Max Zuo is a PhD candidate at Brown University specializing in weakly supervised learning, with 11 years of engineering and research experience bridging computer vision, embodied AI, and wearable sensing. He has interned twice at Google building state-of-the-art vision models and contributed to TensorFlow Vision work such as a ViLD reimplementation and k-Means Transformer presentations. His graduate research at Georgia Tech combined graph neural networks for semantic scene rearrangement and an HMM-based weak supervision approach to label recovery for wearable event understanding. Comfortable moving projects from ideation to production, he has built applied systems for indoor localization, one-shot detection, and developer tools across startups and enterprise internships. Max holds a BS and MS in Computer Science (4.0) from Georgia Tech and brings a blend of rigorous academic training and practical ML engineering that often focuses on making learning work under limited or noisy supervision.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Brown University
Master's degree, Computer Science - ML, 4.0, Master's degree, Computer Science - ML, 4.0 at Georgia Institute of Technology
Code repository for our Junior Design class at the Georgia Institute of Technology Fall 2019/Spring 2020. Fanuel Abiy, Robert Freeman, Kyser Montalvo, Evi Salguero, Mitchell Stasko, and Max Zuo.
Contributions:74 pushes, 6 branches, 5 comments in 5 months
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