1st Workshop on Multimodalities for 3D Scenes

Xiaolong Wang is an Assistant Professor in the ECE department at the University of California, San Diego. He received his Ph.D. in Robotics at Carnegie Mellon University. His postdoctoral training was at the University of California, Berkeley. His research focuses on the intersection between computer vision and robotics. His specific interest lies in learning 3D and dynamics representations from videos and physical robotic interaction data. These comprehensive representations are utilized to facilitate the learning of human-like robot skills, with the goal of generalizing the robot to interact effectively with a wide range of objects and environments in the real physical world. He is the recipient of the NSF CAREER Award, Intel Rising Star Faculty Award, and Research Awards from Sony, Amazon, Adobe, and Cisco.

Andrew Owens Andrew Owens is an assistant professor at The University of Michigan in the department of Electrical Engineering and Computer Science. Prior to that, he was a postdoctoral scholar at UC Berkeley. He received a Ph.D. in Electrical Engineering and Computer Science from MIT in 2016. He is a recipient of an NSF CAREER Award, a Computer Vision and Pattern Recognition (CVPR) Best Paper Honorable Mention Award, and a Microsoft Research Ph.D. Fellowship.

Katerina Fragkiadaki is the JPMorgan Chase Associate Professor in the Machine Learning Department in Carnegie Mellon University. She received her undergraduate diploma from Electrical and Computer Engineering in the National Technical University of Athens. She received her Ph.D. from University of Pennsylvania and was a postdoctoral fellow in UC Berkeley and Google research after that. Her work focuses on combining forms of common sense reasoning, such as spatial understanding and 3D scene understanding, with deep visuomotor learning. The goal of her work is to enable few-shot learning and continual learning for perception, action and language grounding. Her group develops methods for computer vision for mobile agents, 2D and 3D visual parsing, 2D-to-3D perception, vision-language grounding, learning of object dynamics, navigation and manipulation policies. Pioneering innovations of her group’s research include 2D-to-3D geometry-aware neural networks for 3D understanding from 2D video streams, analogy-forming networks for memory-augmented few-shot visual parsing, and language-grounding in 2D and 3D scenes with bottom-up and top-down attention. Her work has been awarded with a best Ph.D. thesis award, an NSF CAREER award, AFOSR Young Investigator award, a DARPA Young Investigator award, Google, TRI, Amazon, UPMC and Sony faculty research awards. She is a program chair for ICLR 2024.

Linda Smith is a cognitive scientist recognized for her work on early object name learning as a form of statistical learning. Smith co-discovered infants' few-shot learning of object names, showed that few-shot learning is itself learned, and documented the relevant experiences. Smith was born in and grew up in Portsmouth, New Hampshire. She graduated from the University of Wisconsin (Madison) with a Bachelor of Science degree in experimental psychology and from the University of Pennsylvania with a Ph.D. in experimental psychology. She joined the faculty of Indiana University (Bloomington) in 1977 and is in the Department of Psychological and Brain Sciences and the Program in Cognitive Science. She won the David E. Rumelhart Prize for theoretical contributions to cognitive science and is a member of both the National Academy of Sciences and the American Academy of Arts and Science.