I am currently working at ByteDance as head of the applied machine learning (AML) research team. Previously, I worked as a research scientist at Google. Quite a while ago, I received my PhD from Computer Science Department, Princeton University Our team works on fundamental machine learning research and its applications for many of our products, such as TikTok and Douyin, among others. We are actively hiring research engineers, scientists and interns. Please apply here. Contact: chong.wang@[companyname].com

Some Awards:

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

Research Highlights:

• Learning large-scale end-to-end retrieval models without resorting to approximate nearest neighbor search,
  • Deep Retrieval: An End-to-End Learnable Structure Model for Large-Scale Recommendations, 2020
• 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 and LinkedIn.

Recent News:

• 07/2020: Deep Retrieval: An End-to-End Learnable Structure Model for Large-Scale Recommendations is on arxiv!
• 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.

Fun stuff:

• Stories written by my daughter Amy on Wattpad.