報告題目(Title)：Minimalist Systems for Pervasive Machine Learning

時間(Date & Time)：2023.9.25     10-11am

地點(Location)：理科一号樓1131（燕園校區）Room 1131, Science Building #1 (Yanyuan)

主講人(Speaker)：Fan Lai

邀請人(Host)：Xin Jin

報告摘要(Abstract)：

Although cloud computing has successfully fostered the last leap forward in machine learning (ML), today's ML is becoming increasingly unsustainable. First, the exponential growth in ML resource demand is outpacing the affordable growth in total resource capacity. Even worse, the conventional wisdom of collecting everything into the cloud and then improving ML is becoming infeasible, due to the skyrocketing volumes of edge data and tightening data restrictions (e.g., regulations, user privacy concerns).

This talk demonstrates how we can build a software systems stack that embraces minimalism at its core to overcome these two roadblocks. By co-designing ML, systems, and networking, we can (1) minimize the resource demand of ML by slashing the total amount of system execution needed to achieve the same ML accuracy; and (2) minimize data collection by effectively offloading ML to the planet-scale data source. Finally, I will outline my vision for making both ML and systems highly accessible, efficient, and automated for the upcoming decade.

主講人簡介(Bio)：

Fan Lai is an incoming assistant professor at the University of Illinois Urbana-Champaign and a visiting faculty member at Google. His research brings together machine learning, systems, and computer networking to enable efficient machine learning and data analytics up to the planetary scale. His work appears in venues like OSDI, NSDI, ICML, and ICLR, and has been adopted by Meta, LinkedIn, and Cisco. He was selected as the ML and Systems Rising Star (2023) and has received two awarded papers.

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