I am a computer science PhD student at the Massachusetts Institute of Technology (MIT) — studying artificial intelligence through natural language processing and machine learning. I am lucky to be advised by Jacob Andreas.
I work on improving sequence modeling for language processing and understanding. Languages exhibit some notion of compositionality (productivity and systematicity) whereas current neural language learners lack required inductive biases to achieve this data efficiently. My recent work aims at understanding simple biases that will enable neural sequence models to achieve types of generalization that humans do
I am also interested in language supervision/grounding and worked on two recent projects: (i) using language to guide image classifiers to learn representations that enable learning of new classes (only with few samples) without forgetting the old ones, (ii) using language models to guide policy learning in a virtual home environment.