Deborah Richards is a Professor in the School of Computing at Macquarie University, affiliated with the Ethics and Agency Research Centre, Performance and Expertise Research Centre, and Frontier AI Research Centre. She holds roles as Industry and External Relations Director for the School and Director of the Virtual Reality Lab. Her research focuses on Intelligent Virtual Agents, Virtual Reality applications in education and health, knowledge acquisition, and cybersecurity ethics. She has authored over 425 publications and led 19 research projects, including studies on AI in healthcare and ethical AI design. Education: PhD (Artificial Intelligence, Macquarie University), MAppSc (Information Studies), BBus (Computing and MIS), and a Postgraduate Certificate in Higher Education (Leadership and Management). She advises on PhD and MRes students in Intelligent Virtual Agents research. Research interests include virtual agents for health behavior change, AI ethics, and educational technology. Notable projects include the eADVICE web-based management system for urinary incontinence and the development of embodied conversational agents for wellbeing support. Awards include the ABC Science Media Fellowship (2005) and Vice-Chancellor’s Citations for Outstanding Contributions to Student Learning (2017). Grants and collaborations span health informatics, cybersecurity, and AI ethics. She co-leads initiatives like the Respectful Maternity Care study and the Virtual Reality Lab’s immersive learning environments. Future work emphasizes ethical AI integration in education and healthcare.
Yonatan Bisk is an Assistant Professor at Carnegie Mellon University (CMU) in the School of Computer Science , with dual appointments in the Language Technologies Institute and Robotics Institute . His research bridges Natural Language Processing (NLP) with robotics, focusing on grounded and embodied language understanding. Assistant Professor, Language Technologies Institute, CMU (2021–Present) Courtesy Appointment, Robotics Institute, CMU Research Themes : Language as a social codification of embodied experience Interpretable multimodal model training Human-robot collaboration frameworks Embodied question-answering systems Selected Trends : His recent publications show increasing focus on cross-modal attention mechanisms (Vid2Robot), error detection in toolchains (Tools Fail), and theory-of-mind reasoning in language agents (SOTOPIA). Multimodal integration spans vision, audio, and robotic control contexts (ANAVI). Labs & Collaborations : Founder of CLAW Lab (Connecting Language to Action and the World) Collaborations with Microsoft Research, Meta Inc, and CMU's REAL (Robotics, Embodied AI, Learning) community
Professor Yasamin Mostofi is a faculty member in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. She is also affiliated with the Department of Computer Science and the Center for Control, Dynamical Systems and Computation. Her work bridges wireless systems, robotics, and machine learning. PhD, Stanford University MS, Stanford University MS, Sharif University of Technology Research Interests : Her lab focuses on wireless systems and autonomous agents , developing novel mathematical models for RF sensing and communication-aware robotics. Current directions include WiFi/millimeter wave/6G-based imaging and analytics, networked robotics for cellular systems, human-robot collaboration, and machine learning integration. Recent Publications : Her work spans through-wall crowd analytics , robot-assisted connectivity , and vision-aided wireless sensing , with applications in smart health, security, and retail optimization. Key trends include cross-modal integration (WiFi and vision), synthetic data generation, and real-world clinical validation. Scientific Recognition : Presidential Early Career Award for Scientists and Engineers (PECASE) Antonio Ruberti Prize, IEEE Control Systems Society NSF CAREER Award IEEE Region 6 Outstanding Engineer Award IEEE Fellow (2020) Advising and Leadership : She mentors PhD students in systems research, with graduates joining leading companies like Qualcomm and Google. She co-founded NPJ Wireless Technology (Nature Portfolio) and serves on editorial boards including IEEE Transactions on Control of Network Systems .
Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .
Emilio Frazzoli is a Full Professor at ETH Zurich’s Department of Mechanical and Process Engineering. He leads the Institute for Dynamic Systems and Control and the Center for Sustainable Future Mobility, focusing on autonomous systems, robotics, and socio-technical control frameworks. Current affiliations: ETH Zurich (Dynamic Systems and Control, Sustainable Future Mobility) Research: Autonomous mobility-on-demand, game theory for resource allocation, and safety verification in multi-agent systems His work bridges robotics, control theory, and economics, with projects like the open-source AMoDeus simulation framework for autonomous taxis and karma-based resource allocation systems. Recent publications emphasize trustworthy AI, reproducibility in autonomous vehicle control, and human-robot interaction challenges. Notable projects include nuReality (VR-based pedestrian interaction studies) and CARSI II (context-driven vehicle interfaces).
