Speakers

Sanja Fidler is vice president of AI research at NVIDIA, leading the company’s Spatial Intelligence Lab research lab in Toronto. She is also an associate professor at the University of Toronto, and an affiliate faculty member at the Vector Institute, which she co-founded. Previously, she was a research assistant professor at Toyota Technological Institute at Chicago, a philanthropically endowed academic institute located in the University of Chicago campus. Fidler co-authored over 130 scientific papers in the fields of computer vision, machine learning and NLP. She has served as Area Chair for a variety of conferences, including the Conference on Computer Vision and Pattern Recognition (CVPR), International Conference in Computer Vision (ICCV), the Conference on Empirical Methods in Natural Language Processing (EMNLP), the International Conference on Learning Representations (ICLR), the Conference on Neural Information Processing Systems (NeurIPS), and SIGGRAPH.

Jia Deng is a Professor of Computer Science at Princeton University. His research focuses on computer vision and machine learning. He received his Ph.D. from Princeton University and his B.Eng. from Tsinghua University, both in computer science. He is a recipient of the Sloan Research Fellowship, the NSF CAREER award, the ONR Young Investigator award, an ICCV Marr Prize, a CVPR test-of-time award and two ECCV Best Paper Awards. 

James Hays is a professor in the School of Interactive Computing at the College of Computing, Georgia Institute of Technology. His research interests span computer vision, robotics, and machine learning. He works on problems related to recognition, synthesis, and manipulation. His research often involves finding new data sources to exploit (e.g. geotagged imagery) or creating new data sets where none existed (e.g. sketches or grasps). Before joining Georgia Tech, he was the Manning Assistant Professor of Computer Science at Brown University. He was a postdoc at MIT with Antonio Torralba, completed his Ph.D. at Carnegie Mellon University with Alexei Efros, and received his B.S. from Georgia Tech. He is the recipient of the Alfred P. Sloan Fellowship, the NSF CAREER award, the PAMI Mark Everingham Prize, and the ECCV Koenderink Prize.

Francesco Ferroni is a principal research scientist and senior manager in Cosmos Lab at NVIDIA. He has worked on Cosmos1,2,3 versions, with a particular focus on data curation, generator training and post-training, and infrastructure and evaluation. He has a background in autonomous driving (at ArgoAI) and computational physics (DPhil). Prior to NVIDIA, he was a software engineering manager at ArgoAI, where he led multiple teams working on computer vision and machine learning for autonomous vehicles, primarily in the context of onboard perception and mapping/localization. He enjoys being at the interface of large-scale engineering projects and cutting edge academic research. He has a track record of delivering innovative solutions to machine learning problems, either embedded in robots or in the cloud.

Nadine Chang is a senior research scientist at NVIDIA Spatial Intelligence Lab. She obtained her PhD under Martial Hebert and Michael Tarr at CMU, where she focused on long-tail learning and vision language model applications. She was the recipient of the NSF Graduate Research Fellowship in 2020. Her current interests include multimodal language models (MLLMs) for data selection and pruning, pursuit of hallucination free and robust MLLMs, and overall perception tasks. She has organized several workshops and tutorials in top-tier conferences, including Autonomous Driving (CVPR 2025), workshop on Exploring the Next Generation of Data (CVPR 2025), and Continuous Data Cycle via Foundation Models tutorial (CVPR 2025).

Bryan Wilder is an Assistant Professor in the Machine Learning Department at Carnegie Mellon University. He studies the foundations of machine learning in social, policy, and healthcare settings, blending new methodology with field evaluations to improve AI’s societal impact. His work is shaped by collaborations with governments, nonprofits, health systems, and other partners. At CMU, he directs the Lab for AI and Social Impact. The research has been funded by Schmidt Sciences, NSF, NIH, CDC, the Engler Family Foundation, and ARO. He completed his PhD in Computer Science at Harvard University. Before joining CMU, he was a postdoctoral Schmidt Science Fellow at the Harvard School of Public Health. He serves as Chair of the Board of Directors for EAAMO and the associated ACM EAAMO conference.

Despoina Paschalidou is a Senior Research Scientist at NVIDIA’s Toronto AI Lab, based in Santa Clara. Her research lies at the intersection of computer vision, graphics, and spatial intelligence, with a focus on scene understanding across 3D and video. Her work spans 3D reconstruction, generative models for objects, scenes, and videos, compositional 3D representations, and perception for embodied systems, with the broader goal of building systems that can understand, organize, and reason about the visual world at scale. She was previously a Postdoctoral Researcher at Stanford University with Leonidas Guibas, and received her PhD from the Max Planck ETH Center for Learning Systems, advised by Andreas Geiger and Luc Van Gool. During her PhD, she also spent time at NVIDIA Research and Facebook AI Research. She received her undergraduate degree in Electrical and Computer Engineering from Aristotle University of Thessaloniki in Greece.