Synopsis: For most of its existence, Visual Computing has been primarily focused on algorithms, with data treated largely as an afterthought. Only recently, with the advances in AI, did our field start to truly appreciate the singularly crucial role played by data, but even now we might still be underestimating it. In this talk, I will begin with some historical examples illustrating the importance of large visual data for both human and computer vision. I will then share some of our recent work demonstrating the power of very simple algorithms when used with the right data, including visual in-context learning and visual data attribution.

• Alexei (Alyosha) Efros is a professor of Electrical Engineering and Computer Science at UC Berkeley. His research is in the area of visual computing and machine learning. He is particularly interested in using data-driven techniques to tackle problems where large quantities of unlabeled visual data are readily available.
• Efros received his B.S. (summa cum laude) in Computer Science from the University of Utah in 1997, and PhD from UC Berkeley in 2003. He did a post-doc at the University of Oxford and then spent a decade on the faculty of Carnegie Mellon University before joining Berkeley.
• He is a recipient of CVPR Best Paper Award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), SIGGRAPH Significant New Researcher Award (2010), three PAMI Helmholtz Test-of-Time Prizes (1999,2003,2005), the ACM Prize in Computing (2016), Diane McEntyre Award for Excel- lence in Teaching Computer Science (2019), Jim and Donna Gray Award for Excellence in Undergraduate Teaching of Computer Science (2023), and PAMI Thomas S. Huang Memorial Prize (2023).

There will be a private reception and dinner (for guests 21 and older) after the lecture for industry partners and faculty. $600 for tables of 8; and $100 per individual ticket. Please contact Sheri Carp at sheri@cs.utah.edu to secure your reservation.