Peng Qi
Peng Qi
Research, Learn, Code, and Play.

Brief Bio

I am currently a Ph.D. student at the Computer Science Department of Stanford University. I work in the NLP group, and I am advised by Prof. Chris Manning.

I am interested in building machine learning models that understand natural languages in a similar fashion as humans do. Outside of NLP research, I am broadly interested in presenting data in a more understandable manner, making technology appear less boring (to students, for example), and processing data with more efficient computation. I have also worked on speech recognition and computer vision previously.

[CV (slightly outdated)]    [Publications]


Education & Professional Experience

2015.9 - PresentPh.D. Student & Research Assistant, Computer Science Department, Stanford University
2016.4 - 2017.3Master of Science, Department of Statistics, Stanford University
2013.9 - 2015.6Master of Science & Research Assistant, Computer Science Department, Stanford University
2012.7 - 2013.6Research Assistant, State Key Laboratory of Intelligent Technology & Systems, Department of Computer Science and Technology, Tsinghua University
2008.8 - 2012.7Bachelor of Engineering, School of Software, Tsinghua University (Excellent Graduate)

Selected Publications

(See all)
  • Peng Qi, Christopher D. Manning. Arc-swift: A Novel Transition System for Dependency Parsing. ACL 2017. [PDF]    [BibTeX]    [Code]
  • Yuhao Zhang*, Arun Chaganty*, Ashwin Paranjape*, Danqi Chen*, Jason Bolton*, Peng Qi, and Christopher D. Manning. Stanford at TAC KBP 2016: Sealing Pipeline Leaks and Understanding Chinese. In Text Analysis Conference (TAC) Proceedings, 2016. [PDF]   [BibTeX]
  • Andrew L. Maas, Peng Qi, Ziang Xie, Awni Y. Hannun, Christopher T. Lengerich, Daniel Jurafsky, Andrew Y. Ng. Building DNN Acoustic Models for Large Vocabulary Speech Recognition. Computer Speech & Language, 2016. [HTML]    [BibTeX]
  • Peng Qi and Xiaolin Hu. Learning Nonlinear Regularities in Natural Images by Modeling the Outer Product of Image Intensities. Neural Computation, 2013. [PDF]   [Code]   [HTML]    [BibTeX]
  • Xiaolin Hu, Peng Qi, and Bo Zhang. Hierarchical K-Means Algorithm for Modeling Visual Area V2 Neurons. In Neural Information Processing, Lecture Notes in Computer Science, pp. 373–381, Springer Berlin / Heidelberg, 2012. 10.1007/978-3-642-34487-9_46. Best Paper Award
    [PDF]   [HTML]    [BibTeX]

Honors and Awards

  1. 2012 Outstanding Graduate of Tsinghua University
  2. 2011 National Scholarship (top 3% students)
  3. 2010 Citibank Scholarship (for overall excellence)
  4. 2009 Ge-Ru Zheng's Scholarship (for study excellence)
  5. 2008-2011 Freshman Scholarship (Ranked 3rd of Guizhou Province in National College Entrance Exam)


  1. CS 224s Spoken Language Processing, Spring 2014 (Check out Homework 3 I designed)
  2. CS 145 Introduction to Databases, Summer 2014 (Better Homeworks I created, and midterm, final review slides I made)
  3. CS 124 From Languages to Information, Winter 2015
  4. CS224d: Deep Learning for Natural Language Processing, Spring 2015 (Now part of CS 224n. I designed the first iteration of Homework 1)

Work Experience and Social Service

  1. 2013-2014 Backend Engineer & Machine Learning consultant (Part-Time),, Beijing
  2. 2010-2011 Vice President of Students' Union, School of Software, Tsinghua University

Last Updated: Jan. 21, 2015