Jean Oh is a Researcher at the Robotics Institute of Carnegie Mellon University (CMU) , leading the interdisciplinary Bot Intelligence Group (BIG) . Her work focuses on developing persistent robots that co-exist and collaborate with humans in shared environments, emphasizing continuous improvement through training, exploration, and human interaction. Education: Ph.D. in Language and Information Technologies, CMU M.S. in Computer Science, Columbia University B.S. in Biotechnology, Yonsei University Oh's research integrates vision, language, and planning systems in robotics, with applications in human-robot teaming , self-driving cars , disaster response , eldercare , and creative robotics . She has pioneered projects like socially-compliant robot navigation in human crowds and AI-driven robotic painting systems. Recent publication trends highlight her work in vision-language planning , social navigation , computational creativity , and human-robot collaboration . Notable contributions include the StyleCLIPDraw algorithm for text-to-art generation and Social-PatteRNN for human-like trajectory prediction. Scientific Awards: Best Paper Award in Cognitive Robotics (ICRA'18, ICRA'15) Best Systems Paper Finalist (HRI'25) Best Oral Paper Finalist (Humanoids'24) Best Paper in Entertainment (IROS'24) Argoverse Challenge Winner (CVPR'24) Best Student Paper (AIAA'24) Best Demo Finalist (RoboSoft'24) Oh mentors a diverse team of PhD, MS, and undergraduate students from CMU departments including Robotics, Computer Science, and Mechanical Engineering. Her research is funded by US Army Research Lab , DiDi Chuxing , and DARPA , with collaborations across industry and academia .
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.
Katerina Fragkiadaki is the JPMorgan Chase Associate Professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. She works at the intersection of Artificial Intelligence, Computer Vision, Machine Learning, Language Understanding, and Robotics. PhD from GRASP Lab, University of Pennsylvania Postdoctoral researcher at UC Berkeley (with Jitendra Malik) and Google Research Recipient of NSF CAREER, DARPA Young Investigator, Amazon, Google, Sony, UPMC, and AFOSR awards Organizer of CoRL 2023 Workshop on Generalist Robots ICLR 2024 Program Chair, multiple area chair roles Her research group focuses on developing machines that autonomously improve world models through human-environment interactions, with specific emphasis on: Representation learning and video understanding 2D/3D unified vision-language models Generative simulation and reinforcement learning Real2Sim/Sim2Real robot learning Continual learning and spatial common sense 3D scene reconstruction and dynamics Recent publications highlight advancements in: 3D mesh generation with compositional transformers Unified 2D/3D perception frameworks Physics-aware generative models Diffusion-based robotic manipulation policies Embodied agents with memory prompting Awards include: 2024: DARPA Young Investigator Award 2023: Amazon Faculty Award 2022: Sony Faculty Research Award 2021: UPMC Faculty Research Award 2020: NSF CAREER Award 2019: Google Faculty Award Key collaborations span institutions including UC Berkeley, Google Research, Stanford, MIT, and University of Tsukuba. Her work bridges theoretical innovation with practical applications in: Autonomous robot manipulation 4D world modeling Language-grounded perception Visual dynamics prediction Embodied program synthesis Physics-based simulation engines
Anca Dragan is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where she runs the InterACT Lab focused on algorithms for human-AI and human-robot interaction. Currently on leave from Berkeley, she leads AI Safety and Alignment at Google DeepMind, overseeing safety for Gemini models and preparing for future advancements. Dragan has been a co-PI of the Center for Human-Compatible AI and served on the steering committee for the Berkeley AI Research (BAIR) Lab. B.Sc. in Computer Science from Jacobs University Bremen, Germany Ph.D. from Carnegie Mellon University Dr. Dragan's research focuses on enabling AI agents to work effectively with, around, and in support of people. Her work bridges robotics, machine learning, and game theory to create systems that better understand human preferences and coordinate with users. Key areas include AI alignment (ensuring AI does what people actually want), learning reward functions from diverse human feedback forms, and developing algorithms for human-AI collaboration across domains like autonomous vehicles, brain-machine interfaces, and recommender systems. Her research emphasizes maintaining uncertainty about human preferences and accounting for the plurality