impact. He also runs the educational video series 5 Minutes with Cyrill , explaining key robotics concepts.
Baris Fidan is a Professor in Mechanical & Mechatronics Engineering at the University of Waterloo, with cross appointments in System Design Engineering and Electrical & Computer Engineering. He is a senior member of IEEE and AIAA. His research focuses on cooperative/adaptive control, autonomous systems, multi-agent networks, and vehicular control applications. He leads the Cooperative & Adaptive Mechatronic Systems (CAMS) Lab, which develops control strategies for autonomous vehicles, robotic systems, and intelligent transportation. Education: PhD in Electrical Engineering, University of Southern California (2003) Masters in Electrical & Electronic Engineering, Bilkent University (1998) Bachelor's in Electrical & Electronic Engineering & Mathematics, Middle East Technical University (1996) Research Interests: His work spans adaptive control theory, sensor networks, multi-agent coordination, autonomous vehicle networks, and biomedical systems control. He emphasizes practical applications in intelligent transportation, robotic navigation, and distributed system optimization. Grants & Projects: He has led major grants including NSERC Discovery Programs on cooperative mechatronic systems and 3D autonomous vehicle coordination. Industrial projects include autonomous driving strategies, vehicle control optimization, and high-precision gear manufacturing technologies. Labs/Teams: Directs the CAMS Lab, which collaborates on projects involving distributed motion planning, sensor localization, and autonomous vehicle networks. Current projects address challenges in urban autonomous driving, cooperative robotic systems, and resilient sensor networks.
Roberto Manduchi is a Professor of Computer Science and Engineering at the University of California, Santa Cruz, within the Baskin School of Engineering. His primary affiliation is with the Computer Science and Engineering department where he leads research in assistive technology for visual impairments. He holds a Dottorato di ricerca in Electrical Engineering from the University of Padova, Italy, and previously worked at Apple and NASA JPL before joining UCSC in 2001. His research focuses on mobile computer vision, inertial sensors, and location-aware systems to enhance spatial awareness and information access for blind and low-vision individuals. Key research areas include indoor navigation systems, screen magnification for low-vision readers, obstacle detection using augmented reality, and text accessibility assessment through specialized OCR pipelines. His work bridges computer vision, human-computer interaction, and accessibility design. Analysis of his recent publications (2022-2025) reveals strong emphasis on inertial-based indoor navigation (e.g., PALMS localization system, backtracking algorithms), screen magnification usability studies, and novel approaches to scene text access for blind users. His research consistently targets practical applications for visual impairment, with significant contributions to pedestrian dead reckoning, magnetic signature localization, and gaze-contingent interfaces. Manduchi serves on the scientific advisory board of Aira and is a board member of the Vista Center for the Blind and Visually Impaired. He leads the UCSC Computer Vision Lab where his team develops accessible computing solutions. His work includes both theoretical contributions to computer vision and tangible assistive applications, with recent projects focusing on smartphone-based inertial odometry, multi-scale tactile maps, and real-time obstacle cueing systems.
Andrew Markham is a Professor of Computer Science at the University of Oxford , affiliated with Kellogg College . He leads a research group focusing on Cyber Physical Systems (CPS) , specializing in sensors, signal processing, and machine learning to enable machines to better perceive the physical world. His work emphasizes cross-disciplinary collaboration, notably in wildlife tracking and indoor positioning systems. He has held roles as a Postdoctoral Fellow (2008-2012), Associate Professor (2013), and Full Professor (2021). Education : PhD in Electrical Engineering (University of Cape Town, 2008), BSc (Hons) in Electrical Engineering (2004). Research Interests : Tracking and localization in GPS-denied environments (e.g., underground, indoors), magneto-inductive systems, physics-informed machine learning, and data-driven approaches for noisy sensor data. His projects include wildlife monitoring via wireless sensor networks and mmWave radar for human motion capture. Key Projects : CARACAL acoustic monitoring system, mmPoint dense human tracking, and RandLA-Net for large-scale point cloud segmentation. His work spans robotics, environmental sensing, and biomedical applications. Advising & Grants : Supervises over 30 students and collaborates with industrial partners. Research teams include Cyber Physical Systems, Autonomous Ubiquitous Sensing, and Wildlife Monitoring initiatives. Labs/Teams : Leads the CPS research group, focusing on sensor networks, inertial navigation, and multimodal fusion systems. Collaborates with zoology and earth science disciplines on applied projects.
