Ming-Wei Chang. Machine learning and its applications to natural language processing, information retrieval and data mining.

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1 Ming-Wei Chang 201 N Goodwin Ave, Department of Computer Science University of Illinois at Urbana-Champaign, Urbana, IL (917) Research Interests Machine learning and its applications to natural language processing, information retrieval and data mining. Education University of Illinois at Urbana-Champaign Urbana, IL, USA Ph.D. Candidate, Computer Science 2005-present Advisor: Dan Roth Thesis: Structured Prediction with Indirect Supervision Excepted graduation date: 2011 National Taiwan University Taipei, Taiwan M.S., Computer Science Thesis: Properties of Dual SVM Solutions as Functions of Parameters and Leave-one-out Bounds for SVR Advisor: Chih-Jen Lin, GPA 4.0/4.0 National Taiwan University Taipei, Taiwan B.S., Computer Science GPA 3.76/4.0, Presidential Award, 2000 Experience Research Assistant, CS Department, University of Illinois Projects: Structured output prediction, Indirect supervision, Constraint Driven Learning, Dataless Classification, Multilingual Dependency Parsing, Named Entities and Relations, Pipeline Models, Domain Adaptation Teaching Assistant, CS Department, University of Illinois Fall, 2009 TA for Machine Learning. Intern, Microsoft Research, Redmond, WA, USA Summer, 2007 Machine Learning and Applied Statistics Group 1

2 Project: Spam filtering. We proposed a novel generative/discriminative hybrid model, partitioned logistic regression. When applied to large-scale spam filtering, the new algorithm achieves 25% error reduction compared to logistic regression, and allows us to build a lightweight and effective personalized spam filtering system. Patent: Personalized spam filtering, filed in Intern, Siemens Corporate Research, Princeton, NJ, USA Spring, Intelligent Vision and Reasoning Department Project: Detecting the internal status of a power plant using machine learning methods. The first Taiwanese intern in Siemens Corporate Research. Research Assistant, Machine Learning Lab, National Taiwan University Projects: Machine learning methods for electricity load forecasting. Time series segmentation using support vector machine. Automatic parameter selection for support vector machines. Teaching Assistant, CS Department, National Taiwan University TA for Statistical Learning Theory and Data Mining and Machine Learning. Honors and Awards Student travel scholarship, ICML, 2010 The Don and Betty Walker Student Scholarship Fund, ACL, 2007 Saburo Muroga Fellowship, University of Illinois at Urbana-Champaign, 2005 Winner of WCCI 2002 competition on sequence recognition, 2002 With B.-J. Chen and C.-J. Lin Winner of EUNITE world wide competition on electricity load prediction, 2001, Time series prediction. Predicting daily maximum load of the next 31 days using data in the past two years. With B.-J. Chen and C.-J. Lin Winner among 18 research teams. (56 teams registered.) 2

3 Winner of Trend Micro Software Competition, 2000 With S.-P. Liao, P.-J. Chen, W.-H. Chung and W.-Y. Lo. Won 1,000,000 NT dollars (approximately 35,000 US) among 99 teams. Sixth Place, ACM International Collegiate Programming Competition 2000 Asia Regional With T.-J. Huang and L.-P. Chou. Second Prize, National College Programming Contest. Taiwan With T.-J. Huang and L.-P. Chou. Invited Talk and Tutorial Presentations Structured Output Prediction with Indirect Supervision. Symposium on Machine Learning in Speech and Language Processing. Bellevue Washington. Host: Hal Daume To appear. A tutorial on Integer Linear Programming in NLP Constrained Conditional Models, The 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). With Dan Roth and Nickolas Rizzolo, A tutorial on Constrained Conditional Models, The 12th Conference of the European Association of Computation Linguistics (EACL). With Dan Roth and Lev Ratinov, Publications Journals [1] Ming-Wei Chang, Lev Ratinov, and Dan Roth. Structured learning with constrained conditional models In submission. [2] Ming-Wei Chang and Chih-Jen Lin. Leave-one-out bounds for support vector regression model selection. Neural Computation, [3] Ming-Wei Chang, Chih-Jen Lin, and Ruby C. Weng. Analysis of switching dynamics with competing support vector machines. IEEE Transactions on Neural Networks, [4] Bo-Juen Chen, Ming-Wei Chang, and Chih-Jen Lin. Load forecasting using support vector machines: A study on EUNITE competition IEEE Transactions on Power Systems, Book Chapters [1] Ming-Wei Chang, Quang Do, and Dan Roth. Multilingual dependency parsing: A pipeline approach. In Nicolas Nicolov, editor, Recent Advances in Natural Language Processing

4 Refereed Conference Papers [1] Ming-Wei Chang, Michael Connor, and Dan Roth. The necessity of combining adaptation methods. In Proceedings of EMNLP, [2] James Clarke, Dan Goldwasser, Ming-Wei Chang, and Dan Roth. Driving semantic parsing from the world s response. In Proceedings of CoNLL, [3] Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser, and Dan Roth. Structured output learning with indirect supervision. In Proceedings of ICML, [4] Ming-Wei Chang, Dan Goldwasser, Dan Roth, and Vivek Srikumar. Discriminative learning over constrained latent representations. In Proceedings of NAACL, [5] Ming-Wei Chang, Dan Goldwasser, Dan Roth, and Yuancheng Tu. Unsupervised constraint driven learning for transliteration discovery. In Proceedings of NAACL, [6] Ming-Wei Chang, Lev Ratinov, Nick Rizzolo, and Dan Roth. Learning and inference with constraints. In Proceedings of AAAI, Nectar Track. [7] Ming-Wei Chang, Lev Ratinov, Dan Roth, and Vivek Srikumar. Importance of semantic representation: Dataless classification. In Proceedings of AAAI, [8] Ming-Wei Chang, Wen tau Yih, and Robert McCann. Personalized spam filtering for gray mail. In Proceedings of CEAS, [9] Ming-Wei Chang, Wen tau Yih, and Christopher Meek. Partitioned logistic regression for spam filtering. In Proceedings of KDD, [10] Ming-Wei Chang, Lev Ratinov, and Dan Roth. Guiding semi-supervision with constraint-driven learning. In Proceedings of ACL, [11] Ming-Wei Chang, Quang Do, and Dan Roth. A pipeline framework for dependency parsing. In Proceedings of ACL, [12] Ming-Wei Chang, Chih-Jen Lin, and Ruby C. Weng. Analysis of switching dynamics with competing support vector machines. In Proceedings of IJCNN, [13] Ming-Wei Chang, Chih-Jen Lin, and Ruby C. Weng. Analysis of nonstationary time series using support vector machines. In Seong-Whan Lee and Alessandro Verri, editors, Proceedings of SVM, Lecture Notes in Computer Science 2388, [14] Ming-Wei Chang, Chih-Jen Lin, and Ruby C. Weng. Adaptive deterministic annealing for two applications: competing SVR of switching dynamics and travelling salesman problems. In Proceedings of ICONIP 2002,

5 Workshop Papers [1] Ming-Wei Chang, Lev Ratinov, and Dan Roth. Constraints as prior knowledge. In ICML Workshop on Prior Knowledge for Text and Language Processing, [2] Ming-Wei Chang, Quang Do, and Dan Roth. A pipeline model for bottom-up dependency parsing. In Proceedings of CoNLL, Shared Task Paper. [3] Ming-Wei Chang, Bo-Juen Chen, and Chih-Jen Lin. EUNITE network competition: Electricity load forecasting, Winner of EUNITE world wide competition on electricity load prediction. Professional Service Journal Paper Review ACM Transactions on Intelligent Systems and Technology Data Mining and Knowledge Discovery IEEE Transactions on Neural Networks Machine Learning Program Committees EACL-2012, CoNLL-2011, IJCAI-2011, ICML-2010, EMNLP-2010, CoNLL-2010, CoNLL-2009, COLINGS-2008, ACL

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