Professor, Computer Science, University of Illinois at Urbana-Champaign
2134 Siebel Center
201 N. Goodwin Avenue
Urbana, IL 61801-2302
Phone: (217) 244-2919
E-mail: kcchang (at) illinois (dot) edu
Assistant: Donna Coleman
Office: 2106 SC
Phone: (217) 244-8837
Fax: (217) 265-6494
E-mail: donnakc (at) illinois (dot) edu
Research | Classes | Publications |
Bio. Kevin C. Chang is a Professor in Computer Science, University of Illinois at Urbana-Champaign. He received a BS from National Taiwan University and PhD from Stanford University, in Electrical Engineering. His research addresses large scale information access, for search, mining, and integration across structured and unstructured big data, with current focuses on "entity-centric" Web search/mining and social media analytics. He received two Best Paper Selections in VLDB 2000 and 2013, an NSF CAREER Award in 2002, an NCSA Faculty Fellow Award in 2003, IBM Faculty Awards in 2004 and 2005, Academy for Entrepreneurial Leadership Faculty Fellow Award in 2008, and the Incomplete List of Excellent Teachers at University of Illinois in 2001, 2004, 2005, 2006, 2010, and 2011. He is passionate to bring research results to the real world and, with his students, co-founded Cazoodle, a startup from the University of Illinois, for deepening vertical "data-aware" search over the web.
Research. I lead the FORWARD Group, which is part of the larger Data and Information Systems Laboratories, at the CS department of UIUC. Our research overall aims at bridging structured and unstructured big data--- to bring structured/semantic-rich access to the myriad and massive unstructured data which accounts for most of the world's information. Therefore, our research spans across data mining, data management/databases, information retrieval, machine learning, with current efforts focusing on interactive data management, entity-centric Web search and mining, social media analytics, and social network mining. As our objectives, we aim at developing novel systems, principled algorithms, and formal theories that ultimately deliver real world applications. As our approaches, we seek to be inspired by and learn from the data we are tackling-- i.e., we believe the key to tame big data is to learn the wisdom hidden in the large scale of the data.
Publications. @GoogleScholar, @DBLP
Founded Cazoodle: Search, integrate, and organize the real world, a UIUC startup aiming at bringing forward data-aware search, the objectives of the MetaQuerier and WISDM projects, to the world.
DataSpread: Enabling Interactive Big Data Management. . (2015 - Present) We aim to integrate the two disparate paradigm of accessing tabular data-- database and spreadsheet-- through their marriage to enable interactive access at the front-end to power query and storage engine at the backend. (Demo: VLDB'15)
BigSocial: Towards Big Social Data Platform for Entity-Centric and User-Aware Analytics. (2012 - Present) As we people are now connected in social networks and our voices are now heard via social media, we aim to exploit these new and vast “human sensors” prevalent in our digital society-- to listen to the whole world and make sense of it [SIGIR'12, KDD'12, VLDB'12, ICDE'13b,VLDB'13a, VLDB'13b, EDBT'14, WWW'14, ICML'14, KDD'14, BigComp'15,IJCAI'15, VLDBJ'15, AAAI'16, ICDE'16] (Demos: ICDE'12, ICDM'15)
- Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically. Y. Fang, K. C.-C. Chang, and H. W. Lauw. In ICML 2014, 2014. (310/1238=25%). PDF Slides
- User Profiling in an Ego Network: Co-profiling Attributes and Relationships. R. Li, C. Wang, and K. C.-C. Chang. In WWW 2014, pages 819-830, April 2014. (84/650 = 12.9%). PDF Slides BibTex Dataset
- Towards Social User Profiling: Unified and Discriminative Influence Model for Inferring Home Locations. R. Li, S. Wang, H. Deng, R. Wang, and K. C.-C. Chang. In KDD 2012, 2012. PDF Slides BibTex Dataset
WISDM: Web Indexing and Search for Data Mining. (2007 - Present) The Web has gone far beyond a corpus of pages-- it contains all sorts of "stuff", can we search the Web for every "thing"- entities and their relations- that it contains?[CIDR'07,VLDB'07,WSDM'10,
- Unifying Learning to Rank and Domain Adaptation: Enabling Cross-Task Document Scoring. M. Zhou and K. C.-C. Chang. In KDD 2014, 2014. (151/1036 = 14.6%). PDF
- Towards Rich Query Interpretation: Walking Back and Forth for Mining Query Templates. G. Agarwal, G. Kabra, and K. C.-C. Chang. In WWW 2010, pages 1-10, 2010. (104/743=14%). PDF Slides BibTex
- EntityRank: Searching Entities Directly and Holistically. T. Cheng, X. Yan, and K. C.-C. Chang. In Proceedings of the 33rd Very Large Data Bases Conference (VLDB 2007), pages 387-398, Vienna, Austria, September 2007. (91/538=16.9%). PDF Slides BibTex
MetaQuerier: Exploring and Integrating the Deep Web. (2001 - 2007) The Web has deepened dramatically- A significant and increasing amount of information is now hidden on the "deep Web," behind the query interfaces of searchable databases, can we enable access and integrate such dynamic data? [KDD'02, ICDM'02,
SIGMOD'03, SIGMODRecord'04, SIGMOD'04, KDD'04,TKDE'04, CIKM'04, VLDB'05, CIDR'05, KDD'05, TODS'06, CACM'07, VLDB'07, CIKM'08) (Demos: SIGMOD'04, SIGMOD'05, ICDE'05, ICDE'07)
- Toward Large Scale Integration: Building a MetaQuerier over Databases on the Web. K. C.-C. Chang, B. He, and Z. Zhang. In Proceedings of the Second Conference on Innovative Data Systems Research (CIDR 2005), pages 44-55, Asilomar, Ca., January 2005. (26/86=30%). PDF Slides
- Structured Databases on the Web: Observations and Implications. K. C.-C. Chang, B. He, C. Li, M. Patel, and Z. Zhang. SIGMOD Record, 33(3):61-70, September 2004. PDF
- Statistical Schema Matching across Web Query Interfaces. B. He and K. C.-C. Chang. In Proceedings of the 2003 ACM SIGMOD Conference (SIGMOD 2003), pages 217-228, San Diego, California, June 2003. (52/342=15%). PDF Slides
AIM: Supporting Efficient Top-k Ranked Query Processing-- AIMing for top query answers. (2001 - 2007) Our goal is to support ranked queries, or top-k queries, for matching data by "soft" conditions such as similarity, relevance, or preference, in order to return best k answers.
