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.
Amir is a recognized expert in industry with long lasting contributions.
He was 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).
Contact Email: "amirgh _at_ berkeley . edu".
Recent News
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[11/15/20]: Will be serving as Area Chair for ICML'21. I will try my best to make Reviewer #2 to be fair!
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[09/25/20]: Two papers accepted in NeurIPS'20: HAWQ-V2, and Boundary Thickness and Robustness.
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[06/01/20]: Our paper on Rethinking Batch Normalization has been accepted in ICML 2020.
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[04/21/20]: I will serve as Supercomputing conference chair in Machine Learning track in 2021.
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[02/25/20]: I will give the opening Keynote talk for NSF Workshop on Smart Cyberinfrastructure.
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[02/10/20]: I will give an invited lecture in UC Berkeley's EE 290 course on Efficient Neural Network Training and Inference.
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[02/06/20]: I will give an invited lecture in Stanford's CS 217 course on Precision and Quantized Training for Deep Learning.
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[11/11/19]: Two papers accepted in AAAI'20: Q-BERT, and Inefficiency of K-FAC for large batch size training.
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[09/30/19]: Two papers accepted in NeurIPS'19: ANODEV2 in the main conference, and our work on Trace Weighted Quantization as spotlight in beyond first order methods workshop.
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[09/29/19]: I will be presenting our work on second-order quantization (HAWQ and Q-BERT) in BLISS seminar on October 2nd.
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[08/15/19]: Very excited to participate in AI4ALL,
an annual teaching program for high school students from underrepresented communities to promote diversity and inclusion in AI.
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[05/07/19]: Congratulations to Linjian Ma (now PhD student at UIUC), Jiayu Ye (now at Google), and Gabe Montague (co-founder of Bike and Pedal)
on successfully defending their Masters project.
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[03/21/19]: Will be giving a talk at BSTARS'19.
Many thanks to the Berkeley Statistics department for the invitation.
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[03/01/19]: Our Trust Region paper has been accepted to CVPR'19!
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[02/28/19]: Will be giving a talk in Fifth Annual Industry Day at Simons Institute
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[11/06/18]: Three papers accepted in NeurIPS'18 (one main conference and two workshops)
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[11/01/18]: I will be giving a talk in Stanford CME-510 lecture series
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[03/30/18]: Just learned that my PhD thesis has won UT Austin's 2018 Outstanding Disseration Award.
Thanks George for your great mentorship
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[03/28/18]: We have released SqueezeNext, the smallest neural network designed so far (112x smaller than AlexNet)
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[03/05/18]: Bichen's paper is selected for spotlight in CVPR'18
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[02/26/18]: Selected as a finalist for Robert J. Melosh Medal. Very excited to visit Duke University
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[02/08/18]: Will be giving a lecture in CS267 on GPUs [Watch Here]
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[11/21/17]: Our paper won the Best Student Paper award at SC'17!
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[05/08/17]: Invited to will give a talk at Stanford ICME Rising Stars.