Towards Trustworthy Learning-Enabled Networked Systems

Abstract: Machine learning (ML) is revolutionizing the systems landscape, fundamentally transforming how we design, manage, and optimize networked systems and applications. Learning-based solutions now outperform manually designed ones across a wide range of environments. However, system practitioners, from cloud service architects to network operators, often hesitate to adopt learning-based solutions in production environments since they are difficult to interpret, debug, and trust.

In this talk, I argue that explainability is key to building trustworthy learning-enabled systems, bridging the gap between operator intent and model behavior. Toward this goal, I will present two explainability solutions tailored to networked systems. First, I will present Agua, a framework that explains a model’s decisions using high-level, human-understandable concepts (e.g., “volatile network conditions”). I will showcase several practical use cases of Agua in networking environments, including debugging unintended behaviors, identifying distribution shifts, devising concept-based strategies for efficient retraining, and augmenting environment-specific datasets. Second, I will introduce CrystalBox, a framework that explains a controller’s behavior in terms of the future impact on key network performance metrics. I will demonstrate its utility in two practical use cases: network observability and guided reward design. Together, these tools lay the foundation for intelligent networked systems where operators can intuitively design, debug, and adapt learning-based systems.

Bio: Sangeetha Abdu Jyothi is an Assistant Professor at the University of California, Irvine, and an Amazon Scholar. Her research interests lie at the intersection of networking, systems, and machine learning. She received her Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign in 2019. Her work has been recognized with several awards, including the NSF CAREER Award (2025), ACM CoNEXT Best Paper Award (2024), and the IETF/IRTF Applied Networking Research Prize (2022). In 2022, she was named an N2Women Rising Star in Networking and Communications.

Website: https://www.ics.uci.edu/~sabdujyo/