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"Operationalizing Machine Learning for Networks"
by Shinan Liu, University of Chicago.
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, QoE measurement, activity recognition, and intrusion detection. 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, Shinan will talk about his recent endeavors (i.e., NetDiffusion and ServeFlow) on how to effectively solve these challenges and make ML practical for digital well-being tasks using network information.
About the Speaker
Shinan Liu is a final year Ph.D. candidate 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 the Ph.D. program at UChicago in 2022 and is a recipient of the Daniels Fellowship. He harbors a strong interest in networked systems, security, interpretable AI, and measurement, with his research often focusing on network traffic analysis, cellular networks, the Internet of Things, and cyber-physical systems.
Before coming to the University of Chicago, Shinan was the CEO of a start-up company called Dominity Security Co., Ltd. He is a research consultant at LangSafe.ai, and he also had internships at FedML, Virginia Tech, Qihoo 360, Microsoft Research Asia (short-term visit), KnowWhy, and Tsinghua NISL.
Shinan's research work has been recognized and published in top conferences and journals such as USENIX Security, SIGMETRICS, CoNext, and UbiComp. Additionally, his research has been featured in multiple media outlets, including Forbes, The Wall Street Journal, and Forbes.
Date and Time: Tuesday, April 16 · 4:00 – 5:00 pm (GMT+00:00) United Kingdom Time
Location: Online (Pre-Registration is Required to Obtain the Meeting Link)
Event Registration Link: https://forms.gle/rwwWdiB3njcCcNPU7
Online Registration Ends April 14 at 04:00 PM (GMT+00:00) United Kingdom Time
Recording
will be available after May, 6th 2024 based on the speaker's request
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