Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).
Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays
Musa Jouaneh is a Professor in the Mechanical, Industrial and Systems Engineering department at the University of Rhode Island 's College of Engineering . His research spans Robotics, Automation, Mechatronics , and Motion Control systems, with recent work focusing on robotic rehabilitation platforms, fastener extraction, and neural network applications in disassembly processes. Education: Ph.D., Mechanical Engineering, University of California at Berkeley (1989) M.Eng., Mechanical Engineering, University of California at Berkeley (1986) B.S., Mechanical Engineering, University of Louisiana, Lafayette (1984) Research Trends in Jouaneh's recent publications emphasize robotic rehabilitation using magnetic actuation, automated fastener detection via neural networks, and trajectory optimization for servo motor systems. His work bridges mechatronic design with industrial automation , particularly in disassembly and assembly applications. Grants include projects like "Cobots for Outfitting of Hangers" (ONR, 2023) and "Device for Proprioception Training" (RI Commerce, 2024). He leads the Mechatronics Lab and Intelligent Control and Robotics Laboratory , focusing on practical automation solutions.
Sewoong Oh is a Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he has been faculty since 2019. His research focuses on the foundations of machine learning with particular emphasis on differential privacy, secure and robust machine learning, and federated learning. Prior to joining UW, he was at the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign from 2012-2019. He is affiliated with multiple NSF AI Institutes including ACTION (Agent-based Cyber Threat Intelligence and Operation), AI-EDGE (Future Edge Networks and Distributed Intelligence), and IFML (Foundations of Machine Learning). Oh received his PhD in Electrical Engineering from Stanford University in 2011 under Andrea Montanari, followed by postdoctoral work at MIT's Laboratory for Information and Decision Systems under Devavrat Shah. His educational background spans top institutions in theoretical computer science and electrical engineering. His research spans the critical intersection of machine learning security, privacy, and robustness. Oh's work addresses fundamental challenges in making AI systems reliable and trustworthy, with particular focus on defending against backdoor attacks, developing privacy-preserving algorithms, and creating efficient tokenization methods for language models. His recent SuperBPE work demonstrates how moving beyond traditional subword tokenization can significantly improve language model efficiency and performance. His research consistently bridges theoretical foundations with practical implementations, as evidenced by numerous open-source repositories containing production-ready code. Oh's publications reveal a consistent trajectory toward addressing security and privacy concerns in increasingly complex machine learning systems, with recent work focusing on language model tokenization, data-centric AI, and federated learning frameworks. His research shows strong interdisciplinary connections between theoretical computer science, statistics, and practical machine learning systems. ACM SIGMETRICS best paper award (2015) NSF CAREER award (2016) ACM SIGMETRICS rising star award (2017) GOOGLE Faculty Research Awards (2017, 2020) 2024 ICML Best Paper Award (for work by advisee Jon Hayase) Professor Oh maintains an active research group with numerous PhD students, postdocs, and undergraduate researchers. His students have secured prestigious positions at leading tech companies including Amazon, Google, and Snap, as well as academic appointments at institutions like University of Wisconsin-Madison and Shanghai Jiao Tong University. He has received substantial research funding through his participation in multiple NSF AI Institutes and industry research awards from Google. His lab maintains several active GitHub repositories implementing cutting-edge research in backdoor defense, robust statistics, and privacy-preserving machine learning.
Hao Su is an Associate Professor in the Department of Computer Science and Engineering at University of California, San Diego . He serves as Chairman & CTO of Hillbot Inc , and leads the SU Lab which focuses on building autonomous systems that learn actively in physical environments. His affiliations include the Institute for Learning-enabled Optimization at Scale , Artificial Intelligence Group , Contextual Robotics Institute , Halicioğlu Data Science Institute , and Center for Visual Computing . As a researcher in Computer Vision, Robotics, and Neural Geometry , he has made significant contributions to 3D foundation models, reward-free world models, diffusion policy frameworks, and GPU-accelerated simulation environments. His 2024-2025 publications include advancements in hand-eye calibration, dynamic mesh reconstruction, and multi-stage robotic manipulation. His scientific awards include: Frontiers of Science Award (2025) TPAMI Young Research Award (2025) NSF CAREER Award (2023) ACM SIGGRAPH Best Doctorate Thesis Honorable Mention (2019) He has served as Program Chair for CVPR 2025 and Area Chair for ICLR 2022 and NeurIPS 2023 , while previously serving as Publication Chair for 3DV 2016 and Program Committee for SIGGRAPH Asia Workshops .
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.
