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.
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. We have further reached state-of-the-art results in video super-resolution. A further line of work entails evaluating generative models such as GANs and improving their performance.
15th European Conference on Computer Vision (ECCV), Part III, 11207, pages: 111-127, Lecture Notes in Computer Science, (Editors: Vittorio Ferrari, Martial Hebert,Cristian Sminchisescu and Yair Weiss), Springer, September 2018 (conference)
Proceedings of the 35th International Conference on Machine Learning (ICML), 80, pages: 4448-4456, Proceedings of Machine Learning Research, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (conference)
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