BU Computer Systems Seminar

Speaker: Shinan Liu, PhD Candidate, University of Chicago

Talk Title: “Operational ML in Networks for Digital Well-being”

Abstract: The landscape of computer networking has witnessed a rapid expansion, with machine learning (ML) models playing an increasingly vital role in network management, such as service recognition, home network automation, and activity recognition. Operationalizing ML in networking, unlike traditional ML pipelines, presents unique challenges attributable to the specific traits of network data. Factors such as a lack of data sources, the presence of diverse forms of concept drift, and the system's need to accommodate substantial traffic volumes, all contribute to these complexities. In this talk, I will talk about my recent endeavors on how to effectively solve these challenges, and make ML practical for digital well-being tasks using network information.

Bio: Shinan Liu is a final year Ph.D. student in the Computer Science Department at the University of Chicago, where he is advised by Prof. Nick Feamster. He earned his Master of Science degree within his Ph.D. program at UChicago in the Summer of 2022 and is a recipient of the Daniels Fellowship. Before his current academic pursuits, he served as the CEO of Chengdu Dominity Co., Ltd., and has gained valuable experience interning at LangSafe.ai, FedML Inc, Virginia Tech, Qihoo 360, Microsoft Research Asia (short-term visit), KnowWhy, and Tsinghua University’s Network Security Lab.

He harbors a strong interest in network systems, security, interpretable AI, and measurement, with his research often focusing on cellular networks, the Internet of Things, and cyber-physical systems. His work has been recognized and published in top conferences and journals such as USENIX Security, UbiComp, SIGMETRICS, and CoNext. Additionally, his research has been featured in multiple media outlets, including Forbes, The Wall Street Journal, and ACM TechNews, showcasing its relevance and impact on both the academic community and the general public.