Ahmed Attia

   Ahmed Attia

     The day you start learning is your actual birthday

I moved to Argonne National Lab;
Please Visit my Current WebPage



Affiliations

SAMSI
SAMSI


NC State University
NCSU


VT
VTech


Computer Science Dept. Virginia Tech


Computational Science Lab
Computational Science Lab



Argonne National Lab
Argonne National Lab


Math and Computer Science Dept. Argonne National Lab
Argonne National Lab





Mansoura University
Computational Science Lab





Contact Me




  • Profile

  • Vita

  • Research

  • Publications

  • Software

  • Teaching
  • Conferences
    & Talks

  • Resume

I am a SAMSI postdoctoral research scholar, and a member of the SAMSI Program on Optimization. I am also affiliated with the Math dept at North Carolina State University . My work at SAMSI , and NCSU is mentored by Dr. Alen Alexanderian, and Prof. Ilse Ipsen.

I received my PhD degree Computer Science and Applications from Virginia Tech . This makes me a HOKIE HOKIE

I was a member of data assimilation group in the Computational Science Lab HOKIE
Advised by Prof.Dr. Adrian Sandu, my PhD thesis was mainly about developing efficient and scalable algorithms for solving fully non-linear and non-Gaussian data assimilation problems.


My Research interests include:

  • Data Assimilation
  • Inverse Problems
  • High Performance Computing, and Parallel Programming
  • PDE-constrained optimization
  • Optimal design of experiments for Bayesian nonlinear inverse problems
  • Uncertainty Quantification
  • Uncertainty Reduction
  • Bayesian Inference
  • Data Mining
  • Machine Learning
  • Artificial Neural Networks
  • Computer Vision


In 2016, I started working as a postdoctoral research scholar at SAMSI, the Statistical and Applied Mathematical Science Institute (RTP, NC). I am also affiliated with the Mathematics department at North Carolina State University. In 2016, I obtained my Ph.D. degree, in Computer Science and Applications from Virginia Polytechnic Institute and State University (Virginia Tech), USA. I worked as an intern, in 2014 and in 2015, at Argonne National Laboratory in the Mathematics and Computer Science department. My research interests are generally in the area of computational science and engineering, and specifically in uncertainty quantification in scientific computing. I received my B.S. degree in Mathematics, Statistics and Computer Science in 2004, and my M.S degree in Statistics and Computer Science in 2008, from Mansoura University, Egypt.

Education:



Current Research:

  • New adaptive approaches for covariance inflation and localization in the ensemble Kalman filtering framework.
  • Goal-Oriented Optimal Experimental Design for infinite-dimensional PDE-based inverse problems.
  • Automate parameter tuning of the Hybrid Monte-Carlo filter and smoother via optimization.
  • Algorithms for nonlinear/non-Gaussian filtering and smoothing in imperfect-model settings.

Previous Research:

  • DATeS: OOP-based extensible data assimilation testing suite
  • Cluster sampling filters: fully non-Gaussian filtering methodology with GMM approximation of the prior.
  • Reduced-Order sampling: solving the non-Gaussian data assimilation problem in a reduced-order-model subspace.
  • HMC sampling smoother: a Hybrid Monte-Carlo sampling smoother as alternative to 4DVAR
  • HMC sampling filter: a new Hybrid Monte-Carlo Sampling algorithm as alternative to EnKF


2017

2016

2014/2015

Citations



Data Assimilation and Inverse Problems:

  • DATeS: A highly-extensible data assimilation testing suite. The core of DATeS is implemented in Python to enable for Object-Oriented capabilities. The main functionalities, such as the models, the data assimilation algorithms, the linear algebra solvers, and the time discretization routines are independent of each other, such as to offer maximum flexibility to configure data assimilation applications. DATeS can interface easily with large third-party models written in Fortran or C, and with various external solvers.
          Check DATeS Webpage, or go to the download page.

  • DAPack(PY-DA*): An Extensible Python Package for Data Assimilation.
    Check the documentation here , or here.



  • Download: Resume as pdf.