Curriculum Vitae Education & Experience Selected Awards Publications Talks Packages & Code Other Awards
Contact Information
Email: kchang10 AT illinois -DOT- edu
Kai-Wei ChangI am a Ph.D. student at University of Illinois at Urbana-Champaign supervised by Prof. Dan Roth. Before coming here, I received a M.S. and a B.S. degree at National Taiwan University under supervision of Prof. Chih-Jen Lin. My research interests are machine learning, data mining, and natural language processing. Please see my Curriculum Vitae for details.Related Softwares & Research Projects:
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Education & Experience
- Ph.D. Candidate. Department of Computer Science, Aug. 2010 -- current.
- Member of the Cognitive Computation Group; Advisor: Prof. Dan Roth
- M.S., Department of Computer Science & Information Engineering, June 2009.
- GPA 4.00/4.00. Ranked 1st out of 158
- Member of the Machine Learning and Data Mining Group; Advisor: Prof. Chih-Jen Lin (Thesis)
- B.S., Department of Computer Science & Information Engineering, June 2007.
- B.S., Department of Electrical Engineering, (Dual degree), June 2007.
- GPA 4.00/4.00 (CS), 3.92/4.00 (EE), 3.96/4.00 (Total), Ranked 3rd out of 108, with 5 Presidential Awards (top 5% each semester)
- Member of the Machine Learning and Data Mining Group; Advisor: Prof. Chih-Jen Lin
Intern, Microsoft Silicon Valley, May 2012 - Aug. 2012 (Mentor: Dr. S. Sathiya Keerthi)
Study learning and inference algorithms for structure prediction problems with complex output structure. (paper under submission).
Intern, Microsoft Research, Redmond, May 2013 - Aug. 2013 (Mentor: Scott Wen-Tau Yih)
Intern, Google Beijing Research, May 2008 - Sep. 2008
We applied linear SVM solvers to the explicit form of polynomially mapped data and
improved the performance of a data-driven dependency parsing system. (see this
JMLR paper)
Research Awards
- C.L and Jane W. S. Liu Award, University of Illinois, 2013
- Microsoft Research PhD Fellowship Finalist,2013
- Yahoo! Key Scientific Challenges Program Award, 2011
- Best Research Paper Award, KDD 2010
- Master Thesis Award, Taiwanese Association for Artificial Intelligence (TAAI), 2009
- Studying Abroad Scholarship, Ministry of Education, Taiwan, 2009
Publications (Google Scholar Citations)
- K.-W. Chang, V. Srikumar, D. Roth. Multi-core Structural SVM Training. To appear in ECML 2013.
- K.-W. Chang, S. Sundararajan, S. S. Keerthi. Tractable Semi-Supervised Learning of Complex Structured Prediction Models. To appear in ECML 2013
- A. Rozovskaya K.-W. Chang, M. Sammons, D. Roth. The University of Illinois System in the CoNLL-2013 Shared Task. To appear in CoNLL Sharded Task 2013
- R. Samdani, K.-W. Chang, D. Roth. A Discriminative Latent Variable Model for Clustering of Streaming Data with Application to Coreference Resolution To appear in ICML workshop on Inferning: Interactions between Inference and Learning (2013)
- K.-W. Chang, B. Deka, W.-M. H. Hwu,, D. Roth. Efficient Pattern-Based Time Series Classification on GPU IEEE International Conference on Data Mining, (ICDM 2012)
- K.-W. Chang, R. Samdani, A. Rozovskaya, N. Rizzolo, M. Sammons, D. Roth. Illinois-Coref: The UI System in the CoNLL-2012 Shared Task Proceedings of the Sixth Conference on Computational Natural Language Learning: Shared Task (CoNLL 2012)(Poster,)
- K.-W. Chang, D. Roth. Selective Block Minimization for Faster Convergence of Limited Memory Large-scale Linear Models. The 17th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2011) (Code,Poster,Slide)
- K.-W. Chang, R. Samdani, A. Rozovskaya, N. Rizzolo, M. Sammons, D. Roth. Inference Protocols for Coreference Resolution. Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task (CoNLL 2011) (Our system ranked 3rd among 21 submissions) (Poster,Slide)
- H.-F. Yu, C.-J. Hsieh, K.-W. Chang, C.-J. Lin
Large linear classification when data cannot fit in memory.
ACM Transactions on Knowledge Discovery from Data Volumn 5 Issue 4 (2012).
(Code).
A preliminary version appeared in KDD 2010 and received (Best Research Paper Award) (KDD paper).
Another simplified version is published in IJCAI 2011 (Best paper track). (IJCAI paper) (Video)
Here is a 5-min introduction of this work (Video) - G.-X. Yuan, K.-W. Chang, C.-J. Hsieh, C.-J. Lin. A comparison of optimization methods for large scale L1-regularized linear classification.. Journal of Machine Learning Research (JMLR) 11(2010), 3183-3234. (Code , implementation in LIBLINEAR)
- Y.-W. Chang, C.-J. Hsieh, K.-W. Chang, Michael Ringgaard, and C.-J. Lin. Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. Journal of Machine Learning Research (JMLR) 11(2010), 1471-1490. (Code, Extension of Liblinear) )
- F.-L. Huang, C.-J. Hsieh, K.-W. Chang, and C.-J. Lin. Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models. Journal of Machine Learning Research (JMLR) 11(2010), 815-848. A short version appears as a short paper at ACL 2009. (Code)
- H.-Y. Lo, K.-W. Chang, S.-T. Chen, T.-H. Chiang, C.-S. Ferng, C.-J. Hsieh, Y.-K. Ko, T.-T. Kuo, H.-C. Lai, K.-Y. Lin, C.-H. Wang, H.-F. Yu, C.-J. Lin, H.-T. Lin and S.-d. Lin. An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes. JMLR Workshop and Conference Proceedings V.7, 57-64, 2009 (Third Place of the KDDCup'09 Slow Track).
- R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research (JMLR) 9(2008), 1871-1874. (LIBLINEAR package)
- S. S. Keerthi, S. Sundararajan, K.-W. Chang, C.-J. Hsieh, and C.-J. Lin. A Sequential Dual Method for Large Scale Multi-ClassLinear SVMs. The 14th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2008). (Implementation in LIBLINEAR)
- C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan. A Dual Coordinate Descent Method for Large-scale Linear SVM. The 25th International Conference on Machine Learning (ICML 2008). (Code slide, video, implementation in LIBLINEAR)
- K.-W. Chang, C.-J. Hsieh, and C.-J. Lin. Coordinate Descent Method for Large-scale L2-loss Linear SVM. Journal of Machine Learning Research (JMLR) 9(2008), 1369-1398. (Code)
Talks
- Large Linear Classification when Data Cannot Fit In Memory. (slide) Talk at Microsoft Research, July 24, 2012.
Other Honors & Awards
Research:
- Scholarship for Graduate Student, GARMIN, 2008
- Third Place in CoNLL Shared Task 2011 (the most prestigious NLP shared task competition).
- Fourth Place in CoNLL Shared Task 2012 (English track).
- Third Prize in the Slow Track of KDDCUP 2009
out of more than 400 submissions - Winner in SVM track of Pascal Large Scale Learning Challenge in ICML 2008 Workshop
the only team that finished solving all huge problems - Undergraduate Research Grant(with Prof. Chih-Jen Lin), National Science Council , 2007
- Outstanding Students Conference Travel Grant, Foundation for The Advancement of Outstanding Scholarship, Taiwan, 2008
- Student Travel Grant: ICML 08', IJCAI 11', ICML 11', KDD 11', ICDM 12'
- Honorary Member of the Phi Tau Phi Scholastic Honor Society, National Taiwan University, 2009
officially recommended by NTU, from top 3% of 156 graduating master students in CS department - Presidential Award, National Taiwan University, Fall 2003, Spring 2004, Fall 2004, Spring 2005, Fall 2006
given to the top 5% undergraduate students each semester - Dean's List, National Taiwan University, 2006
Given to top ranked students who provide academic consulting service (Calculus)
- ACM ICPC Asia Regional programming Contest
Sixth Place (2003,2006), Seventh Place (2005), With Cho-Jui Hsieh, Peng-Ren Cheng - National College Programming Contest (Taiwan)
Outstanding Performance Award(2009), Second Prize (2005), with Cho-Jui Hsieh, Peng-Ren Cheng - Third Prize of physics, National High School Science Fair, 2002
with Cho-Jui Hsieh, Peng-Ren Cheng, and Li-Jen Chu