About Me

I am Liangyu Wang, a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), specializing in efficient training and inference for large language models (LLMs) through distributed computing and advanced GPU programming. Before that, I completed my master degree at The Chinese University of Hong Kong, focusing on multimodal machine learning. Currently, I am conducting LLM pretraining research at the Alibaba Qwen Team. My research interests include optimizing distributed training and inference of LLMs, improving multi-threaded and multi-stream scheduling, and enhancing privacy-preserving methods for LLMs. I have interned as a LLM Pretraining Engineer at Aramco, working with large-scale GPU clusters to boost training throughput and model scalability. Currently, I am working on:

• Efficient reinforcement learning (RL) for LLMs reasoning
• Distributed training and inference of LLMs
• Efficient algorithm and infrastructure design for LLMs
• Efficient privacy-preserving methods