Jonathan Huang

jhuang11@stanford.edu
personal page

Research interests

  • Machine learning
  • Approximate probabilistic inference
  • Bayesian modeling of dynamical systems
  • Identity management
  • Statistical Ranking Analysis
  • Noncommutative Harmonic Analysis
I am currently most excited about the rise of MOOCS (Massive Open Online Courses) such as Coursera and Udacity and how to make use of the massive amount of student data generated by these websites which log, among many other things, every video event, forum interaction, or homework attempt submitted by each student. Not surprisingly, there are a number of probabilistic reasoning problems and combinatorial data types which appear in the "MOOC-alytics" domain. With collaborators, I am working towards highly scalable methods that use machine learning techniques for modeling student behavior and for providing automated or semi-automated informative feedback for complex and open-ended homework assignments.

Recent publications

This is a representative list of my papers. Please see my webpage (linked above) for a more complete list of publications.
Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC, Jonathan Huang, Chris Piech, Andy Nguyen, Leonidas Guibas. In the 16th International Conference on Artificial Intelligence in Education (AIED 2013) Workshop on Massive Open Online Courses (MOOCshop) Memphis, TN, USA, July 2013.   
Tuned Models of Peer Assessment in MOOCs, Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, Daphne Koller. In Proceedings of The 6th International Conference on Educational Data Mining (EDM 2013), Memphis, TN, USA, July 2013.   
J. Huang, A. Kapoor, C. Guestrin. Riffled Independence for Efficient Inference with Partial Rankings. Journal of Artificial Intelligence Research (JAIR)., Vol. 44 (2012), pages 491-532   
J. Huang, C. Guestrin. Uncovering the Riffled Independence Structure of Ranked Data. Electronic Journal of Statistics, Vol. 6 (2012) 1999-230. Communicated July 2010.   
J. Huang, C. Guestrin, L. Guibas. Fourier Theoretic Probabilistic Inference over Permutations. Journal of Machine Learning (JMLR), Volume 10 pp. 997-1070, May 2009