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About

I am a PostDoctoral research fellow in ASPIRE Lab at UC Berkeley. I received my PhD from the Institute for Computational Engineering and Sciences (ICES) at UT Austin. I am interested in machine learning algorithms for image segmentation and object detection, High Performance Computing (HPC), parallel algorithms, and optimization problems( resume).



You can contact me at "amir __at__ accfft _dot_ org".






Aerospace Engineering

Bachelor of science in Aerospace engineering from Tehran Polytechnic (Amirkabir University).

Mechanical Engineering

Master of science in Mechanical engineering from The University of Texas at Austin.

Computational Science

PhD in Computational Science and Engineering Mathematics program at UT Austin.

Research

Parallel Image Registration

Image registration is a process in which a mapping from a reference image to a target image is sought. It is key in many different applications ranging from medical imaging to machine learning. We have develoepd a state-of-the-art parallel registration solver that has been scaled up to 8,192 cores, and have been able to solve a record 3D image registration problem with 200 billion unknowns in less than 4 minutes. The code that we have developed is based on AccFFT along with a novel parallel high-order interpolation kernel. The result of this work will appear in SC'17( best student paper finalist [pdf]).

Accelerated FFT Library

Accelerated FFT (AccFFT) is a new parallel FFT library for computing distributed Fast Fourier Transforms on GPU and CPU architectures. The library has been designed with the goal of achieving maximum performance, without making the user interface complicated. AccFFT supports parallel FFTs distributed with slab or pencil decomposition for both CPU and GPU architectures. The library's scalability has been tested upto 131K CPU cores, and upto 4K GPUs [pdf].





Novel Stokes Solver using FMM

Stokes equation is one of the most important equations derived from Navier-Stokes. Numerical solutions and discretization of the Stokes equation is challenging. For instance, one cannot use arbitrary discretization spaces for velocity and pressure. Moreover, it is an elliptic but indefinite problem, which further complicates the construction of fast linear algebraic solvers and preconditioners, especially for problems with highly variable coefficients or high-order discretizations. We are using a novel adaptive fast multipole method (pvfmm), which uses an integral formulation scheme that can circumvent most of the difficulties with the Stokes equation. Compared to finite element methods, our formulation decouples the velocity and pressure, and generates fields that are by construction divergence free [pdf].

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Massively Parallel Poisson Solvers

The need for large scale parallel solvers for elliptic partial differential equations (PDES) pervades across a spectrum of problems with resolution requirements that cannot be accommodated on current systems. Poisson solvers must scale to trillions of unknowns. Example of methods that scale well are the FFT (based on spectral discretizations), the Fast Multipole Method, and multigrid methods (for stencil-based discretizations). We have benchmarked these methods and compared their parallel efficiency as well as the corresponding cost per unknowns for different test cases. FFT is tested with p3dfft, FMM with pvfmm, AMG with ML package, and GMG with an in house code [pdf].





Brain Tumor Inverse Problem

Gliomas are tumors that arise from Glial cells in the brain. They account for 29% of all brain and central nervous system (CNS) tumors, and 80% of all malignant tumors out of about 60,000 cases diagnosed each year in the United States. Despite advances in surgery, chemo/radio therapy, the median survival rate of high grade Gliomas has remained about one year in the past 30 years. One of the key parameters in increasing the survival rate of patients is how well the tumor invasion boundaries are detectable. With the current imaging technologies only the bulk of the tumor abnormalities, can be detected, and the infiltrated tumor cells get masked. I am trying to approximate the extent of tumor infiltration by coupling the imaging data with tumor growth dynamics [pdf].

Publications

Papers

  • A. Gholami, A. Mang, K. Scheufele, C. Davatzikos, M. Mehl, and G. Biros. A framework for scalable biophysics-based image analysis Proceedings of ACM/IEEE SuperComputing Conference (SC'17), 2017 (Best Student Paper Finalist) [pdf].

  • A. Mang, A. Gholami, C. Davatzikos, and G. Biros. PDE constrained optimization in medical image analysis Optimization and Engineering (submitted), 2017

  • A. Mang, A. Gholami, and G. Biros. Distributed-memory large-deformation diffeomorphic 3D image registration Proceedings of ACM/IEEE SuperComputing Conference (SC16), 2016 [pdf].

  • A. Gholami, J. Hill, D. Malhotra, and G. Biros. AccFFT: A library for distributed-memory FFT on CPU and GPU architectures. (submitted), 2015 [pdf].

  • D. Malhotra, A. Gholami, and G. Biros. A volume integral equation Stokes solver for problems with variable coefficients. Proceedings of ACM/IEEE SuperComputing Conference (SC14), 2014 (Best Student Paper Finalist) [pdf].

  • A. Gholami, A. Mang, and G. Biros. An inverse problem formulation for parameter estimation of a reaction–diffusion model of low grade gliomas. Journal of mathematical biology, Vol. 72, pp 409-433, 2015. [pdf].

  • A. Gholami, D. Malhotra, H. Sundar and G. Biros. FFT, FMM, or Multigrid? A comparative Study of State-Of-the-Art Poisson Solvers for Uniform and Nonuniform Grids in the Unit Cube. SIAM Journal on Scientific Computing, Vol. 38 (3), 2016 [pdf].


Talks

  • A. Gholami and G. Biros. On preconditioning Newton method for PDE constrained optimization problems. Minisymposium at SIAM Conference on Imaging Sciences, Albuquerque, NM, USA, 2016.

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

  • A. Gholami and G. Biros. Parameter estimation for malignant brain tumors. Minisymposium at SIAM CSE, Salt Lake, Utah, USA, 2015.

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

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


Posters

  • A. Gholami and G. Biros. AccFFT: A New Parallel FFT Library for CPU and GPU Architectures Poster at ACM/IEEE SuperComputing Conference (SC15), Austin, TX, 2015

  • A. Gholami and G. Biros. Inverse problem method for parameter estimation of a reaction-diffusion model of low grade gliomas Poster at 13th U.S. National Congress on Computational Mechanics, San Diego, CA, USA, 2015

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

  • A. Gholami and G. Biros. Image-driven inverse algorithms for brain tumor modeling and diagnosis. ASME Congress and Exposition, IMECE2012,Houston, USA, 2012.



  • A. Gholami and G. Biros. Fast algorithms for inverse problems of reaction-diffusion-advection equations SIAM Annual Meeting, Minneapolis, USA, 2012.


Patents

  • A. Gholami and B. Natarajan. A novel high performance inplace transpose algorithm. Submitted to AMD Patent Office, 2015.

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