The Harvard Systems Group at the John A. Paulson School of Engineering and Applied Sciences (SEAS) operates at the vital intersection of systems, theory, and hardware. We design and build the architectures, networks, and databases that form the backbone of modern computing.
From scaling global cloud infrastructure to optimizing the software-hardware interface, we don’t just study systems—we reinvent the layers that power the future.
Survival curves are the standard framework for modeling time-to-event outcomes in clinical biostatistics, yet they exist only at the population level, never as a per-instance label a model could learn to reproduce. Emma will ask whether frontier LLMs can nonetheless generate valid, personal survival curves zero-shot, benchmarking four models on emergency department revisit and hospital readmission prediction.
eventlocation_onSEC 2.122 & 2.123personZishen Wan (Harvard University)
As AI evolves from individual models into dynamic, autonomous systems, it poses new challenges for computer architecture while also becoming capable of joining the design process itself. Zishen will present work toward AI-native computing systems from two directions: cross-layer co-design for physical and neuro-symbolic AI, and AI agents that reason about, evaluate, and design computer architectures.
eventlocation_onSEC 2.122 & 2.123personAkira van de Groenendaal (Carnegie Mellon University)
Akira will present two recent projects on serving bursty, prefix-heavy LLM inference workloads: one showing how bursty arrivals can improve a cluster’s time-per-output-token and what that implies for request routing, and another on tuning the private vs. shared split of a distributed KV cache to optimize time-to-first-token—together demonstrating that intuitive systems choices can leave performance gains on the table.
Smart cache eviction algorithms can adapt to workloads, but they often pay for it with complexity, instability, or overhead. Learning-Augmented Heuristics takes a different approach: keep the fast heuristic on the data path, and use learning to configure it at a slower timescale.
Bursty arrivals usually cause a drop in performance and are seen as headaches in production systems. In this blog, we investigate a phenomenon where burstiness actually improves performance, uncovering what conditions produce this effect.
Cache eviction is usually presented as a ranking problem. LRU ranks objects by
recency. LFU ranks them by frequency. More advanced algorithms combine several
signals, adapt their parameters, and maintain increasingly sophisticated data
structures to decide which object is least valuable.
Most LLM servers treat a deployed model as fixed: its weights have one
precision, its KV cache has one memory budget, and both remain unchanged until
the process restarts.
