My research interests include probabilistic and approximate algorithms, game AI, graph theory, computational photography, computer vision and machine learning along with its countless applications. During my PhD, I am focusing on creating efficient intelligent algorithms for use in image and video processing and perceptual metrics for evaluation. More generally, I am working on deep generative models.
Most recently, our work with convolutional generative adversarial neural networks has reached state-of-the-art results for the task of single image super-resolution in both quantitative and qualitative benchmarks.
I am currently interning at Google Brain research.
Proceedings of the 3rd ACM conference on Learning @ Scale, pages: 369-378, (Editors: Haywood, J. and Aleven, V. and Kay, J. and Roll, I.), ACM, L@S, 2016, (An earlier version of this paper had been presented at the ICML 2015 workshop for Machine Learning for Education.) (conference)
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems