Omead Pooladzandi is Co-Founder and Co-Head of Research at PrismML, a Pasadena-based AI lab building ultra-dense intelligence for the edge. He earned his Ph.D. in Electrical and Computer Engineering from UCLA, where his research focused on curvature-informed optimization and generative methods for efficient deep learning. His open-source work centers on PSGD, a preconditioned optimizer framework spanning first-order and second-order curvature informed methods used to train models at scale. His research has been published at ICML, NeurIPS, ICLR, and CVPR. At PrismML, he co-leads development of radically compressed architectures — including the lab's open-sourced 1-bit models — that run high-fidelity AI on edge with less memory than your Spotify cache.
