Address: Microsoft Research,
641 6th Ave, New York,
New York, 10011
I am a Senior Researcher at Microsoft Research, New York.
I received my PhD in computer science from Cornell University (2019) and my
bachelors in computer science from Indian Institute of Technology Kanpur (2013).
Research Interests:
My main interest is in developing provable and practically efficient algorithms with
application to real-life problems. My empirical focus is on problems in
natural language understanding and allied fields. I am
currently active in reinforcement learning theory, interactive learning, and language and vision problems.
News: Our new paper on provably-efficient rich-observation reinforcement learning is on arXiv
Quick Links: MSR Reinforcement Learning Group, A Bandit Game, CIFF Code Base, My Blog, RL Formulas
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning
arXiv, 2019.
[Paper]
Combating the Compounding-Error Problem with a Multi-step Model
arXiv, 2019.
[Paper]
Early Fusion for Goal Directed Robotic Vision
In International Conference on Intelligent Robots and Systems (IROS), 2019.
[Paper] [Best paper nomination]
Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
In Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
[Paper] [Dataset and SDR Code] [Navigation Code]
Mapping Navigation Instructions to Continuous Control Actions with Position Visitation Prediction
In Proceedings of the Conference on Robot Learning (CoRL), 2018.
[Paper] [Code] [Demo Video]
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2018.
[Paper] [Code, Data and Simulators]
Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017.
[Paper] [Code] [Arxiv Preprint]
Neural Shift-Reduce CCG Semantic Parsing
In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016.
[Paper] [Supplementary] [Code]
Environment-driven lexicon induction for high-level instructions
In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2015.
[Paper]
[Supplementary]
[Code]
[Data]
[Simulator]
[Bibtex]
Tell Me Dave: Context-Sensitive Grounding of Natural Language to Manipulation Instructions
In Proceedings of the Robotics: Science and systems (RSS), 2015.
[Paper] [Website]
[Simulator] [Bibtex]
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
Deep Reinforcement Learning Workshop at the Conference on Neural Information Processing Systems (NeurIPS), 2018.
[Paper]
The Third Workshop on Representation Learning for NLP (Rep4NLP)
Workshop at the Annual Meeting of the Association for Computational Linguistics (ACL), 2018.
[Workshop Proceedings]
Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning
Workshop on Prediction and Generative Modeling in Reinforcement Learning (PGMRL) at the International Conference on Machine Learning (ICML), 2018.
[ArXiv Preprint]
PAC with Hoeffding-Bernstein [Post]
Growing Bifurcation of AI Scholarship [Post]
Are Synthetic Datasets in AI Useful? [Post]
Are we doing NLP the right way? [Post]
Writing and Proof Reading Research Code [Post]
Getting into Top CS PhD Programs in the US [Post]
Mathematical Analysis of Policy Gradient Methods [Post]
Tutorial on Markov Decision Process Theory and Reinforcement Learning. [Slides Part 1] [Slides Part 2] [Post]
I received my bachelors degree in computer science from the Indian Institute of Technology, Kanpur where my undergrad thesis on learning to solve IQ questions was advised by Amitabha Mukerjee and Sumit Gulwani. My studies at time have been supported by OPJEMS Merit scholarship (2011-12 and 2012-13), Cornell University Fellowship (2013) and amazon AWS Research grant (2016). I try to spend some time playing piano, writing compositions and reading news.