Dr. Heather Culbertson is an Assistant Professor in the Department of Computer Science at the University of Southern California, leading the HaRVI (Haptics Robotics and Virtual Interaction) Laboratory. Her research focuses on designing haptic hardware and algorithms to create intuitive human-robot interactions through touch, emphasizing realism in virtual environments and social connectedness. Key areas include mediated touch, wearable haptic systems, and multisensory integration in VR/AR applications. The lab's work spans robotics, human perception modeling, and cross-disciplinary solutions for healthcare and immersive technologies. Research interests revolve around tactile feedback systems, social robotics, and the psychological impact of haptic interfaces. Recent work explores real-time haptic texture rendering, affective communication via touch, and motion platforms for VR. Culbertson's projects often integrate human factors early in design to ensure usability and immersion. Publications highlight advancements in haptics coding standards, drone-human interaction, and emotion regulation systems. Her contributions bridge engineering and cognitive sciences, addressing challenges in wearable tech, surgical training simulations, and assistive robotics. The HaRVI Lab collaborates across disciplines to advance tactile interfaces for virtual reality, social robotics, and medical applications.
Mingyu Ding is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges robotics, embodied AI, and computer vision, focusing on building agents that interact effectively with physical environments. PhD in Robotics, University of Hong Kong (2022), advised by Ping Luo Postdoctoral Fellow, BAIR@UC Berkeley (with Masayoshi Tomizuka) Visiting Scholar, CSAIL@MIT (with Joshua Tenenbaum) B.S. in Computer Science, Renmin University of China (under Zhiwu Lu) His work emphasizes robot learning through physical simulation, multimodal foundation models, and self-supervised methods. Key contributions include Embodied Concept Learner (ECL) and Sparse Diffusion Policy frameworks. Recent publications highlight trends in 3D vision, diffusion-based planning, and language-driven robotic behavior synthesis. Awards include ICRA Best Paper (2024), ME Rising Star (2023), and CVPR Doctoral Consortium (2023). Session Chair for ICRA 2025 Associate Editor for IROS 2025 Guest Editor for Robotics Special Issue: Embodied Intelligence
Marc Erich Latoschik is a Professor in the Department of Human-Computer Interaction at the University of Würzburg, Germany. He previously held roles at Bayreuth University, Bielefeld University, and FHTW Berlin. His research focuses on virtual and augmented reality, embodiment, human-computer interaction, and applications in health, education, and social systems. Education: Completed his PhD in 2001 at Bielefeld University with a thesis on multimodal interaction in virtual reality. Research Interests: Embodied interaction, virtual embodiment, presence and plausibility in VR/AR, avatar design, social virtual reality, health applications (e.g., VR therapy for body image issues), and XR security/privacy. Active in developing frameworks like Reality Stack I/O and MAIL for VR/AR research. Key Projects: ViTraS study on body image exercises in VR, avatars for mass use via smartphone reconstruction, and motion-based biometrics in XR. Collaborates with medical teams on cybersickness detection and emergency training simulations. Labs/Teams: Leads research groups on immersive technologies and social VR applications. Involved in interdisciplinary projects combining HCI, AI, and healthcare.
Gerd Bruder is an Associate Professor at the University of Central Florida , affiliated with the Department of Computer Science . He previously held positions as a Research Associate Professor at UCF's Institute for Simulation and Training (2016-2022), Post-Doc at University of Hamburg (2014-2016), and Post-Doc at University of Würzburg (2011-2014). Habilitation : Computer Science, University of Hamburg (2017) Ph.D. : Computer Science, University of Münster (2011) M.Sc. : Computer Science (minor in Mathematics), University of Münster (2009) Research Interests : Gerd Bruder's work focuses on Extended Reality (XR) systems, spanning Virtual Reality (VR) , Augmented Reality (AR) , and Mixed Reality (MR) . Key areas include human factors , digital twins , 3D user interfaces , and perceptual modeling , with applications in healthcare, military operations, architecture, and smart environments. Recent Publications highlight innovations in XR locomotion , trust calibration with autonomous agents, spatial cognition , and haptic-visual integration . His 2025 articles address task-switching efficiency in XR and collaborative mixed reality design. Scientific Awards : Best Paper at ACM VRST 2023 2021 TechConnect Innovation Award Best Demo at ACM SUI 2021 Multiple Best Paper Awards (2016-2023) Best Poster & Demo Awards (2015-2020) Patents include systems for medical simulation , smart environment interruption management , and adaptive AR interfaces . His work bridges academic research and practical applications across VR, AR, and MR domains.
Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.
Julian Jara-Ettinger is an Associate Professor of Psychology and Computer Science at Yale University. He holds a Ph.D. from MIT (2016). His research focuses on understanding the cognitive and computational mechanisms underlying human social behavior, including fairness, linguistic communication, gesture, moral reasoning, and pedagogy. He employs interdisciplinary methods such as computational modeling, eye-tracking, cross-cultural studies, and developmental research to bridge psychology and artificial intelligence. Key research areas include the development of social cognition in children, the integration of theory of mind with communication, and the application of cognitive science principles to build socially intelligent machines. His work emphasizes how humans infer others' knowledge, intentions, and desires, with implications for AI safety and ethical systems design. Publications span topics like epistemic inference, moral judgments, and the computational foundations of social interaction. His lab's research often intersects with evolutionary simulations, neural modeling, and cultural psychology. No scientific awards are explicitly mentioned in the provided text. Collaborations involve cross-disciplinary teams addressing challenges in developmental science, AI ethics, and cognitive robotics. His work has practical applications in educational strategies, social policy, and human-AI collaboration frameworks.
Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.
Harish Ravichandar is an Assistant Professor at the School of Interactive Computing , Georgia Institute of Technology, and a core faculty member of the Institute for Robotics and Intelligent Machines (IRIM) . He leads the Structured Techniques for Algorithmic Robotics (STAR) Lab , focusing on structured computational frameworks and learning algorithms with inductive biases to enhance robot efficiency, reliability, and self-sufficiency in human-robot collaboration and complex applications like dexterous manipulation and multi-agent coordination. His research bridges robot learning , human-robot interaction , and multi-agent systems , emphasizing stable, frugal, and safe skill acquisition from human demonstrations. Key themes include intention inference , trajectory optimization , and heterogeneous team coordination , often leveraging Koopman operators , hypernetworks , and graph-based methods . Scientific recognition includes the NSF CAREER Award , IEEE MRS Best Paper Award , and Georgia Tech’s College of Computing Outstanding Post-Doctoral Research Award . His work also received the ASME DSCC Best Student Paper Award and P&W Institute Graduate Fellowship . Harish’s educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Connecticut (2018) , an M.S. from the University of Florida (2014) , and a B.E. in Instrumentation and Control Engineering from Anna University (2012) . He previously held postdoctoral and research scientist roles at Georgia Tech before his current position.
Jiaoyang Li is an Assistant Professor at the Robotics Institute, Carnegie Mellon University , where she leads research in large-scale multi-robot coordination. She earned her Ph.D. in Computer Science from the University of Southern California (2022) under Professor Sven Koenig and holds a B.Eng. in Automation from Tsinghua University (2017) . Ph.D. (2022): University of Southern California B.Eng. (2017): Tsinghua University Research Focus : Jiaoyang Li's work centers on algorithms for multi-robot coordination in dynamic environments. Key areas include: Multi-Agent Path Finding (MAPF) Lifelong Planning for Autonomous Systems Kinodynamic Motion Planning Warehouse and Transportation Automation Human-Robot Collaboration Integration of Planning and Machine Learning Swarm Robotics Publication Trends : Recent work spans 2023-2025 , showing emphasis on: Scalable coordination for 10,000+ robots Bézier curve optimization for dual-arm assembly Diffusion models in multi-robot motion planning Guidance graph optimization for airport/railway traffic Multi-objective search in warehouse automation Temporal plan graphs for switchable passing orders Scientific Honors : ICAPS-24 Best Student Paper Award Three top dissertation awards (ICAPS-23, IFAAAMAS, USC) 2023 League of Robot Runners champion Outstanding student paper award at ICAPS-20 Technology Commercialization Award from USC Student Leadership : Advises 5 Ph.D. students and 1 Master's student at CMU, with alumni now at MIT (Ying Feng) and USC (Yimin Tang). Collaborates with visiting students from National University of Singapore and Tsinghua University. Lab Affiliation : Directs the Artificial Intelligence for Robot Coordination at Scale (ARCS) Lab , focusing on: Warehouse automation with thousands of robots Multi-arm LEGO assembly systems Railway coordination algorithms Cooperative assembly with multiple manipulators
Shuran Song is an Assistant Professor of Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. Previously, she was faculty at Columbia University. She holds a Ph.D. in Computer Science from Princeton University and a BEng from HKUST. Her research focuses on the intersection of computer vision and robotics, particularly in embodied AI, robot manipulation, and sensorimotor learning. Song's work emphasizes learning from physical interactions to enable robots to perform complex tasks autonomously. She leads the Robotics and Embodied AI Lab (REAL@Stanford) and has received prestigious awards, including the NSF Career Award, Sloan Fellowship, and Microsoft Faculty Fellowship. Education: Ph.D., Computer Science, Princeton University; BEng, HKUST Affiliations: Stanford School of Engineering, Department of Electrical Engineering Research interests include deformable object manipulation, visuomotor policy learning, and generalizable robot skills. Her lab develops algorithms for robots to learn through interaction, with applications in household assistance (e.g., TidyBot) and industrial automation. Notable contributions include the TossingBot and Diffusion Policy frameworks. Publications span robotics, computer vision, and AI conferences (RSS, ICRA, CVPR), focusing on policy learning, deformable object handling, and embodied intelligence. Awards highlight her impact in advancing robot learning and perception. Advises doctoral and master's students in robotics and AI, and collaborates on grants from NSF, DoD, and industry partners. Teaches courses on robot perception and embodied AI at Stanford.
