Title: Scalable and intelligent learning systems

Advisor: Carlos Guestrin

Supervisory Committee: Carlos Guestrin (Chair), Marina Meila-Predoviciu (GSR, Stats), Arvind Krishnamurthy, and Luis Ceze

Abstract: Data, models, and computing are the three pillars that enable machine learning to solve real-world problems at scale. Making progress on these three domains requires not only disruptive algorithmic advances but also systems innovations that can continue to squeeze more efficiency out of modern hardware. Learning systems are in the center of every intelligent application nowadays. 

I will present our perspective on elements of current and future learning systems. I will first touch on how to build learning systems that are accessible and scalable. Then I will talk about how to enable intelligent learning systems that adapts to the hardware environments automatically with learning. Finally, I will discuss how to generalize our approach to do full-stack optimization of the model, system, hardware jointly, and how to build systems to support life-long evolution of intelligent applications.

 

Place: 
CSE2 387 (Gates Center)
When: 
Tuesday, July 30, 2019 - 10:00 to Thursday, March 28, 2024 - 03:01