Brandon Lucia is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University's College of Engineering. He holds the Kavčić-Moura Professorship and leads the Abstract research group. As CEO and co-founder of Efficient Computer Corp., he bridges academic research with commercial applications in energy-efficient computing. Dr. Lucia received his Ph.D. in Computer Science and Engineering from the University of Washington in 2013, following an MS from the same institution in 2010 and a BS in Computer Science from Tufts University in 2007. His research focuses on the intersection of computer architecture, computer systems, and programming languages, particularly in energy-constrained environments. His primary research interests include intermittent computing, energy harvesting computers, orbital edge computing, and parallel computing systems. Lucia's work addresses fundamental challenges in creating programmable, reliable computing devices that operate without batteries by harvesting energy from their environments, with applications in sensing, medical implants, and space systems. He also investigates software systems and architectures for making parallel computing correct, reliable, and efficient in the post-Moore's Law era. Lucia's publication record shows a clear trajectory toward orbital edge computing and nanosatellite systems, with recent work focusing on computational constellations, visual navigation for satellites, and energy-efficient processing in space. His research spans both theoretical foundations of intermittent computing and practical implementations in hardware and software. 2021 Sloan Research Fellowship 2018 NSF CAREER Award 2018 ASPLOS Best Paper Award IEEE MICRO Top Picks in Computer Architecture (2009, 2010, 2016) 2015 OOPSLA Best Paper Award 2019 IEEE TCCA Young Computer Architect Award 2022 Engineering Faculty Award As an advisor, Lucia has mentored numerous graduate students including Brad Denby, Zhuo Cheng, and Kyle McCleary, many of whom have become co-authors on his significant publications. His lab developed the world's first batteryless PocketQube nanosatellite (Tartan-Artibeus-1), which was deployed to low-Earth orbit aboard the SpaceX Transporter-3 Rocket. Lucia's research has received funding from sources including NSF, DARPA, Google, and VMware, supporting both fundamental research and practical implementations of energy-harvesting computing systems.
Soo Jeon is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, part of the Faculty of Engineering. He holds a PhD from the University of California at Berkeley (2007) and prior degrees from Seoul National University. His research focuses on mechatronics, dynamic systems, and control, with applications in robotics, autonomous systems, and precision motion control. He has held roles as Assistant Professor (2009–2015), Associate Professor (2015–2024), and Full Professor (2024–present). Professor Jeon’s expertise includes intelligent sensing and control for mechatronic systems, nonlinear dynamics, and autonomous systems. He has received notable awards such as the 2022 Engineer of the Year Award (AKCSE/KOFST), 2015 NSERC Discovery Accelerator Supplement, and 2010 ASME Rudolf Kalman Best Paper Award. He serves as an Associate Editor for several journals, including the ASME Journal of Dynamic Systems and IEEE Transactions on Automation Science and Engineering. His research interests span robotics, control systems, and automation, with recent work on autonomous navigation, tactile exploration, and model predictive control. He supervises graduate students in MASc and PhD programs and teaches courses like ME 649 (Control of Machines and Processes) and ME 360 (Introduction to Control Systems). Jeon holds patents in areas like low-power magnetic locks and remote plasma source seasoning. His lab, the Waterloo Mechanical Systems & Control Laboratory (WMSCL), focuses on advanced mechatronics and robotics projects, including collaborations with international institutions such as the Korea Institute of Machinery & Materials (KIMM).
Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.
