Abstract

A computational approach to human perception has sparked breakthroughs in AI, human-computer interaction, and social cognition. In this talk, I will introduce attention-driven resource allocation, a transformative paradigm that improve perceived quality for human users (e.g., video streaming) as well as emerging AI applications (e.g., real-time video analytics). In contrast to today’s network systems that optimize handcrafted performance metrics, we advocate that network systems should align resource allocation with user attention. The insight is that video viewers or web users rarely perceive application quality by consciously measuring metrics such as video rebuffering time or web page load time; instead, they react the visual process of video rendering or page loading. Similarly, computer vision applications, though highly data-/compute-intensive, do not require high quality data everywhere or all the time. This talk will use a few case studies from our recent work to present early attempts and promises of attention-driven systems.

Bio: Junchen Jiang is an Assistant Professor of Computer Science at the University of Chicago. He received his PhD degree from Carnegie Mellon University in 2017 and his bachelor’s degree from Tsinghua Univ in 2011. His research interests are networked systems and their intersections with machine learning. He is a recipient of Google Faculty Research Award in 2019, best Paper Award at the Symposium of Edge Computing in 2020, and CMU Computer Science Doctoral Dissertation Award in 2017.