people

Understanding human language has been a sub-challenge in the way of intelligent machines. However, natural language processing has been transforming the world so quickly that language has been the leading force behind AI.

I have worked on different types of textual data, including social media, patents, scientific documents, news, DNA, and protein sequences, and tackled different types of language problems, e.g., multi-modal language modeling, news story construction, pun detection, logic grid puzzle/word algebra problem solving, local citation recommendation, and viral escape modeling.

Currently, I’m working on machine learning for NLP and NLP applications and combining NLP with symbolic reasoning. To reason about language, seeking an automatic mapping between an NLP-based semantic representation and a logic representation is an important future direction.

I’m also excited about the self-reflection ability of language agents and their role in scientific discovery.

Understanding human language has been a sub-challenge in the way of intelligent machines. However, natural language processing has been transforming the world so quickly that language has been the leading force behind AI.

I have worked on different types of textual data, including social media, patents, scientific documents, news, DNA, and protein sequences, and tackled different types of language problems, e.g., multi-modal language modeling, news story construction, pun detection, logic grid puzzle/word algebra problem solving, local citation recommendation, and viral escape modeling.

Currently, I’m working on machine learning for NLP and NLP applications and combining NLP with symbolic reasoning. To reason about language, seeking an automatic mapping between an NLP-based semantic representation and a logic representation is an important future direction.

I’m also excited about the self-reflection ability of language agents and their role in scientific discovery.