Guohua Hao     Ph.D. Candidate School of Electrical Engineering and Computer Science 1148 Kelley Engineering Center Oregon State University Corvallis, Oregon 97331-5501 Phone: (541) 908-4200 Office: KEC 1097 Email: haog AT eecs DOT oregonstate DOT edu

About

I am a 6th year Ph.D. student in the School of Electrical Engineering and Computer Science at Oregon State University. My major advisor is Prof. Thomas G. Dietterich. Before coming to OSU, I got my Bachelor's degree in Computer Science from University of Science and Technology of China (USTC) in 2003.

Research

Machine learning: structured supervised learning, especially sequential supervised learning, e.g., Conditional Random Fields (CRFs), with an emphasis on feature induction and scalability to problems with large label sets.

Publications

Peer-Reviewed Papers

• Thomas G. Dietterich, Guohua Hao, and Adam Ashenfelter
  Gradient Tree Boosting for Training Conditional Random Fields
  Journal of Machine Learning Research (JMLR), 9(Oct):2113--2139, 2008.
  
  
• Guohua Hao and Alan Fern
  Revisiting Output Coding for Sequential Supervised Learning
  Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-2007), pages 2486-2491, Hyderabad, India, January 6-12, 2007. [Poster]
  
  
• Jinlong Li, Guohua Hao, Weihong Wang, and Xufang Wang
  Learning User Interest in Image Retrieval
  Mini-Micro Systems, Vol.25, No.7, P.1110-1112, 2004. (in Chinese)

Unpublished Papers

• Guohua Hao
  A Review of Sequential Supervised Learning
  Ph.D. Qualifier Exam Paper, School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon, November 17, 2004. [slides]

Posters

• Guohua Hao, Rongkun Shen, Dan Vega, Yaroslav Bulatov, and Thomas G. Dietterich
  Sequential and Spatial Supervised Learning
  Presented at the School of Electrical Engineering and Computer Science Research Festival, Oregon State University, Corvallis, Oregon, 2004.

Software

• TreeCRF: a software package for training conditional random fields (CRFs) with functional gradient tree boosting.

Teaching

• Spring 2008:  Teaching assistant for CS331: Introduction to Artificial Intelligence with Prof. Weng-Keen Wong
• Spring 2008:  Teaching assistant for CS534: Machine Learning with Prof. Xiaoli Fern
• Winter 2008: Teaching assistant for CS162: Introduction to Compute Science II with Prof. Ronald A. Metoyer
• Fall 2007: Teaching assistant for CS162: Introduction to Compute Science II with Prof. Weng-Keen Wong

Courses

• Artificial Intelligence:
  CS531: Artificial Intelligence; CS532: Advanced Artificial Intelligence; CS533: Intelligent Agents and Decision Making; CS534: Machine Learning; CS539: Selected Topics in Artificial Intelligence;
• Statistics:
  ST561/ST562/ST563: Theory of Statistics; ST565: Time Series and Spatial Statistics; ST581: Linear Programming;
• Theory:
  CS515: Algorithms and Data Structures; CS516: Theory of Computation and Formal Languages; CS523: Analysis of Algorithms; CS524: NP Complete and Harder Problems;
• Computer Graphics:
  CS550: Introduction to Computer Graphics [Course Project]; CS551: Computer Graphics; CS553: Scientific Visualization;
• Others:
  CS561: Software Engineering; CS575: Introduction to Parallel Computing; CS582: Object-Oriented Analysis and Programming [Course Project];

Resume

Links