of human values. Dr. Dragan's recent publications reveal a strong focus on addressing fundamental challenges in AI safety and alignment. Her work spans theoretical foundations of reward learning, practical implementations for human-AI coordination, and critical examinations of limitations in current approaches. There's a clear trajectory toward more robust, safe, and value-aligned AI systems that can handle complex human preferences while avoiding both present-day harms and potential catastrophic risks. IEEE RAS Early Academic Career Award in Robotics and Automation (2021) McEntyre Award for Excellence in Teaching (2020) PECASE (Presidential Early Career Award for Science and Engineering) (2019) Sloan Fellowship (2018) NSF CAREER Award (2017) Okawa Foundation Award (2017) MIT Tech Review 35 Innovators Under 35 (2017) Multiple best paper awards at top robotics and AI conferences Dr. Dragan has mentored numerous successful students who have gone on to faculty positions at MIT, Stanford, CMU, and Princeton, as well as industry roles at DeepMind, Waymo, and Meta. Her advising philosophy emphasizes both technical rigor and consideration of broader societal impacts. She has secured significant research funding including NSF CAREER, ONR Young Investigator, and Okawa Foundation awards, supporting work on human-AI interaction and alignment. Dragan has also consulted for Waymo for six years, helping develop roadmaps for deploying increasingly learning-based safety-critical systems. Dr. Dragan leads the InterACT Lab at UC Berkeley, which has produced influential work on Cooperative Inverse Reinforcement Learning, Inverse Reward Design, and other foundational concepts in human-AI interaction. The lab's research has significantly shaped the field of AI alignment, with applications spanning autonomous vehicles that coordinate with human drivers, brain-machine interfaces that adapt to user needs, and language models that better understand human preferences. Current work focuses on scaling safety approaches as AI capabilities advance, ensuring alignment keeps pace with technological progress.
Yonatan Bisk is an Assistant Professor at Carnegie Mellon University within the Language Technologies Institute (with courtesy appointment in Robotics Institute). His research bridges Natural Language Processing , Robotics , and Embodied AI , focusing on language grounding, theory of mind, and multimodal interaction. Education : Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Postdoctoral Experience : USC ISI, University of Washington, Allen Institute for AI Industry Appointments : Microsoft Research, Meta AI His research emphasizes embodied language systems and social intelligence in AI . Recent projects include WebArena for autonomous agents, SOTOPIA for social reasoning, and HomeRobot for open-vocabulary manipulation. He leads the REAL Center (Robotics, Embodied AI, and Learning) to foster interdisciplinary collaboration. Key scientific awards include selection for the DARPA ISAT Study Group (2024). He teaches courses like "Talking to Robots" and "Multimodal Machine Learning" while serving as area chair/editor across NLP, Robotics, and ML communities.
Wenhao Ding is a Research Scientist at NVIDIA's Autonomous Vehicle Group, focusing on enhancing the safety and robustness of physical autonomous systems, particularly autonomous vehicles. His research integrates multi-modal large language models, reinforcement learning, and causal discovery to improve model reasoning capabilities. He holds a Ph.D. from Tsinghua University's Department of Electronic Engineering, with a thesis on 'Generative AI for Critical Digital Twins.' Key research interests include safety-critical scenario generation, causal representation learning, and offline reinforcement learning. His work emphasizes closed-loop simulation for autonomous systems and has led to contributions like the SafeBench benchmarking platform and the RealGen scenario generation framework. He has received the 2022 Qualcomm Innovation Fellowship. Notable collaborations include projects with Prof. Marco Pavone at Stanford and internships at Amazon Lab126 (Astro team) and Bosch Center for AI. He actively reviews for top conferences (ICML, NeurIPS, CVPR) and journals (IEEE T-ITS, RA-L). His recent focus on privacy risks in robotics and causal-aware driving models underscores his commitment to trustworthy AI systems. He organizes conferences like the 2024 IEEE International Automated Vehicle Validation Conference and co-hosted the Secure and Safe Autonomous Driving (SSAD) Workshop at CVPR 2023. His interdisciplinary work bridges theory and practice, addressing critical challenges in autonomous systems' safety and generalization.
