I am a Research Scientist at Google DeepMind. Previously, I was a PhD student at MIT under the supervision of Joshua Tenenbaum. I am primarily interested in building a prototypical human mind. My current research goal is to build learning algorithms that aquire grounded common-sense knowledge from raw sensations. I take a systems integration approach by combining three scientific disciplines -- Deep Learning, Reinforcement Learning and Probabilistic Programming.
- NIPS 2017 paper: Self-Supervised Intrinsic Image Decomposition [PDF]
- JMLR 2017 paper: Variational Particle Approximations [PDF]
- CVPR 2017 paper: Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks [PDF]
- Invited tutorial talk @ ML Summer School in India [URL]
- Invited to give tutorial talk @ DeepHack.RL winter school [video]
- Co-organizing NAMPI workshop at NIPS 2016 (Spain)
- Contributed talk @ Deep RL workshop at NIPS 2016 [Slides]
- Press Article about learning to perform physics experiments: New Scientist
- June 2016: Successfully defended my PhD thesis at MIT
- Press Article about Inverse Graphics: Motherboard Vice, MIT News, ...
- Press Article aboht learning to play MUD/Text adventure games: Phys.org, MIT News , ...
- Dec 2015: Invited Talk @ Bostom ML Meetup
- Sep 2015: Best Paper Honorable Mention Award at EMNLP
- June 2015: Best Paper Honorable Mention Award at CVPR
- Co-organizing NIPS 2015 Workshop on Black box Learning and Inference
- May 2015: Visiting DeepMind
- July 2015: Invited talk at Oxford
- 2012-2016: Singleton Fellow, Leventhal Fellow, MIT CBMM-Siemens Fellow