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About

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

  • [11/15/20]: Will be serving as Area Chair for ICML'21. I will try my best to make Reviewer #2 to be fair!
  • [09/25/20]: Two papers accepted in NeurIPS'20: HAWQ-V2, and Boundary Thickness and Robustness.
  • [06/01/20]: Our paper on Rethinking Batch Normalization has been accepted in ICML 2020.
  • [04/21/20]: I will serve as Supercomputing conference chair in Machine Learning track in 2021.
  • [02/25/20]: I will give the opening Keynote talk for NSF Workshop on Smart Cyberinfrastructure.
  • [02/10/20]: I will give an invited lecture in UC Berkeley's EE 290 course on Efficient Neural Network Training and Inference.
  • [02/06/20]: I will give an invited lecture in Stanford's CS 217 course on Precision and Quantized Training for Deep Learning.
  • [11/11/19]: Two papers accepted in AAAI'20: Q-BERT, and Inefficiency of K-FAC for large batch size training.
  • [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.
  • [09/29/19]: I will be presenting our work on second-order quantization (HAWQ and Q-BERT) in BLISS seminar on October 2nd.
  • [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.
  • [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.
  • [03/21/19]: Will be giving a talk at BSTARS'19. Many thanks to the Berkeley Statistics department for the invitation.
  • [03/01/19]: Our Trust Region paper has been accepted to CVPR'19!
  • [02/28/19]: Will be giving a talk in Fifth Annual Industry Day at Simons Institute
  • [11/06/18]: Three papers accepted in NeurIPS'18 (one main conference and two workshops)
  • [11/01/18]: I will be giving a talk in Stanford CME-510 lecture series
  • [03/30/18]: Just learned that my PhD thesis has won UT Austin's 2018 Outstanding Disseration Award. Thanks George for your great mentorship
  • [03/28/18]: We have released SqueezeNext, the smallest neural network designed so far (112x smaller than AlexNet)
  • [03/05/18]: Bichen's paper is selected for spotlight in CVPR'18
  • [02/26/18]: Selected as a finalist for Robert J. Melosh Medal. Very excited to visit Duke University
  • [02/08/18]: Will be giving a lecture in CS267 on GPUs [Watch Here]
  • [11/21/17]: Our paper won the Best Student Paper award at SC'17!
  • [05/08/17]: Invited to will give a talk at Stanford ICME Rising Stars.

Students

Over the years, I have been fortunate to have the opportunity to work with and mentor the following talented students.

Current Students:

  • Zizheng Tai: Masters student at UC Berkeley
  • Shixing Yu: Vising undergraduate from PKU
  • Yujie Wang: Vising undergraduate from PKU

Alumni (Gone but not forgotten):

  • Daiyaan Arfeen: Undergraduate (Now PhD Student at CMU)
  • Norman Mu: Undergraduate (Now PhD Student at Berkeley)
  • Zach Zheng: Masters student (Now at Apple)
  • Eric Tan: Masters student (Now at Google)
  • Naijing Zhang: Masters student (Now at Google Youtube)
  • Yifan Bai: Masters student (Now at Amazon)
  • Tianmu Lei: Masters student (Now at Google)
  • Xinran Rui: Masters student (Now at TensTorrent)
  • Sarvagya Singh: Masters student (Now at Forward Health)
  • Hanbing Zhan: Masters student (Now at ByteDance)
  • Xing Jin: Masters student (Now at Volume Hedge Fund)
  • Vyom Kavishwar: Masters student (Now at Hive)
  • Sheng Shen: Masters student (Now PhD Student at Berkeley)
  • Jiayu Ye: Masters student (Now at Google)
  • Gabe Montague: Masters student (Co-founder of "Park and Pedal")
  • Linjian Ma: Masters student (Now PhD student at UIUC)
  • Varun Shenoy: Highu school student (Now undergrad at Stanford)


Publications

Papers



Workshops

Selected Talks

  • Keynote in NSF Cyberinfrastructure workshop, Feb., 2020,
    An Integrated Approach for Efficient Neural Network Design, Training, and Inference.


  • UC Berkeley, RiseLab Retreat, Jan. 2020,
    HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks.


  • UC Berkeley, BLISS Seminar, Oct. 2019,
    Systematic Quantization of Neural Networks Through Second-Order Information.


  • Facebook, AI Systems Faculty Summit, Sep. 2019,
    Efficient Neural Networks through Systematic Quantization.


  • BSTARS'19, Berkeley Statistics Department, Mar. 2019,
    Neural Networks Through the Lens of the Hessian.


  • Berkeley Simons Institute, 5th Annual Industry Day, Feb. 2019,
    ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs.


  • Simons Randomized Numerical Linear Algebra and Applications Workshop, Sep. 2018,
    Large Scale Stochastic Training of Neural Networks.


  • Simons Data Science Finale Workshop, Dec. 2018,
    Towards Robust Second-order Training of Neural Networks.


  • Simons Weekly Optimization Reading Group, Oct. 2018,
    Second order optimization for convex and non-convex problems.


  • NERSC Data Seminar, Dec. 2018,
    Beyond SGD: Robust Optimization and Second-Order Information for Large-Scale Training of Neural Networks .


  • Stanford, CME 510: Linear Algebra and Optimization Seminar, Nov. 2018,
    Large-scale training of Neural Networks .


  • UCSF Radiology Department, Oct. 2018 ,
    A Domain Adaptation framework for Neural Network Based Medical Image Segmentation.


  • Intel AI Meeting, Oct. 2018,
    Autonomous Driving Challenges in Computer Vision Research.


  • Facebook AI Research, Sep. 2018,
    Challenges for Distributed Training of Neural Networks.


  • Microsoft Research, Aug. 2018,
    Large Scale Training of Neural Networks .


  • Berkeley Scientific Computing and Matrix Computations Seminar, Sep. 2017,
    A Framework for Scalable Biophysics-based Image Analysis .


  • Stanford, ICME Star Talk Series, 2017,
    Fast algorithms for inverse problems with parabolic pde constraints with application to biophysics-based image analysis,


  • SIAM Minisymposium on Imaging Sciences, Albuquerque, NM, USA, 2016,
    On preconditioning Newton method for PDE constrained optimization problems.


  • 13th U.S. National Congress on Computational Mechanics, San Diego, CA, USA, 2015,
    Challenges for exascale scalability of elliptic solvers using a model Poisson solver and comparing state-of-the art methods.


  • SIAM CSE Minisymposium, Salt Lake, Utah, USA, 2015,
    Parameter estimation for malignant brain tumors.


  • 12th U.S. National Congress on Computational Mechanics, Raleigh, NC, USA, 2013,
    A numerical algorithm for biophysically-constrained parameter estimation for tumor modeling and data assimilation with medical images.


  • SIAM Annual Meeting, San Diego, CA, USA, 2013,
    Image-driven inverse problem for estimating initial distribution of brain tumor modeled by advection-diffusion-reaction equation.




Patents

  • Dynamic directional rounding,
    A. Fit-Florea, A. Gholami, B. Ginsburg, and P. Davoodi.
    Approved by Nvidia Patent Office (US patent pending), 2018.


  • Tensor processing using low precision format,
    B. Ginsburg, S. Nikolaev, A. Kiswani, H. Wu, A. Gholami, S. Kierat, M. Houston, and A. Fit-Flores.
    United States patent application US 15/624,577. 2017 Dec 28.


  • High performance inplace transpose operations,
    A. Gholami and B. Natarajan,
    United States patent US 10,067,911, 2018.


Copyright © Amir Gholami 2014-2020