Tom Mitchell is the Fredkin Professor of AI and Learning and Director of the Center for Automated Learning and Discovery (CALD) at Carnegie Mellon University's School of Computer Science. His research focuses on machine learning, computational neuroscience, and their applications in neuroimaging and natural language processing. He is renowned for pioneering work in developing algorithms to decode brain activity and for contributions to foundational machine learning theory, including co-training and explanation-based learning. Mitchell authored the seminal textbook *Machine Learning* (McGraw Hill, 1997) and has led projects like Never-Ending Learning (NELL), an AI system that autonomously learns from web content. His work bridges computer science and cognitive science, exploring how machines can learn from data and human interaction. Notable research interests include brain-computer interfaces, automated knowledge extraction, and ethical AI. Mitchell's publications span influential journals like *Science* and *Nature*, and he has been recognized for advancing interdisciplinary research in AI and neuroscience. He has advised numerous students and contributed to initiatives like the AAAI Presidential Address on AI and brain sciences. Mitchell's current projects include studying the neural basis of language and developing AI tools for education and healthcare.
Karan Ahuja is the Lisa Wissner-Slivka & Benjamin Slivka Assistant Professor of Computer Science at Northwestern University, directing the Sensing, Perception, Interactive Computing & Experiences (SPICE) Lab. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University (2023) and a B.Tech. in Computer Science (2017). His research focuses on creating technologies that sense and understand human behavior, with applications in mobile health, extended reality, and natural user interfaces. Key projects include LemurDx for ADHD diagnosis, EITPose for wearable hand pose tracking, and MobilePoser for full-body pose estimation via consumer IMUs. Awards include Forbes 30 Under 30 (2024), MIT 35 Innovators Under 35 Asia Pacific, and ACM SIGCHI's Outstanding Dissertation Award. He has worked at Google, Apple, Microsoft Research, Meta Reality Labs, and IBM Research. His lab emphasizes real-world deployments, with technologies licensed and integrated into products used by millions. Prospective students are invited to join his lab at Northwestern via a dedicated application form. Research spans embedded systems, computer vision, and on-device ML, with a focus on impactful applications in healthcare and XR.
Justine Cassell is an SCS Dean's Professor in the School of Computer Science at Carnegie Mellon University, where she also serves as associate dean for technology strategy and impact. She is director emerita of the Human-Computer Interaction Institute (HCII) and co-director of CMU's Simon Initiative and Yahoo-InMind collaboration. Additionally, she is a senior researcher in the ALMAnaCH NLP group at INRIA Paris and holds the founding international chair at the PRAIRIE Paris Institute on Interdisciplinary Research in AI. She maintains courtesy appointments in Psychology, Linguistics, and the Center for the Neural Basis of Cognition. Cassell earned her DEUG in Literature from the Université de Besançon, an M.Litt in Linguistics from the University of Edinburgh, and a dual Ph.D. from the University of Chicago in Psychology and Linguistics. Her academic journey includes founding director roles at the Center for Technology and Social Behavior and the Technology and Social Behavior joint Ph.D. program at Northwestern University, as well as a tenured professorship at the MIT Media Lab where she directed the Gesture and Narrative Language Research Group. Cassell's research focuses on human-human conversation and storytelling, progressively evolving to develop computational systems that can participate in these activities. She is credited with developing Embodied Conversational Agents (ECAs), virtual humans capable of interacting with humans using both language and nonverbal behavior. Her work increasingly addresses the impact of these technologies on learning and communication, particularly through virtual peer systems for children's language and literacy development, and studying online communities' effects on young people's self-esteem and sense of community. Her recent publications demonstrate a clear trajectory toward increasingly sophisticated multimodal interaction systems, with growing emphasis on social bonding, neural synchrony, and the application of conversational AI in educational