I am a Senior Research Scientist at NVIDIA’s Toronto AI Lab. I am trained as a physicist and completed my master’s in quantum information theory. For my Ph.D. in computational and statistical physics, I developed multiscale models and sampling algorithms for molecular dynamics simulations of complex chemical and biological systems. After I finished my Ph.D., I switched to deep learning. Before joining NVIDIA, I worked on deep generative modeling at D-Wave Systems, a quantum computation company, and I co-founded Variational AI, a startup focusing on generative modeling for drug discovery.
I have always been excited by developing mathematical frameworks and algorithmic and data-driven approaches to simulate and model our physical world and to synthesize novel but realistic data from scratch. Currently, my primary research interests revolve around deep generative models. I am interested both in fundamental algorithm development and in applying these models on relevant problems in areas such as representation learning, computer vision, graphics and digital artistry. I am also broadly interested in research that takes inspirations from physics to improve machine learning techniques as well as in applying state-of-the-art deep learning methods to problems in the natural sciences.