ฝัง
- เผยแพร่เมื่อ 21 ต.ค. 2024
- Date: May 20, 2024 - 1:00 PM
Title: Connected Machine Intelligence in Operations
Speaker: Shinan Liu
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, I will talk about my recent endeavors (i.e., ServeFlow and AMIR) on how to effectively solve these challenges, and leverage connected models to make ML practical for networking and network security tasks.
Bio: 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 is the head of the NSF ACTION AI Institute student advisory council and is a recipient of the Daniels Fellowship. He harbors a strong interest in networking, security, and machine learning systems, with his research often focusing on network traffic analysis, 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, SIGMETRICS, CoNext, and UbiComp. Shinan serves ACM IMC and USENIX NSDI as a PC member and a member of the pre-review task force, and he is also a reviewer of USENIX ATC, IEEE INFOCOM, IEEE TDSC, etc. Additionally, his research has been featured in multiple media outlets, including Forbes, The Wall Street Journal, and ACM TechNews.
