Cade Gordon is completing his master’s degree in electrical engineering and computer science at UC Berkeley after completing his bachelor’s degree in two and a half years. Gordon will begin his doctoral research at Stanford University in 2026.
With four first-author publications, Gordon’s research at the Berkeley Artificial Intelligence Research Lab focuses on improving the utility of protein language models for biological design using techniques from robust statistics and mechanistic interpretability. His other research interests include using large language models to conduct autonomous science and multimodal learning.
Throughout his research career, Gordon has focused on democratizing and advancing machine learning for real-world scientific problems. His work on LAION-5B, which scaled model training from just eight GPUs to thousands and created one of the most widely used datasets in vision-language understanding, earned an Outstanding Paper Award at NeurIPS 2022. Building on this foundation in large-scale machine learning, he has applied these techniques to critical problems in drug discovery — developing methods for engineering safer antibody therapeutics at BigHat Biosciences and creating autonomous protein engineering systems at FutureHouse. His contributions to both foundational AI research and its biomedical applications have earned him recognition as a Siebel Scholar and Accel Scholar.
Raised in the Chicago suburbs, Gordon maintains a deep appreciation for the city’s pioneering house music scene. When he’s not in the lab, he can be found on the soccer field, exploring hiking trails or immersed in biographies.