Jia Deng is a Professor of Computer Science at Princeton University and directs the Princeton Vision & Learning Lab. His research focuses on computer vision, machine learning, and robotics, with an emphasis on advancing 3D vision and synthetic data generation. Ph.D., Princeton University, 2012 B.Eng., Tsinghua University, Computer Science His work spans optical flow, depth estimation, and visual reasoning, leveraging procedural scene generation and robust neural architectures. Recent publications highlight advancements in multi-layer depth estimation, stereo matching, and simulation environments for embodied AI. Alfred P. Sloan Research Fellowship, 2018 NSF CAREER Award, 2020 ONR Young Investigator Award, 2020 Multiple Best Paper Awards (ECCV, ICCV, 3DV) Deng leads the Princeton Vision & Learning Lab, which develops foundational tools for computer vision and machine learning. His mentorship extends to advising students and collaborating on interdisciplinary projects.
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).
Professor Andrew Davison holds the position of Professor of Robot Vision at Imperial College London's Department of Computing. He leads the Dyson Robotics Laboratory and the Robot Vision Research Group, focusing on advancing SLAM (Simultaneous Localization and Mapping) and Spatial AI. His groundbreaking work includes the MonoSLAM algorithm (2003), enabling real-time 3D vision for robotics and AR/VR. Current research emphasizes scalable, semantic-rich Spatial AI systems, as outlined in his FutureMapping papers (2018–2019). Education: BA in Physics (Oxford, 1994), D.Phil. (Oxford, 1998). Postdoctoral work at AIST, Japan (1998–2000), followed by a lectureship at Imperial (2002–present). Industrial collaborations include SLAMcore, a Spatial AI startup, and Dyson Robotics Lab. Over 18 PhD students supervised, many now leading roles at Meta, NVIDIA, SLAMcore, and academia. Notable contributions include DTAM, KinectFusion, and Event Camera SLAM. Recognized for software tools like SceneLib and contributions to robotics benchmarks (SLAMBench). Active on Twitter (@AjdDavison) for research updates.
Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.
Sicun Gao is an Associate Professor in the Computer Science and Engineering department at the University of California, San Diego. His research focuses on practical algorithms for NP-hard search and optimization problems in computational systems, emphasizing combinatorial perspectives in numerical and statistical contexts to achieve reliable autonomy. Research Interests: Automated reasoning, Hamilton-Jacobi reachability, safe reinforcement learning, control barrier functions, and optimization in cyber-physical systems. Teaching: Courses on AI search, optimization, and graduate research seminars. The 15 most recent publications highlight advancements in safe AI control, motion planning, and policy optimization, often integrating neural networks with formal verification. Awards include the IEEE Power & Energy Society Technical Committee Prize Paper Award and the IROS RoboCup Best Paper Award. He advises PhD students working on AI-driven control and robotics, with alumni placed at institutions like Seoul National University, Amazon, and Apple. Grants include NSF Career, Air Force Young Investigator, and DARPA Assured Autonomy funding. His lab develops tools like dReal for automated reasoning in nonlinear theories over the reals.
Harris H. Wang is an Associate Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University's Vagelos College of Physicians and Surgeons, where he also serves as Interim Chair of Systems Biology. He is affiliated with the Center for Computational Biology and Bioinformatics (C2B2) and the Integrated Program in Cellular, Molecular and Biomedical Studies (CMBS). B.S., Physics and Mathematics, MIT Ph.D., Biophysics, Harvard University Dr. Wang's research lies at the intersection of systems and synthetic biology, focusing on developing foundational technologies for genome engineering, microbiome manipulation, and synthetic genomics. His lab pioneers methods such as MAGE, MAGIC, CAST, and CAMII to enable high-throughput genetic manipulation, in situ microbiome engineering, and AI-driven microbial culturomics. Key research themes include understanding microbial community dynamics, engineering cellular memory systems, designing biocontained genetic circuits, and applying synthetic biology to human health challenges in personalized medicine and infectious disease. His recent publications reveal a strong trend in spatial and functional metagenomics, CRISPR-based microbiome editing, and synthetic biology tools for data storage and genetic stability. The articles span high-impact journals like Nature , Science , and Nature Biotechnology , reflecting his leadership in developing scalable, programmable biological systems. Scientific Awards: NIH Director’s Early Independence Award Forbes 30 Under 30 in Science Sloan Research Fellowship NSF CAREER Award ONR Young Investigator Award Burroughs Wellcome Fund PATH Award Schaefer Scholar Blavatnik National Award Vilcek Prize PECASE Dr. Wang has advised numerous PhD and postdoctoral researchers, many of whom have gone on to independent scientific careers. His lab is supported by major grants from NIH, NSF, DARPA, DOE, and foundations including the Bill & Melinda Gates Foundation and CZ Biohub NY. He is actively involved in educational initiatives, including organizing Columbia’s iGEM team and the Cold Spring Harbor Laboratory Synthetic Biology course. The Wang Lab is based at the Columbia University Irving Medical Center and is part of national consortia such as the Engineering Biology Research Consortium (EBRC) and the Genome Project-Write (GP-Write) initiative. The lab develops and applies cutting-edge technologies in automation, machine learning, and synthetic biology to engineer microbiomes for applications in medicine, global health, and climate change.
