Two CSE PhD students named Machine Learning and Systems Rising Stars

Two PhD students in computer science and engineering, Fan Lai and Jiachen Liu, have been selected to join the 2023 Machine Learning and Systems Rising Stars cohort, an initiative sponsored by MLCommons. Lai and Liu will join a select group of just 35 current and recently graduated PhD students specializing in machine learning and systems to “develop community, foster research and career growth, [and] enable collaborations” across academia and industry. 

MLCommons is a community made up of over 70 supporting organizations, including researchers at elite universities as well as established corporations and startups in the software, cloud computing, and semiconductor spheres. The network was launched in 2020, growing out of the 2018 MLPerf benchmarks, which “established industry-standard metrics to measure machine learning performance and quickly grew to encompass data sets and best practices.”

The goal of MLCommons is to advance the field by democratizing emerging technologies in machine learning by improving the accessibility and portability of these techniques. The group’s metrics and best practices have since been broadly adopted, and its Rising Stars program plays a key role in identifying and supporting early-career researchers who are working to actively advance these goals.

Lai and Liu, both in Professor Mosharaf Chowdhury’s lab in CSE, have quickly established themselves as valuable contributors to MLCommons’ objectives and to the broader machine learning discipline. 

Lai’s research focuses on the development of systems support across the software stack to enable fast, distributed computing, particularly for cloud computing and machine learning purposes. His research has been broadly adopted in the open-source community, including at Meta and LinkedIn. In early 2023, he was awarded the Richard F. and Eleanor A. Towner Prize for Outstanding PhD Research by the College of Engineering in recognition of his research achievements.

Liu’s research interests center around machine learning resource management. Her research targets the optimization of various resources, including training data, data center resources, and edge resources. This focus serves to maximize machine learning efficiency and redefine current capabilities in the field. Additionally, as DEI chair of the CSE graduate student organization, she is committed to fostering an inclusive environment in academia and advocating for equity in education and research.

As part of the Machine Learning and Systems Rising Star cohort, Lai and Liu will play a key role in driving progress in machine learning as well as supporting collaboration and community-building in this area.