Nabil Chaabane

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.

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.

1. Slimane Adjerid, Nabil Chaabane, Tao Lin and Pengtao Yue: An immersed finite element method for Stokes problems with moving interfaces. Submitted.
2. Nabil Chaabane, Beatrice Riviere, Mikhail Sekachev and Henri Calandra: A parallelizable sequential method for the Biot system. Submitted to Journal of Numerical Analysis, Industrial and Applied Mathematics.
3. Nabil Chaabane, Vivette Girault, Beatrice Riviere and Travis Thompson: Stable enriched Galerkin element for the Stokes problem. Submitted.
4. Nabil Chaabane and Beatrice Riviere: A splitting-based finite element method for the Biot poroelasticity system. Computers & Mathematics with Applications, 2017 Accepted.
5. Nabil Chaabane and Beatrice Riviere: A sequential discontinuous Galerkin method for the coupling of flow and geomechanics. Journal of Scientific Computing, Apr. 2017. pp1-21.
6. Nabil Chaabane, Vivette Girault, Charles Puelz and Beatrice Riviere: Convergence of IPDG for coupled time-dependent Navier-Stokes and Darcy equations. Journal of Computational and Applied Mathematics, Nov 2017. Vol 324: 25-48.
7. Slimane Adjerid, Nabil Chaabane and Tao Lin: An immersed discontinuous finite element method for Stokes interface problems. Computer Methods in Applied Mechanics and Engineering, Aug 2015. Vol 293: 170-190.
8. Slimane Adjerid and Nabil Chaabane: An improved superconvergence error estimate for the LDG method. Applied Numerical Methods, December 2015. Vol 98: 122-136.
9. Riadh Abdelfattah, Karem Chokmani and Nabil Chaabane: A specific methodology for atmospheric effect reduction on SAR interferograms. In Proc. IGARSS 2010: 1637-1640.

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.