I am a research scientist at Google. Before Google, I worked in the deep learning group, Microsoft Research and Baidu Silicon Valley AI Lab. I am interested in machine learning and their applications to speech, translation and natural language understanding. If you are a Ph.D. student interested in a research internship working with me at Google, please send me an email with your resume and research interests at chongw@[companyname].com.

Education:

• Ph.D. in Computer Science, Princeton University, 2012. Advisor: David M. Blei
• B.E. and M.E. in Automation with honors, Tsinghua University, 2003 and 2006.

Some Awards:

• Google PhD fellowship in machine learning 2010-2012
• Siebel Scholar, class of 2012
• Best (student) paper awards or honorable mentions at KDD 2011, AISTATS 2011, NIPS 2009

Research Highlights:

• Research on a generic loss function for sequence modeling through segmentations. (1) Sequence Modeling via Segmentations; (2) Towards Neural Phrase-based Machine Translation (code).
• Research on lookahead convolution architecture enabled the deployment of an end-to-end speech recognition system to benefit hundreds of millions of users (http://maps.baidu.com) by significantly reducing the latency. See a torch implementation.
• Research on collaborative topic modeling is used by New York Times for their recommendation engine.

Publications:

• Find me at Google scholar

Recent News:

• 03/2018: Invited talk at Caltech, Pasadena, CA.
• 02/2018: Thoracic Disease Identification and Localization with Limited Supervision to appear at CVPR, 2018.
• 01/2018: Towards Neural Phrase-based Machine Translation to appear at ICLR, 2018. (code)
• 01/2018: A General Method for Robust Bayesian Modeling to appear at Journal of Bayesian Analysis.
• 09/2017: Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes to appear at NIPS, 2017.