Summary
Xueshen Liu is a PhD student in Computer Science at the University of Michigan with seven years of research and engineering experience spanning distributed and parallel computing, ML infrastructure, and LLM efficiency. He has combined academic rigor with industry practice through Student Researcher roles at Google focused on cost-efficient RL for large language models on hybrid TPU/GPU cloud systems. His earlier work includes QoE-driven MAC-layer resource scheduling, edge service migration using reinforcement learning, and hardware-aware sensing for battery diagnostics, reflecting a strong systems-to-application breadth. Trained across SJTU, UMich, and TU Berlin, he blends electrical and computer engineering foundations with data-science perspectives. Notably, he works on making LLM training and inference more resource- and cost-efficient—translating cutting-edge systems research into practical cloud workflows. Based in Ann Arbor, he thrives at the intersection of distributed systems and ML infrastructure, optimizing performance under real-world constraints.
6 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Michigan
Other, Data Science, Other, Data Science at Technische Universität Berlin
Bachelor’s Degree, Electric & Computer Engineering, Bachelor’s Degree, Electric & Computer Engineering at Shanghai Jiao Tong University