contexts. The research shows progression from basic conversational agents to complex systems that model social dynamics and support learning through virtual peers, with recent work exploring large language models' role in conversational grounding and the application of multimodal approaches to studying social bonds. Edgerton prize at MIT (2001) Anita Borg Institute Women of Vision award for Leadership (2008) AAAS Fellow (2012) Royal Society of Edinburgh Fellow (2016) ACM Fellow (2016) Henry and Bryna David prize for social science applicable to public policy (2018) Honorary doctorate by the University of Edinburgh (2023) Cassell has been instrumental in developing numerous research initiatives including the Simon Initiative on Technology-Enhanced Learning and the Yahoo InMind Project on the Future of Personal Assistants. Her work has received significant funding for projects investigating virtual peer technology for children with autism, scaffolding science achievement in culturally diverse classrooms, and connection machines studying nonverbal behaviors in conversation. She has advised numerous students and researchers who have gone on to make significant contributions in human-computer interaction, social computing, and educational technology. As director of the HCII and through her current research leadership roles, Cassell has fostered interdisciplinary collaboration across computer science, psychology, linguistics, and education. Her ALMAnaCH research group at INRIA Paris and her continued work at CMU focus on advancing socially interactive agents, with particular attention to their applications in education and their ability to model and support human social interaction. Her leadership extends to the French governmental committee CNNUM (Conseil National du Numérique), where she contributes expertise on the future of digital technology.
Lisa Lee is a Research Scientist at Google DeepMind, focusing on creating AI agents that emulate biological learning and adaptability. She previously taught at Princeton University and received TA awards for Deep Reinforcement Learning and Probabilistic Graphical Models. Education: PhD in Machine Learning from Carnegie Mellon University (advised by Ruslan Salakhutdinov and Eric Xing); A.B. in Mathematics from Princeton University (advised by Sanjeev Arora). Her research centers on AI embodiment, intrinsic motivation, and hierarchical planning. She explores how evolutionary-inspired inductive biases and memory mechanisms can enable agents to generalize across physical and conceptual domains, as demonstrated in her work on robotic agility benchmarks and multimodal transformers. Notable scientific contributions include the Barkour quadruped robot benchmark, Gemini multimodal models, and theoretical work on causal language models. She co-organized key AI workshops at NeurIPS and ICML, and her awards include Princeton's TA of the Year for technical courses. Leadership: ICML Workflow Chair (2019), NeurIPS workshop co-organizer (2019, 2021), peer reviewer for top AI conferences.
Aaron Steinfeld is a Research Professor at Carnegie Mellon University 's Robotics Institute with a courtesy appointment in the Human-Computer Interaction Institute . His work bridges human-robot interaction and advanced transportation , focusing on inclusive design for users with disabilities. PhD in Industrial and Operations Engineering (1999) and MSE/BSE in the same field (1994/1993) from University of Michigan Postdoctoral work in Transportation Human Factors at UC Berkeley's California PATH program (2000) Research spans four key areas: Assistive Robotics : Developing inclusive technologies for visually impaired and disabled users Transportation Innovation : Redesigning transit systems through crowdsourcing and citizen science Human-Robot Group Dynamics : Studying social navigation and multi-robot coordination Trust & Communication : Creating frameworks for robot self-assessment and user trust alignment Recent publications emphasize social navigation benchmarks , pedestrian behavior modeling , and adaptive transit interfaces . Current projects include accessible delivery robots (NIDILRR 90IFDV0042) and FHWA-funded work on autonomous vehicle accessibility. Scientific Recognition NSF AI Institute (AI-CARING) co-PI ONR MURI grant recipient DARPA and multiple NSF grant collaborations Best Paper awards at CHI 2019 and IROS 2020 Advising both PhD and Master's students in human-robot interaction, his lab maintains partnerships with Georgia Tech, UMass Lowell, and Disney Research. He co-edited the book Accessible Public Transportation .
