Keynotes & Panels
Keynote
From Machine Perception to Human Expertise
About the speaker Kristen Grauman
Kristen Grauman is a Professor in the Department of Computer Science at the University of Texas at Austin and Co-founder and CEO of VIZO Labs. Previously, she was a Research Director at Meta FAIR. Her research in computer vision and machine learning focuses on video understanding and embodied perception. She recently led the Ego4D and Ego-Exo4D initiatives, large-scale efforts advancing first-person and ego-exo video understanding. She received her Ph.D. from MIT and is a Fellow of AAAS, IEEE, and AAAI. Her honors include the 2026 Hill Prize in AI, the 2025 PAMI Huang Prize, a Sloan Fellowship, the IJCAI Computers and Thought Award, and multiple best paper and test-of-time awards in computer vision. She has served as Associate Editor-in-Chief for PAMI and Program Chair for CVPR, NeurIPS, and ICCV.
Abstract
Computer vision has made remarkable progress in understanding the visual world. Yet these advances have focused on expanding the capabilities of the machine as an observer. The next frontier is to move from AI that observes human activity to AI that enables people to build their own expertise.
Imagine learning a new skill---whether physical, creative, or practical---with an AI guide that has learned from vast amounts of video, understands precisely what you are doing, anticipates the effects of your actions, and most importantly, can identify what you should change to improve. Such an AI guide would meet the learner where they are, offering guidance tailored to their abilities and goals.
To realize this vision, we need advances in two fundamental areas: fine-grained understanding of human activity and the generation of personalized, actionable guidance. Toward that end, I will present our recent progress on models that ground skilled activity in the body and the 3D world to anticipate outcomes and plan toward goals, as well as models that translate this understanding into concrete language and visual feedback. I will also show how these ideas make visual instruction more accessible, including AI guides on smartglasses that transform how-to videos into proactive guidance for blind and low-vision learners. Together, these advances bring us closer to AI that does more than make us more productive—it empowers us to learn and improve.
Panel
The Age of LLMs: Who Is the Researcher Now? How LLMs Are Changing Search, Ideation, and Scientific Judgment
About the speakers
Anna Rogers is a tenured Associate Professor in the Data Science Section at the IT University of Copenhagen, affiliated with the NLPNorth research group. Her work focuses on interpretability and robustness of NLP applications based on Large Language Models, as well as their sociotechnical impacts. She is currently an editor-in-chief of ACL Rolling Review, the peer review platform for all major NLP conferences. She is also one of the chief scientists of the National Centre for Artificial Intelligence in Society (CAISA).
Roberta Sinatra is Professor of Computational Social Science at the University of Copenhagen (KU) and at the IT University of Copenhagen (ITU), and External Faculty at the Complexity Science Hub in Austria. She co-founded the NEtwoRks, Data, and Society (NERDS) research group at ITU, which she led from 2019 to 2022, and is co-lead of the Pioneer Centre for AI in Copenhagen. Her research sits at the intersection of network science, data science, and computational social science. She has received several awards, including the Complex Systems Society Junior Prize, the DPG Young Scientist Award for Socio- and Econophysics, a Villum Young Investigator grant, and an ERC Consolidator Grant.
Isabelle Augenstein is a Professor of Computer Science at the University of Copenhagen, where she co-leads the Danish Pioneer Centre for Artificial Intelligence. Her work on explainability, factuality, and bias in NLP has been recognized with an ERC Starting Grant, the Karen Spärck Jones Award, and election to the Royal Danish Academy of Sciences and Letters.
Abstract
LLMs are changing how researchers move from the existing literature to new scientific questions. Beyond finding and summarizing papers, these systems can suggest connections across fields, identify possible gaps, propose hypotheses, support experiments, and critique manuscripts. As they move from retrieving information to influencing research directions and evaluation, it becomes increasingly important to ask where assistance ends, and intellectual contribution begins, and which aspects of originality, responsibility, and scientific judgment must remain human.
