Yang Zhang

Research Staff Member

Yang Zhang is a research scientist at MIT-IBM Watson AI Lab. His research focuses on deep learning for speech, natural language, and other time-series processing. Recently, he has been working on disentanglement techniques for speech and its application to low-resourced languages, as well as improving NLP model interpretability via rationalization. Before joining MIT-IBM Watson AI Lab, Yang is a researcher at IBM Research Yorktown. Yang obtained his Ph.D. degree from University of Illinois at Urbana-Champaign (UIUC). His advisor is Mark Hasegawa-Johnson.

Top Work

Class-wise rationalization: teaching AI to weigh pros and cons

Class-wise rationalization: teaching AI to weigh pros and cons

Natural Language Processing

Publications with the MIT-IBM Watson AI Lab

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
 
Global Prosody Style Transfer Without Text Transcriptions
Global Prosody Style Transfer Without Text Transcriptions
 
Auto-NBA: Efficient and Effective Search Over The Joint Space of Networks, Bitwidths, and Accelerators
Auto-NBA: Efficient and Effective Search Over The Joint Space of Networks, Bitwidths, and Accelerators
 
The Lottery Ticket Hypothesis for the Pre-trained BERT Networks
The Lottery Ticket Hypothesis for the Pre-trained BERT Networks
 
Deep Symbolic Superoptimization Without Human Knowledge
Deep Symbolic Superoptimization Without Human Knowledge