Matthew J. Muckley is a Member of the Technical Staff, where he focuses on advancing physical world models, an area central to AMI’s broader mission of building robust, generalizable AI systems.

Based in New York, Muckley brings a deep background in machine learning, computer vision, and scientific computing, with a track record of translating cutting-edge research into scalable, real-world systems.

Prior to joining AMI, Muckley spent over six years at Meta’s Fundamental AI Research (FAIR) organization, where he played a key role in several high-impact initiatives. Notably, he led pretraining and data curation efforts for the V-JEPA 2 vision encoder, contributed to advances in neural compression, and helped organize the influential fastMRI Reconstruction Challenge. His work at Meta and earlier at NYU Langone Health helped shape widely used open datasets and tools—including fastMRI and torchkbnufft—cementing his reputation as a researcher who bridges foundational AI and applied scientific domains.

Muckley holds a Ph.D. in Biomedical Engineering from the University of Michigan, where his research focused on accelerating MRI reconstruction algorithms, alongside master’s degrees in both Biomedical Engineering and Electrical Engineering.

Over the course of his career, he has authored numerous papers in top-tier venues in machine learning and medical imaging, contributed to open-source ecosystems, and earned recognition, including Outstanding Reviewer awards at CVPR and ICML. His arrival at AMI signals a continued investment in technically rigorous, interdisciplinary talent at the frontier of AI research.