Weaving Intelligence into Network Operations
academic talk @ FIT 3-225, FIT 3-225, Tsinghua University, Beijing
Speaker: 刘诗楠（香港大学）
Date:
Abstract:
Modern computer networks generate extensive amounts of data that can benefit network research, management, and security. This data is fast-evolving, increasingly encrypted, and highly siloed, which makes it difficult to analyze using traditional methods based on predefined rules and signatures. Machine learning (ML) methods have shown promise in identifying complex patterns and insights in network data. Yet, these methods often face reliability issues in real-world network operations. In this talk, I will focus on multiple practical challenges unique to integrating data-driven approaches in networking: (1) the need to acquire diverse traffic patterns siloed in different network entities, (2) the need for scalable platforms that support real-time decision-making for high-throughput data flows. By addressing these challenges, new opportunities arise for collaboration across multiple network entities and for performing data inference at tens of Gbps on general-purpose hardware. I will also discuss how overcoming these challenges can pave the way for a future that empowers all stakeholders—model developers, network operators, and network service users—to interpret and manage network interactions with greater reliability and transparency.
Bio:
刘诗楠，香港大学数据与系统工程系的长聘轨助理教授、博士生导师，他领导了网络智能系统与安全（NAISS）实验室。他在芝加哥大学计算机科学系获得博士学位，师从Nick Feamster教授。他曾领导美国国家科学基金会ACTION人工智能研究所学生顾问委员会，并获得机器学习与系统领域新星奖、CBI奖学金、ACTION人工智能奖学金以及Daniels奖学金。他的研究专注于计算机网络与安全领域，重点开发易于使用、可靠且高性能的机器学习系统用于网络数据分析，并运用网络数据分析解决安全与隐私领域的核心问题。其研究成果已在USENIX Security、NSDI、SIGMETRICS、CoNext及UbiComp等顶级会议和期刊上发表。他担任ACM IMC和USENIX NSDI的程序委员会委员及预审工作组成员，同时是NeurIPS、USENIX ATC、IEEE INFOCOM、IEEE TDSC、IEEE TIFS、IEEE IoTJ等期刊和会议的审稿人。此外，他的研究成果曾被福布斯、华尔街日报和ACM TechNews等多家媒体报道。