Hello, and welcome to my website!

I am Po-Sen. I am a research scientist at Google DeepMind. Before DeepMind, I worked at Microsoft Research and Clarifai. I received my PhD from University of Illinois at Urbana-Champaign (UIUC), advised by Prof. Mark Hasegawa-Johnson. I received my B.S. degree from National Taiwan University.


Contacts

Email: huang 146 AT U I U C DOT edu

Selected Publications (Google Scholar)

  1. Yelong Shen*, Jianshu Chen*, Po-Sen Huang*, Yuqing Guo, Jianfeng Gao
    M-Walk: Learning to Walk in Graph with Monte Carlo Tree Search
    NIPS, 2018 (PDF)
  2. Chenglong Wang, Po-Sen Huang, Alex Polozov, Marc Brockschmidt, Rishabh Singh
    Execution-Guided Neural Program Decoding
    ICML Neural Abstract Machines & Program Induction workshop, 2018 (PDF)
  3. Po-Sen Huang, Chenglong Wang, Rishabh Singh, Wen-tau Yih, Xiaodong He
    Natural Language to Structured Query Generation via Meta-Learning
    NAACL, 2018 (PDF, Code)
  4. Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng
    Towards Neural Phrase-based Machine Translation
    International Conference on Learning Representations (ICLR), 2018 (PDF, Code)
  5. Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou, Li Deng
    Sequence Modeling via Segmentations
    Proc. of the International Conference on Machine Learning (ICML), 2017 (PDF, Code)
  6. Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen
    ReasoNet: Learning to stop reading in machine comprehension
    Proc. of the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017 (PDF)
  7. Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis
    Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
    IEEE/ACM Transactions on Audio, Speech, and Language Processing, Dec. 2015 (PDF, Bibtex, Code)
  8. Po-Sen Huang, Haim Avron, Tara Sainath, Vikas Sindhwani, Bhuvana Ramabhadran
    Kernel Methods match Deep Neural Networks on TIMIT
    Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. (PDF, Bibtex, Code) [IBM Research Spoken Language Processing Student Grant]
  9. Po-Sen Huang, Minje Kim, Mark Hasegawa-Johnson, Paris Smaragdis
    Deep Learning for Monaural Speech Separation
    Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014. (PDF, Slides, Bibtex, Code) [Starkey Signal Processing Research Student Grant]
  10. Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, Larry Heck
    Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
    Proc. of the ACM International Conference on Information and Knowledge Management (CIKM), 2013. (PDF, Bibtex, Code)
  11. Po-Sen Huang, Scott Deeann Chen, Paris Smaragdis, Mark Hasegawa-Johnson
    Singing-Voice Separation From Monaural Recordings Using Robust Principal Component Analysis
    Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012 (PDF, Bibtex, Code)

Software

  1. Natural Language to Structured Query Generation via Meta-Learning
  1. Towards Neural Phrase-based Machine Translation (NPMT)
  1. Singing-Voice Separation From Monaural Recordings Using Robust Principal Component Analysis
  1. Deep Learning for Monaural Source Separation

Honors & Awards

July 2017

Visual Question Answering Challenges First Place | CVPR

  1. Team Adelaide-Teney ACRV MSR

May 2014

IBM Research Spoken Language Processing Student Grant | ICASSP

  1. Research grant for the paper, "Kernel Methods match Deep Neural Networks on TIMIT"

May 2014

Starkey Signal Processing Research Student Grant | ICASSP

  1. Research grant for the paper, "Deep Learning for Monaural Speech Separation"

Apr. 2014

Yi-Min Wang and Pi-Yu Chung Endowed Research Award | ECE, UIUC

  1. Given to a doctoral graduate student who has demonstrated excellence in research