Chang Xu
Chang Xu is a Senior Researcher in the Machine Learning Group at Microsoft Research Asia. She works on fundamental machine learning algorithms, multi-modal learning, and Large Language Models, with a focus on foundation models, reasoning, and agentic systems. She has published extensively at top-tier conferences including ICLR, ICML, NeurIPS, and KDD, and applies her research to real-world domains such as AI in finance, healthcare, and storage systems.
“M13's Carter Reum and Basis Set Ventures' Chang Xu argue the opposite — the second and third-order effects are where the real money will be made, and they will be harder to identify.”
Source→“This paper solves one of the most pressing deployment problems in physical AI: VLA models fail at exactly the wrong moments — complex, ambiguous situations — and this framework cuts those failures in half without requiring retraining or sacrificing real-time control speeds.”
Source→“Rather than running expensive multi-candidate evaluation on every timestep (the approach that kills competing methods), VLA-ATTC uses a lightweight uncertainty detector: generate two action candidates, measure their divergence using Dynamic Time Warping (DTW), and only trigger deep deliberation when they disagree.”
Source→“The combination of Chang Xu and Shan You suggests affiliation with a major Chinese AI lab or university. Their focus on plug-and-play inference augmentation without base model modification is a pragmatic, deployment-oriented research direction.”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.