AI Researcher with an interest in deep learning for computer vision.

In this paper, we use machine learning to speed up computations for prediction of ice-formation on an aircraft.

In this paper with develop a variant of SGD based on the classical Polyak step-size.

In this thesis, I find explicit solutions for stationary one-dimensional mean-field games both with and without congestion. It starts off with the equation derivation, then follows with solution computation and concludes with analyzing the behaviour of solutions.

In this paper, we find explicit solutions for one-dimensional first-order stationary mean-field games with congestion.

In this paper, we find explicit solutions for one-dimensional first-order mean-field games without assuming the coupling is monotonically non-decreasing.

From June 2016 to August 2016 I had the opportunity to visit McGill University to do a research internship with Prof. Adam Oberman on two small projects in numerical analysis. The first consisted of implementing the Benamou-Brenier formulation and the second was developing a filtered scheme for the infinity Laplacian. A year later I was back to McGill to pursue graduate studies.

My first poster on explicit solutions for mean-field games.

In Summer 2016, KAUST organized a PDE summer school, which I joined as a supervisor. The outcome of this school is recorded in the paper Existence of positive solutions for an approximation of stationary mean-field games.

From March 2014 to June 2014 I had the opportunity to visit KAUST to do a research internship with Prof. Diogo Gomes on using Hamilton-Jacobi methods to minimize functions. Read more about my experience and the program here.