Prior to joining Google, I was a scientist and research leader at ASAPP, where I manage a group of researchers and engineers for applied machine learning and natural language processing.

Broadly speaking, I am interested in the algorithmic perspective of machine learning and various applications of natural language processing. My recent research focuses on the following topics:

Efficient ML: devise neural network models that can scale at training or inference while retaining state-of-the-art results on target applications (EMNLP21, EMNLP18, ICML17); and build novel learning algorithms for model compression (EMNLP20a, EMNLP20b).

Rationalizing model prediction: introduce self-explaining models that selects a subset of input as a justification for the models prediction (EMNLP16, ACL20a); or thru interacting with the user (ACL20b).