Amir Gholaminejad

Amir Gholami is a post-doctoral research fellow at UC Berkeley. He received his Ph.D. from UT Austin, working on large scale 3D bio-physics based image segmentation, a research topic which received UT Austin’s best doctoral dissertation award in 2018. He is a Melosh Medal finalist, recipient of best student paper award in SC’17 (Supercomputing Conference), Gold Medal in the ACM Student Research Competition, as well as best student paper finalist in SC’14. His current research includes quantized Neural Networks, Neural Ordinary Differential Equations, large scale training, and stochastic second-order methods.


ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning

PyHessian: Neural networks through the lens of the Hessian

Hessian-based Analysis of Large Batch Training and Robustness to Adversaries

Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization

Blog Posts

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