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About

Amir Gholami is a postdoctoral research fellow in BAIR Lab. He received his PhD 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, Gold Medal in the ACM Student Research Competition, as well as best student paper finalist in SC’14. His current research includes different aspects of Systems for Machine Learning (SysML), and large scale training of Neural Networks (resume).

Contact Email: "amirgh _at_ berkeley . edu".


Recent News

  • 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 desgined 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!




Publications

Papers



Workshops

Talks

  • 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.


  • A novel high performance inplace transpose algorithm,
    A. Gholami and B. Natarajan,
    US Patent Pending, 2017.


  • Pool boiling cooling system.,
    A. Gholami, R. Hosseini, M. Nabil, and M. H. Samadinia.
    Iran Industrial Property Office, 68033, 2010.


Copyright © Amir Gholami 2014-2020