Shuaiwen Song

Shuaiwen Song is an Associate Professor in the School of Computer Science at The University of Sydney. His research interests include high performance computing, performance evaluation, concurrent/parallel systems and technologies, mobile computing, graph, social and multimedia data, deep learning, distributed computing and systems software, and machine learning. He directs the Future System Architecture Lab (FSA) at the university. His work focuses on system software and hardware, breaking down abstraction barriers and rethinking the hardware-software interface. He has published in top HPC and computer architecture conferences and has received various awards and recognitions for his contributions to scalable computing and system design.

Shuaiwen Leon Song is a VP of Research at Together.ai and a tenured professor with expertise in Large-Scale ML System Design, High Performance Computing, and Frontier Technologies. His research focuses on optimizing large-scale machine learning systems, particularly in the areas of GPU interconnects, memory management, and energy efficiency. Song's work includes developing frameworks for efficient deep learning training and inference, such as Flash-LLM and Deepspeed-Ulysses. He also explores the performance and power characteristics of modern GPU architectures and HPC systems, contributing to the design of energy-efficient and high-performance computing solutions.