I am an assistant professor in the department of Computer Science at UCLA. My research goal is to build intelligence systems that solve real-world problems by automatically acquiring knowledge. This challenging goal involves two fundamental components: A machine learning component that can efficiently make coherent decisions for problems with complex structures, and a natural language understanding component that enables the system to extract knowledge from unstructured text. I have been published broadly in machine learning, natural language processing, artificial intelligence, and data mining.
- email: kw AT kwchang DOT net
- see my calendar for scheduling a meeting with me.
- Office hours: Tue 4pm-5pm, Engr VI 374
Please read my Research Statement to learn my general research directions.
If you are an undergraduate/master student at UCLA, please read 1~2 of my papers and drop by my office hour. Due to the large number of inquiries from prospective students, please understand that I generally cannot respond personally. But if you are a strong candidate, I will contact you during the admission season.
- 01.2018, We will give a tutorial on Quantifying and Reducing Gender Stereotypes in Word Embeddings at FAT 18
- 01.2018, Check out our paper accepted by ICLR 2018.
- 12.2017, The slides of my tutotiral at TAAI 17' can be found here
- 08.2017, Our paper on reducing gender bias won the best long paper award at EMNLP 17.
- 07.2017, Our paper on multi-sense embedding won the best paper award at Rep4NLP workshop at ACL 17.
- 05.2017, I will be joining the UCLA-CS this Fall. I've enjoyed every moment at UVa, where the department and my collaborators gave me enormous support to start my career. I'm excited about the next adventure and looking forward to collaborating with excellent folks in SoCal.
- Old news
- Winter 2018, CS M146: Introduction to Machine Learning, UCLA
- Fall 2017, CS269: Seminar: Current Topics in Artificial Intelligence: Machine Learning in Natural Language Processing, UCLA
- Spring 2017, CS6501: Advanced Machine Learning -- Structured Prediction and Deep Learning, University of Virginia
- Fall 2016, CS6501: Natural Language Processing, University of Virginia
Softwares & Demos
- Illinois-SL: Package for learning structured prediction models.
- Liblinear: A library for large-scale linear classification.
- Vowpal Wabbit: An online learning system (I contributed to several L2S applications)
- Coreference Resolution Demo: Identify noun phrases that refer to the same entity.
- MSR Continuous Space Text Representations: Measure the degree of relation of two words. (I contributed to MRLSA).
- Package for learning with limited memory: Learning a linear classifier when data cannot fit in memory.
Experience and Education (Curriculum Vitae)
- Assistant Professor, the Computer Science Department at the UCLA 2017-
- Assistant Professor, the Computer Science Department at the University of Virginia 2016-2017
- Post-doc, Microsoft Research New England 2015-2016
- Ph.D., Department of Computer Science, University of Illinois at Urbana-Champaign 2010--2015
- M.S. (Computer Science), National Taiwan University, 2007--2009
- B.S. (Computer Science), B.S. (Electrical Engineering), National Taiwan University, 2003--2007
- Summer intern, Microsoft Research New York (2014), Microsoft Research Redmond (ML group, 2013), Microsoft Cloud and Information Services Lab (2012), Google Taiwan (2008)