About

I completed my B.Sc. in physics and mathematics at Université de Montréal in 2011, after which I got into the very competitive Perimeter Scholars International program at the Perimeter Institute. After being exposed to exciting cutting-edge research in various aspects of theoretical physics, I decided to pursue my PhD in computational string theory at the University of British Columbia. My research focused on the application of analytical and numerical methods to study classical gravitational systems. In particular I have worked on many varied topics such as holographic superconductors, entanglement propagation in strongly coupled field theories, and black hole instabilities in higher-dimensional spacetimes.

I am now seeking data science and deep learning opportunities, which would allow me to keep solving interesting and challenging problems with consequences affecting real people - a welcome change from the abstract nature of my previous work. Data science has the potential to greatly influence and transform the world we live in. It is inspiring to see people harnessing the power of machine learning to achieve ambitious goals such as creating poverty-predicting software from satellite images or improving healthcare access and efficiency to those who need it the most, and I am very enthusiastic at the prospect of being part of such initiatives. Theoretical physicists are trained to see the unseeable, and I believe that my expertise in tackling highly abstract problems would provide employers and clients alike a valued perspective on the many insights waiting to be extracted to improve the world around us.