Keynote
Yann LeCun — World Models: Enabling the next AI revolution
About the speaker Yann LeCun
Yann LeCun is the Executive Chairman of AMI Labs and the Jacob T. Schwartz Professor at NYU affiliated with the Courant Institute - School of Mathematics, Computer Science, and Data Science. He was the Chief AI Scientist at Meta (2018-2025), the founding Director of Facebook AI Research (2013-2017) and of the NYU Center for Data Science (2011-2014). He received an EE Diploma from ESIEE (Paris) in 1983, and a PhD in Computer Science from Sorbonne Université (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU in 2003 after a short tenure as a fellow of the NEC Research Institute. In late 2013, LeCun became Director of AI Research at Facebook, while remaining on the NYU Faculty part-time. He was visiting professor at Collège de France in 2016. His research interests include machine learning and artificial intelligence, with applications to computer vision, natural language understanding, robotics, and computational neuroscience. He is best known for his work in deep learning and the invention of the convolutional network method which is widely used for image, video and speech recognition. He is a member of the US National Academy of Sciences, National Academy of Engineering, and the French Académie des Sciences, a Chevalier de la Légion d’Honneur, a fellow of AAAI and AAAS, the recipient of the 2025 Queen Elizabeth Prize for Engineering, the 2024 VinFuture Grand Prize, 2022 Princess of Asturias Award, the 2014 IEEE Neural Network Pioneer Award, the 2015 IEEE Pattern Analysis and Machine Intelligence Distinguished Researcher Award, the 2016 Lovie Award for Lifetime Achievement, the University of Pennsylvania Pender Award, and honorary doctorates from IPN, Mexico, EPFL, Université Côte d’Azur, HKUST, and Université de Genève. He is the recipient of the 2018 ACM Turing Award (with Geoffrey Hinton and Yoshua Bengio).
Abstract
Keynote
From Visual Recognition to Embodied AI
About the speaker Jamie Shotton
Jamie Shotton is a leader in AI research and development, with a track record of incubating transformative new technologies and experiences from early-stage research to shipping product. He is Chief Scientist at Wayve where he leads Wayve Labs, building foundation models for embodied intelligence, such as GAIA and LINGO, to enable safe and adaptable autonomous vehicles and other robots. Prior to Wayve he was Partner Director of Science at Microsoft and head of the Mixed Reality & AI Labs where he shipped foundational features including body tracking for Kinect and the hand- and eye-tracking for HoloLens. He has explored both fundamental research and applications of AI across autonomous driving, robotics, mixed reality, virtual presence, human-computer interaction, gaming, robotics, and healthcare. He has received multiple Best Paper and Best Demo awards at top-tier academic conferences, and the Longuet-Higgins Prize test-of-time award at CVPR 2021. His work on Kinect was awarded the Royal Academy of Engineering’s gold medal MacRobert Award in 2011, and he shares Microsoft’s Outstanding Technical Achievement Award for 2012 with the Kinect engineering team. In 2014 he received the PAMI Young Researcher Award, and in 2015 the MIT Technology Review Innovator Under 35 Award. He was awarded the Royal Academy of Engineering’s Silver Medal in 2020 and elected a Fellow of the Royal Academy of Engineering in 2021.
Abstract
Twenty years ago, at ECCV 2006, the challenge that captivated me was teaching machines to recognize objects in the visual world. Since then, computer vision has undergone an extraordinary transformation.
Through a journey from early visual recognition, through Kinect and HoloLens, to embodied AI at Wayve, this talk will reflect on some of the technical ideas and personal lessons behind that transformation: learning rather than engineering; betting on data and compute scaling; and being careful not to confuse today’s constraints with tomorrow’s limits.
Today, the opportunity is much bigger: building systems that can understand enough of the physical world to act intelligently within it. I’ll look at autonomous driving as an early proving ground for embodied AI, and at some of the open questions on the path toward more general embodied intelligence. If the past twenty years are any guide, the boundaries of what’s possible can move faster than we expect.
