"People who wish to analyze nature without using mathematics must settle for a reduced understanding." Richard Feynman
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Amir Gholami is a senior research fellow at ICSI and Berkeley AI Research (BAIR). He
received his PhD from UT Austin, working on large scale 3D image
segmentation, a research topic which received UT Austin’s best
doctoral dissertation award in 2018. He is a Melosh Medal
finalist, the recipient of best student paper award in SC'17,
Gold Medal in the ACM Student Research Competition, as well as
best student paper finalist in SC’14.
He was also part of the Nvidia team that for the first time made low precision neural network training
possible (FP16), enabling more than 10x increase in compute power through tensor cores.
That technology has been widely adopted in GPUs today.
Amir's current research focuses on efficient AI at the Edge, and scalable training of Neural Network models
(resume).