[SIGMOD'02, SIGMOD'05, SIGMOD'06a, SIGMOD'06b, ICDE'07, TODS'07, TKDE'07, SIGMOD'07a,SIGMOD'07b, TODS'08] (Demos: VLDB'05, SIGMOD'07)
- Top-k Query Processing in Uncertain Databases. M. A. Soliman, I. F. Ilyas, and K. C.-C. Chang. In Proceedings of the 23rd International Conference on Data Engineering (ICDE 2007), pages 896-905, Istanbul, Turkey, April 2007. (122/659=18%). PDF
- RankSQL: Query Algebra and Optimization for Relational Top-k Queries. C. Li, K. C.-C. Chang, I. F. Ilyas, and S. Song. In Proceedings of the 2005 ACM SIGMOD Conference (SIGMOD 2005), pages 131-142, Baltimore, Maryland, June 2005. (66/431=15%). PDF Slides
- Minimal Probing: Supporting Expensive Predicates for Top-k Queries. K. C.-C. Chang and S.-W. Hwang. In Proceedings of the 2002 ACM SIGMOD Conference (SIGMOD 2002), pages 346-357, Madison, Wisconsin, June 2002. (42/239=18%). PDF Slides
Classes. I teach database systems and data mining, with the following recent courses.
- Yuan Fang, Walking Forward and Backward: Towards Graph-based Searching and Mining. July 2014. First employment: Research Staff, A*STAR, Singapore.
- Mianwei Zhou, Entity-Centric Search: Querying By Entities and For Entities. July 2014. First employment: Research Staff, Yahoo! Labs, Sunnyvale, California.
- Rui Li, Towards a General Platform for Analyzing Social Media. Dec. 2013. First employment: Research Staff, Yahoo! Labs, Sunnyvale, California.
- Tao Cheng, Toward Entity-Aware Search, Jun. 2010. First employment: Research Staff, Microsoft Research, Redmond, Washington.
- Chengkai Li, Enabling Data Retrieval: By Ranking and Beyond, Jun. 2006. First employment: Assistant Professor, University of Texas at Arlington, Arlington, Texas.
- Zhen Zhang, Large Scale Information Integration on the Web: Finding, Understanding and Querying Web Databases, Dec. 2006. First employment: CTO, Cazoodle Inc., Champaign, Illinois.
- Bin He, A Holistic Paradigm for Large Scale Schema Matching, Jun. 2006. First employment: Research Staff, IBM Almaden Research Center, San Jose, California.
- Seung-won Hwang, Supporting Ranking for Data Retrieval, Jun. 2005. First employment: Assistant Professor, Pohang University of Science and Technology, Pohang, Gyeongbuk, Korea.
- Best-Papers Selection, VLDB 2013.
- Academy of Entrepreneurial Leadership Faculty Fellow Award, 2008.
- IBM Faculty Award, 2004, 2005.
- NCSA (National Center for Supercomputing Applications) Faculty Fellows Award, 2003.
- National Science Foundation CAREER Award 2002.
- UIUC List of Teachers Ranked as Excellent by Their Students, Fall 2001, Spring 2004, Fall 2005, Spring 2006, Fall 2010, Fall 2011.
- Best-Papers Selection, VLDB 2000.
- Philips Research FMA Fellowship, 1996 - 1998.
- Associate Editor for PVLDB 2015, Apr. 2014 -- Mar. 2015.
- Associate Editor for IEEE Transactions on Knowledge and Data Engineering, Jan. 2013 -- Present.
- Track Chairs/Senior PC Members: WWW2014 (Workshop Track), AAAI 2013 ("AI and the Web" track), WWW 2013 ("Bridging Structured and Unstructured Data" Track), WSDM 2012 (Best Paper Award Committee), ICDE 2011 (Demo Track), WSDM 2011, KDD 2010.
- PC Members for SIGMOD, VLDB, ICDE, KDD, ICDM, WWW, SIGIR, WSDM, CIKM, AAAI in recent years.
- From Information Extraction Research to Vertical Search Products: The Semantic Gap is More Than the Structure Divide. Keynote Talk, Fourth Workshop on Data Extraction and Object Search, WWW 2014, April 2014. [Slides]
- Keynote Talk, Vertical Search Relevance Workshop, WWW 2014, April 2014.
- Tutorial: Towards a Social Media Analytics Platform: Event Detection and User Profiling for Twitter, Tutorial at WWW 2014, April 2014.
- Tutorial: Data-Aware Search over the Web: Large-Scale Mining and Integration, Short Course at ADSC, January 2010.