Summary
Dacheng Li is a PhD student at UC Berkeley specializing in machine learning and distributed systems with eight years of research and engineering experience across academia and startups. His work—mentored by Ion Stoica and Joseph Gonzalez—focuses on scaling long-context LLM training and human-evaluated LLM benchmarks, with publications including DISTFLASHATTN (COLM 2024), Chatbot Arena (ICML 2024), and NeurIPS 2023 analyses. Previously he contributed to MPC-friendly transformer inference and automated model-parallel strategy search (ICLR/NIPS), and co-founded an AI-driven housing recommender, blending systems thinking with product impact. Trained at Carnegie Mellon (MS, Machine Learning) and UCSD (BS in CS & Math), he combines deep theoretical understanding with practical system-building for large-scale ML workloads. An interesting through-line: he repeatedly tackles privacy, efficiency, and real-world evaluation together—bringing human preference signals into systems designed for training and inference at scale.
8 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of California, Berkeley
Bachelor's degree, Computer Science, Mathematics, Bachelor's degree, Computer Science, Mathematics at 美国加州大学圣迭戈分校
Master's degree, Machine Learning, Master's degree, Machine Learning at 美国卡内基梅隆大学
Master of Science - MS, Machine Learning, Master of Science - MS, Machine Learning at Carnegie Mellon University