Maja J Matarić is the Chan Soon-Shiong Chaired and Distinguished Professor of Computer Science at the University of Southern California's Viterbi School of Engineering, with courtesy appointments in Neuroscience and Pediatrics. She serves as founding director of the USC Robotics and Autonomous Systems Center, co-director of the USC Robotics Research Lab, and Principal Scientist at Google DeepMind. Previously, she held leadership roles as USC's interim Vice President of Research (2020-2021) and Vice Dean for Research (2006-2019). Her educational background includes: PhD in Computer Science and Artificial Intelligence from MIT (1994) MS in Computer Science from MIT (1990) BS in Computer Science from University of Kansas (1987) Matarić pioneers Socially Assistive Robotics (SAR) , a field her lab named, focusing on human-robot interaction that provides assistance through social rather than physical support. Her research targets critical health and wellness challenges including post-stroke rehabilitation, autism spectrum disorder therapy, cognitive exercises for Alzheimer's patients, ADHD academic support, and mental health interventions. She develops systems modeling user engagement, personality, and motivation, with extensive real-world deployments in schools, rehabilitation centers, and homes. Analysis of her recent publications reveals dominant themes in cognitive health robotics (2025 CHI paper on LLM-powered elder care), pediatric assistive technology (2025 IDC speech therapy review), and adaptive preference modeling (2025 HRI contrastive learning work). Her research consistently bridges machine learning with human-centered design for vulnerable populations. Major scientific recognition includes: ACM Athena Lecturer Award (2024) ACM Eugene L. Lawler Humanitarian Award (2024) ACM Fellow (2020) Presidential Mentoring Award (2011) Multiple society fellowships (AAAS, IEEE, AAAI) As a dedicated mentor, Matarić has championed underrepresented groups through CRA-W, placing numerous women in faculty positions. She leads USC Viterbi's K-12 STEM Outreach Program serving low-income Los Angeles schools and authored The Robotics Prime for student education. Her research has secured significant funding enabling real-world technology transfer, with documented impact in rehabilitation centers and homes through deployable SAR systems. Her Robotics Research Lab at USC drives innovation in embodied AI, with current projects spanning LLM-integrated elder care robots, ADHD academic companions, and autism therapy systems. The lab emphasizes co-design with end-users and rigorous real-world validation across diverse populations.
Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
Roberto Martin-Martin is an Assistant Professor of Computer Science at the University of Texas at Austin, where he leads the Robot Interactive Intelligence (RobIN) Lab. His research bridges robotics, computer vision, and machine learning to enable robots to operate autonomously in human-centric environments like homes and offices. Previously, he was a Postdoctoral Scholar at the Stanford Vision and Learning Lab working with Fei-Fei Li and Silvio Savarese, and an AI Researcher at Salesforce AI. Education: Ph.D. and M.Sc. in Robotics from Technische Universität Berlin (TUB), advised by Professor Oliver Brock B.Sc. from Universidad Politécnica de Madrid Dr. Martin-Martin's research focuses on developing AI algorithms that combine reinforcement learning and imitation learning with advanced planning and control to address core challenges in robot perception. His work spans mobile and whole-body manipulation, dexterous and contact-rich interactions, and long-horizon tasks in unstructured environments. He takes inspiration from human cognition through psychology and cognitive science to develop solutions for skills ranging from simple pick-and-place operations to complex tasks like cooking and furniture assembly. His recent publications demonstrate a strong trend toward enabling robots to learn from human demonstrations, particularly through video, and to safely adapt these demonstrations to their own morphology. There's significant emphasis on mobile manipulation, bimanual tasks, and developing hardware that supports robust robot learning through trial and error. His work shows increasing integration of large language models and vision-language models to enhance robot understanding and task execution. Scientific Awards: RSS Pioneer (2020) Winner of Amazon Picking Challenge (2015) RSS Best Systems Paper Award (2016) ICRA Best Paper Award IROS Best Mechanism Award Amazon Faculty Award AAAI Young Faculty IJCAI Early Faculty Nominated for Best Paper at IROS (2014, 2017) Dr. Martin-Martin advises PhD students including Arpit Bahety, who is working on mobile manipulation and learning. He serves as Chair of the IEEE Technical Committee on Mobile Manipulation and is a co-founder of QueerInRobotics. His research is supported by industry partnerships and academic funding sources that enable his lab to develop both hardware and software innovations in robotics. He directs the Robot Interactive Intelligence (RobIN) Lab at UT Austin, which takes a holistic approach to robot intelligence, developing both the hardware (like the BaRiFlex gripper) and software frameworks necessary for robots to learn from interaction. The lab's research addresses the full pipeline from perception to action, with particular emphasis on learning from human demonstrations, safe exploration, and adapting to novel objects and environments.