Panel
Can AI Build New Knowledge?
About the speakers
Dima Damen is a Professor of Computer Vision at the University of Bristol and Senior Research Scientist at Google DeepMind. Dima is currently an EPSRC Fellow (2020-2026), focusing her research interests in the automatic understanding of object interactions, actions and activities using wearable visual (and depth) sensors. She is best known for her leading works in Egocentric Vision, and has also contributed to novel research questions including mono-to-3D, video object segmentation, assessing action completion, domain adaptation, skill/expertise determination from video sequences, discovering task-relevant objects, dual-domain and dual-time learning as well as multi-modal fusion using vision, audio and language. She is the project lead for EPIC-KITCHENS, the seminal dataset in egocentric vision, with accompanying open challenges and follow-up works: EPIC-Sounds, VISOR and EPIC Fields, as well as the recent HD-EPIC. She is part of the large-scale consortium effort Ego4D and Ego-Exo4D. She is an ELLIS Fellow, associate editor (AE) of IJCV, and was a program chair for ICCV 2021 and Associate Editor-in-Chief (AEIC) of IEEE TPAMI (2023-2025). She is frequently a Senior Area Chair and Area Chair in major conferences and was selected as Outstanding SAC in ECCV 2024 and Outstanding Reviewer in CVPR2021, CVPR2020, ICCV2017, CVPR2013 and CVPR2012. Dima received her PhD from the University of Leeds (2009), joined the University of Bristol as a Postdoctoral Researcher (2010-2012), Assistant Professor (2013-2018), Associate Professor (2018-2021) and was appointed as chair in August 2021. She supervises 2 postdoctoral researchers, 8 PhD students and 2 Visiting PhD students. At the University of Bristol, Dima leads the Machine Learning and Computer Vision (MaVi) lab (https://uob-mavi.github.io/). At Google DeepMind, Dima is part of the Vision team, led by Andrew Zisserman, focusing on video understanding research. Her latest contribution is to the CVPR 2025 paper: Learning from Streaming Video with Orthogonal Gradients (https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Learning_from_Streaming_Video_with_Orthogonal_Gradients_CVPR_2025_paper.pdf)
Yilun Du is an Assistant Professor at Harvard in the Kempner Institute and Computer Science. He received his PhD from MIT EECS, advised by Leslie Kaelbling, Tomas Lozano-Perez, and Joshua B. Tenenbaum. He also holds a bachelor's degree from MIT, was a research fellow at OpenAI and a senior research scientist at Google DeepMind. His research focuses on developing intelligent embodied agents in the physical world through generative AI, decision making, and robot learning.
Viorica Patraucean is a Research Scientist in Google DeepMind, interested in efficient multimodal world models and their evaluation. She is the lead organiser of the annual Perception Test challenge, which tracks progress in multimodal video understanding (at the fourth edition this year at ECCV). She is also the co-founder of the Eastern European Machine Learning summer school, which brings together students and ML experts from all over the world in an Eastern European country, to raise the visibility of this region in the community.
Abstract
Modern AI systems are remarkably good at using knowledge acquired during large-scale training. But it is still unclear whether they can genuinely build new knowledge from experience: identify what they do not know, acquire the right evidence, form new abstractions, revise their understanding of the world, validate what they have learned, and retain it for future use. One way to view current AI progress is as a human-machine continual-improvement loop. A model is trained; humans inspect its failures; researchers design new datasets, benchmarks, architectures, losses, memory mechanisms, or post-training methods; the model is retrained and evaluated; and the cycle repeats.
In this sense, AI systems are improving continually, but much of the knowledge-building still happens outside the model, through human diagnosis and design. The question for next-generation AI is whether more of this loop can be internalized. Can future systems identify their own knowledge gaps, decide what evidence they need,
learn from video or interaction, store useful memories and exceptions, validate new knowledge, and update themselves without losing previous capabilities?
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