"People who wish to analyze nature without using mathematics must settle for a reduced understanding." Richard Feynman
Amir Gholami is a research scientist in RiseLab and BAIR at UC Berkeley. 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,
best student paper finalist in SC’14, as well as Amazon Machine Learning Research Award in 2020.
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, AutoML, and scalable training of Neural Network models
Contact Email: "amirgh _at_ berkeley . edu".
There is an internship opportunity for research in the area of Efficient Machine Learning (the position requires the student to be enrolled in UC Berkeley). Please email me your CV if you are interested
with the subject of "Efficient ML Internship Application".
Trace weighted Hessian-aware quantization,
Z. Dong, Z. Yao, D. Arfeen, Y. Cai, A. Gholami, M. Mahoney, and K. Keutzer, Spotlight at NuerIPS'19 workshop on Beyond First-Order Optimization Methods in Machine Learning, 2019.
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.
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.