Tejas Kulkarni is a PhD candidate at MIT working on Deep Learning, Reinforcement learning and Probabilistic Modeling. He is interested in building intelligent agents that learn to solve a variety of goals by interacting with their environment. In particular, his research focuses on spatio-temporal abstractions of data that enable data-efficient learning. His work has received best paper (honorable mention) awards at the Computer Vision and Pattern Recognition (CVPR) conference in 2015 and at the Conference on Empirical Methods on Natural Language Processing (EMNLP) in 2015. He has also been awarded the Henry Singleton award and the Leventhal Fellowship for his graduate work.