Summary
Shibo Hao is a PhD candidate in Data Science at UC San Diego and a Member of Technical Staff focused on NLP and ML, with seven years of experience bridging symbolic knowledge and neural language models. He explores making models more robust, interpretable, and practically helpful by combining knowledge graphs and latent-space reasoning — work that recently contributed to a Meta internship and a paper on training LLMs to reason in continuous latent spaces. Trained at Peking University, he brings strong theoretical grounding alongside hands-on research experience mentoring by leaders like Yuandong Tian and Jason Weston. Based in San Diego, he is especially interested in approaches that make model behavior more explainable and human-centered, often integrating symbolic structure into neural representations.
7 years of coding experience
1 year of employment as a software developer
University of California, San Diego
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Peking University
Beijing No.4 High School