I am currently working at ByteDance as head of the applied machine learning (AML) research team at Seattle. I am interested in machine learning and their applications to various problems. Previously, I worked as a research scientist at Google. Quite a while ago, I received my PhD from Princeton University, working with David M. Blei. We are hiring research engineers, scientists and interns! Contact: chong.wang@[companyname].com

Some Awards:

• Google PhD fellow and Siebel Scholar
• Paper awards or honorable mentions at NIPS, AISTATS, KDD

Research Highlights:

• Neural networks meet structured Bayesian methods,
  • Fully Supervised Speaker Diarization provides better accuracy and efficiency in speaker diarization, ICASSP 2019.
    • Official Google AI blog.
    • Media reports: VentureBeat, SiliconANGLE, InfoQ, futurism, cnBeta, Sina Tech, iThome, ChinaEmail, eepw, QbitAI, oschina.
    • Open source on github with 700+ stars!
• A generic approach for sequence modeling through segmentations,
  • Sequence Modeling via Segmentations, ICML 2017;
  • Towards Neural Phrase-based Machine Translation (code), ICLR 2018;
  • Subgoal Discovery for Hierarchical Dialogue Policy Learning, EMNLP 2018.
  • Neural Phrase-to-Phrase Machine Translation on Arxiv.
• Lookahead convolution architecture (ICLR 2016 workshop) enabled the deployment of an end-to-end speech recognition system (ICML 2016) to benefit hundreds of millions of users (http://maps.baidu.com) by significantly reducing the latency. See a torch implementation.
• Collaborative topic models (KDD 2011) are used by New York Times for their recommendation engine.

Publications:

• Find me at Google scholar

Recent News:

• 05/2019: Rate Distortion For Model Compression: From Theory To Practice accepted to ICML 2019.
• 02/2019: Fully Supervised Speaker Diarization accepted to ICASSP 2019.
• 12/2018: Neural Logic Machines accepted to ICLR 2019.
• 11/2018: Invited talk at University of Washington, Seattle.
• 10/2018: Fully Supervised Speaker Diarization submitted to Arxiv, perhaps the first supervised method doing so!
• 08/2018: Subgoal Discovery for Hierarchical Dialogue Policy Learning to appear at EMNLP (oral presentation), 2018.
• 06/2018: Invited talk at machine learning thoery workshop, Peking University, Beijing, China.
• 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.