Atieh Taheri is a Presidential Postdoctoral Fellow at Carnegie Mellon University's Human-Computer Interaction Institute (HCII), working with Professors Jeffrey Bigham and Patrick Carrington. She holds a PhD in Electrical and Computer Engineering from UC Santa Barbara, advised by Misha Sra. Her research focuses on accessibility and assistive technologies, particularly in immersive environments like AR/VR, emphasizing participatory design involving disabled users. She has pioneered solutions such as hands-free gaming controllers and tactile feedback systems for individuals with motor impairments. Education: PhD in Electrical & Computer Engineering, UC Santa Barbara (2024) MSc in Electrical & Computer Engineering, UC Santa Barbara (2019) BSc in Computer Engineering, Sharif University of Technology (2011) Research Interests: Accessibility, inclusive design, assistive technologies, AR/VR/XR, user-centered approaches, and human-computer interaction. Her work bridges assistive technology with cutting-edge interfaces, prioritizing holistic experiences that address both functional and sensory needs. Key Contributions: Developed facial expression-based gaming controllers, tactile feedback devices (e.g., MouseClicker), and accessible conversational AI (Virtual Buddy). Her research has been recognized through awards like the UIST 2023 People's Choice Award and CHI 2021 Student Game Competition victory. Professional Experience: Student Researcher at Google (2022-2023) Associate roles at Apple (2016-2017) Software Engineer Intern at Magic Leap (2015) Service & Advocacy: Organized workshops like EC3V (CVPR 2023), served on committees for NeurIPS WiML (2019), and contributed to fundraising for Spinal Muscular Atrophy (SMA) research. She actively promotes accessibility in technology through interdisciplinary collaboration.
Aran Nayebi is an Assistant Professor at Carnegie Mellon University, affiliated with the Machine Learning & Neuroscience Institute. His research focuses on integrating machine learning and neuroscience to understand biological intelligence and develop biologically plausible AI models. He explores embodied agents, neural network dynamics, and human-AI alignment through game-theoretic frameworks. Key research interests include reverse-engineering natural intelligence through embodied agents, developing models that align with brain dynamics (e.g., zebrafish agents, rodent tactile processing), and addressing ethical challenges in AI alignment. His work bridges computational neuroscience and neural network design, emphasizing scalable models and biologically inspired algorithms. Recent publications span topics such as AI-driven economic policy (Universal Basic Income), neuroAI turing tests, and model-brain comparison techniques. His interdisciplinary approach addresses both technical challenges (e.g., neural network scaling) and societal implications of AI.
Jason Li is an Assistant Professor in the Computer Science Department at Carnegie Mellon University. He specializes in robotics and artificial intelligence, focusing on theoretical foundations of algorithms and complexity. His research emphasizes robotic manipulation, simulation frameworks, and perception systems for real-world applications. He teaches advanced courses including 15-750 (Algorithms), 15-754 (Advanced Algorithms), 15-451 (Algorithm Design and Analysis), 15-651 (Algorithms in the Real World), and 15-850 (Special Topics in Robotics) , reflecting his expertise in algorithmic theory and practical implementation. Key research contributions include frameworks like ORBIT for surgical robotics and DexWild for dexterous human-robot interaction. His work bridges simulation and real-world deployment, with a focus on synthetic data (Synthetica) and physically consistent perception models. He currently advises PhD students Henry Fleischmann and George Li. His teaching spans both undergraduate and graduate levels, emphasizing hands-on projects and theoretical rigor.
