Machine Learning Systems

Ziwu Liu

M.S. Student, Computer Science

edge computing Federated learning Machine Learning Systems resilient computing

My passion for the evolving landscape of technology has driven me to focus on the intricate interplay between resilient computing, federated learning, edge computing, and machine learning systems. My research aims to seamlessly integrate edge computing with federated learning to create resilient and adaptive machine learning systems that can operate effectively even in resource-constrained settings.

Tongzhou Gu

M.S. Student, Computer Science

Machine Learning Systems High Performance Computing

Tongzhou is a MS/Ph.D. Student in the Software-Defined Advanced Networked and Distributed Systems (SANDS) Lab in KAUST, advised by Prof. Marco Canini. He obtained his bachelor’s degree from the Southern University of Science and Technology in 2022. During the time of his undergraduate study, he was an SDE intern at AWS Shanghai AI Lab and a research intern at SANDS Lab. Tongzhou’s research interests lie in High-Performance Networking and Computing as well as Distributed Machine Learning Systems. Currently, he is working on accelerating large-scale ML training with comprehensive approaches