Speakers

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.

Tsung-Yi Lin is a principal research scientist at NVIDIA Research. He was previously at Google Research, Brain Team. He works on computer vision and machine learning. He did his PhD at Cornell University and Cornell Tech, where he was advised by Serge Belongie. He did his masters at University California, San Diego and his bachelors at National Taiwan University. He received the Best Student Paper Award for Focal Loss at ICCV 2017. He led the creation of the COCO dataset which received the PAMI Mark Everingham Prize at ICCV 2023 and Koenderink Prize at ECCV 2024.

Ishan Misra is a Director, Research Scientist in the TBD Labs research division at Meta's SuperIntelligence group. He works on computer vision and machine learning research specifically in generative AI and representation learning. Previously he was at the GenAI group at Meta where he led the research efforts on video generation models. He was the tech lead for Meta's Movie Gen project for foundation models in video generation, video editing, video personalization, and audio generation. Prior to GenAI, He worked at FAIR in Meta on self-supervised learning in computer vision and multimodal learning. He got his PhD at Carnegie Mellon University.

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.

Susan Zhang is a Principal Research Engineer at DeepMind, where she focuses on distributed systems and large-scale machine learning infrastructure. With over a decade of experience architecting high-performance compute systems, she has played a pivotal role in some of the most ambitious AI projects of the last decade. Before joining DeepMind, she was a core technical leader at Meta AI, where she helped build the infrastructure and data pipelines behind the LLaMA family of large language models. Her expertise spans end-to-end system design for massive compute clusters, including data curation, automatic evaluation, and deep learning frameworks for reinforcement learning, language, and vision models at scale. At Meta, she led a team of around 20 research engineers responsible for LLM data curation, evaluation pipelines, interactive demo platforms, and cluster reliability tooling, shaping the roadmap for high-impact generative AI research. At DeepMind, she continues to push the boundaries of scalable AI systems, advancing the foundations that underpin frontier models like Gemini and beyond.