I am an Assistant Professor of Computer Science at Purdue University.

My research focuses on applications of statistical machine learning to natural language processing (NLP), natural language understanding (NLU), and reasoning. I am currently primarily working on ways (e.g. reasoning) to improve the generalizability and robustness of ML, NLP, and NLU models especially on tasks and domains beyond those encountered during their training. More specifically, I have worked on algorithms to abduce world models (i.e. theories) from both natural language and formal languages (i.e., logical forms), and to reason over knowledge and world models to perform downstream tasks, such as question-answering. I am also interested in symbolic and neuro-symbolic representations of meaning and reasoning in the context of NLU. In the past, I developed generative probabilistic models of grammar to train semantic parsers and generate natural language utterances from logical forms. I have applied Bayesian nonparametrics, approximate posterior inference, combinatorial optimization, and real-time visualization. I am also broadly interested in other applications of statistical machine learning, such as to the natural sciences.