Building Efficient and Scalable Machine Learning Systems by Dr. Qinghao Hu

Abstract

The rapid evolution of foundation models is increasingly bottlenecked by a widening gap between algorithmic demands and system efficiency. As model scale and context lengths explode, infrastructure efficiency plateaus. Addressing these challenges requires a full‑stack rethinking of machine learning systems. This talk presents a research framework centered on algorithm–system co‑design to improve efficiency across the ML lifecycle, covering hyperparameter exploration, post‑training reinforcement learning, scalable vision–language models, and future system support for agentic models at scale.

Biography

Dr. Qinghao Hu is a Postdoctoral Associate at the Massachusetts Institute of Technology, advised by Professor Song Han. His research focuses on efficient machine learning systems, including datacenter scheduling, distributed training, reinforcement learning, and model serving. He is a recipient of multiple awards, including the ASPLOS Distinguished Paper Award, WAIC Best Paper Award, Google Ph.D. Fellowship, and the ML and Systems Rising Stars Award. He obtained his PhD from Nanyang Technological University and was previously a visiting scholar at ETH Zürich.