Nabil Chaabane

Applied scientist, PhD.

I received my engineering degree from the higher school of communication, Tunisia in 2009, where I worked on image processing and remote sensing applications. In a joint project, I was employed by INRS, Quebec to study the desertification of southern Tunisia. In 2015, I received my Ph.D from Virginia Tech. My doctoral research dealt with the study of droplets deformation under different matrix flows. My primary research interests are the development and analysis of numerical methods applied to fluid dynamics problems modeled by the means of partial differential equations. In 2015, I joined the team led by Dr. Riviere as a postdoctoral fellow. My current research deals with the study of the coupling of flow and geo-mechanics and high performance computing implementation.

Research interests

  1. High performance computing.
  2. Discontinuous Galerkin method.
  3. Coupling of geo-mechanics and flow.
  4. Computational fluid dynamics.
  5. Reservoir simulation.
  6. Finite element method.


  • Ph.D in Mathematics, Virginia Tech
  • M.S in Mathematics, Virginia Tech
  • Engineering degree in Telecommunication, Higher School of Communication, Tunisia
  • Publications

    Neural networks

    Convolutional neural networks have been proven to be an excellent tool by the image processing and object recognition community. One of my interests is to implement convolutional neural networks to detect key facial points. A large set of images is used to train the network. In a more rigorous mathematical setting, an optimization problem is solved to determine the parameters of the network. This will allow us to detect key facial points in arbitrary input images. An example of these images and the key facial points detected is shown above.