Saurabh will examine both sides of AI’s effect on the reliability and security of computing systems—a foe when it complicates reasoning about probabilistic programs and enables phishing at scale, a friend when it powers causal root-cause diagnosis and automatic vulnerability patching—and highlight recent work from the NSF Center Chorus on streaming ML with probabilistic accuracy guarantees and secure decentralized ML.

As ML training scales across racks, data-center buildings, regions, and even federated clients, periodic synchronization leaves compute idle and prolongs iterations. Minghao will argue for network-aware co-design—jointly optimizing algorithms, parallelism and pipeline strategies, and heterogeneity handling—and present THC, ScaleAcross Explorer, and FIELDING as works that cut synchronization costs across increasingly large deployments.

Many workloads, such as repeated scans and LSM-tree compaction, access groups of objects in similar orders, forming access loops that several cache eviction algorithms handle well—though it remains unclear what makes them effective. Yunjia will distill the structural features behind this efficiency into portable eviction gadgets that can be plugged into existing policies, and show that these features also improve cache stability by reducing miss-ratio anomalies and cliffs.