Dr. Ranjay Krishna is an Assistant Professor at the Paul G. Allen School of Computer Science & Engineering. His research lies at the intersection of computer vision and human computer interaction. This research has received best paper, outstanding paper, and orals at CVPR, ACL, CSCW, NeurIPS, UIST, and ECCV, and has been reported by Science, Forbes, the Wall Street Journal, and PBS NOVA. His research has been supported by Google, Amazon, Cisco, Toyota Research Institute, NSF, ONR, and Yahoo. He holds a bachelor's degree in Electrical & Computer Engineering and in Computer Science from Cornell University, a master's degree in Computer Science from Stanford University and a Ph.D. in Computer Science from Stanford University. His recent works cover instruction tuning for addressing complex visual tasks with low computational budget.

Dr. Eric Schulz is an incoming professor at LMU and the director of the Institute of Human Centered AI at Helmholtz Munich. He finished PhD at UCL in 2017 working on generalization and exploration in reinforcement learning. From 2017 to 2019, he was a Data Science Postdoctoral Fellow at Harvard University, where he worked on computational models of learning and decision making and from 2020 to 2023 he was a Max Planck Independent Group Leader at the MPI for Biological Cybernetics. His recent studies on LLMs will bring valuable insights to our workshop from a cognitive perspective.

Dr. Judy Hoffman is assistant Professor in the School of Interactive Computing at Georgia Tech and a member of the Machine Learning Center. Research interests include computer vision, machine learning, domain adaptation, robustness, and fairness. Prior to joining Georgia Tech, Dr. Hoffman was a Visiting Research Scientist at Facebook AI Research and a postdoctoral scholar at Stanford University and UC Berkeley. She received her PhD from UC Berkeley, EECS in 2016 where she was a member of BAIR and BDD. Her recent works focus on FOMO efficiency, e.g. Token Merging (ToMe) and Binary Vision Transformers (BiViT) to reduce the computational burden.

Sergey Tulyakov Sergey is a Principal Research Scientist heading the Creative Vision team at Snap Research. His work focuses on creating methods for manipulating the world via computer vision and machine learning. This includes 2D and 3D methods for photorealistic object manipulation and animation, video synthesis, prediction and retargeting. His work has been published as 30+ top conference papers, journals and patents resulting in multiple tech transfers, including Snapchat Pet Tracking and Real-time Neural Lenses (gender swap, baby face, real-time try-on and many others). His recent work address efficient generation and personalization in the visual domain.