Postdoctoral Scholar, Stanford University
- Date & time
- Thursday, February 27, 2025 · 11:00 AM
- Location
- Donald Bren Hall 4011
Abstract
Current success in artificial intelligence relies heavily on internet-scale data with unified representations. However, such large-scale homogeneous data is not readily available for spatial computing applications involving 3D geometry. In this talk, I will present approaches to building spatial intelligence systems with limited 3D data by combining existing mathematical models into existing machine learning pipelines. I will share my work applying these approaches to develop data-driven methods that can synthesize and analyze 3D geometry. Finally, I will discuss future opportunities and challenges of data-efficient spatial intelligence.
About the speaker
Guandao Yang is a postdoctoral scholar at Stanford, where he works with Professor Leonidas Guibas and Professor Gordon Wetzstein. His research lies in the intersection of computer graphics, computer vision, and machine learning. He completed his Ph.D. at Cornell in 2023, advised by Professor Serge Belongie and Professor Bharath Hariharan. During his Ph.D., Guandao had experience collaborating with various industry research labs, including NVIDIA, Intel, and Google. He received his bachelor's degree from Cornell University in Ithaca, majoring in Mathematics and Computer Science.