Kevin Chenchuan Chang

For More Information

• Kevin C.C. Chang's Research Group, the Forward Data Lab
• Kevin C.C. Chang

Education

• Ph.D. Electrial Engineering, Stanford University, 2001

Biography

Kevin Chen-Chuan 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 and knowledge acquisition, for search, mining, and integration across structured and unstructured big data, with current focuses on Web search/mining and social media analytics. He received ICDE 10-Year Test of Time Award in 2022 and Best Paper Selection/Awards in VLDB 2000 and 2013 and ASONAM 2019, NSF CAREER Award in 2002, 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, 2011, 2019, 2022, 2023. 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, and developed GrantForward.com funding discovery and dissemination service, a vertical search engine integrating 20,000 sources, subscribed by 200+ institutions including Harvard, Stanford, Yale, Cornell University, University of California, CMU, Mayo Clinic, and National Institutes of Health.

Professional Highlights

• PC Members, SIGMOD, VLDB, ICDE, KDD, EDBT, ICDM, WWW, ASONAM, SIGIR, WSDM, CIKM, AAAI, Recent years.
• Area Chair, NeurIPS 2023, 2023.
• Associate Editor, Proceedings of the VLDB Endowment (PVLDB), 2014 - 2015.
• Area Editor, Encyclopedia of Database Systems, 2014 - 2016.
• Workshop Co-chair, 31st IEEE International Conference on Data Engineering (ICDE 2015), 2014 - 2015.
• Associate Editor, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2013 - 2017.
• Workshop Co-chair, 22rd International World Wide Web Conference (WWW 2014), 2013 - 2014.
• PC Co-chair, Track “Bringing Unstructured and Structured Data”, 2012 - 2013.
• Best Paper Award Committee, ACM International Conference on Web Search and Data Mining, 2012.
• Senior PC, ACM International Conference on Web Search and Data Mining, 2011.
• Co-chair, Demonstration Track, ICDE 2011, 2011.
• Senior PC, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2010.
• Area Editor, Encyclopedia of Database Systems, 2007 - 2009.
• Workshop Chair, APWeb 2007, 2007.
• Steering Committee, International Workshop on Information Integration on the Web (IIWeb 2007) at AAAI, 2007.
• Workshop Chair, ACM SIGMOD 2006 Conference, 2006.
• Co-chair, International Workshop on Information Integration on the Web (IIWeb 2006) at WWW, 2006.
• Co-chair, International Workshop on Challenges in Web Information Retrieval and Integration (WIRI 2006) at ICDE, 2006.
• Guest Editor, SIGKDD Explorations 6(2) Special Issue on Web Content Mining, 2004.
• Chair, NSF DIMACS Center Tutorial/Summer School on Social Choice and Computer Science, 2004.

Research Statement

I lead the FORWARD Data Lab 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 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 natural language processing, data mining, data management/databases, information retrieval, and machine learning. 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.

Research Interests

• Natural language processing, data mining, data management, and information retrieval with machine learning techniques, with emphasis on the application areas of Web and social media-based knowledge acquisition and organization across structured and unstructured data.

Research Topics

• Autonomous Systems and Artificial Intelligence
• Cognitive computing
• Data science and analytics
• Data/Information Science and Systems
• Decision science
• Machine learning
• Network science and engineering
• Socio-technical systems and networking

Articles in Conference Proceedings

• Are Large Pre-Trained Language Models Leaking Your Personal Information?. Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang. In Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, 2022.
• Coordinated Topic Modeling. Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, 2022.
• DEER: Descriptive Knowledge Graph for Explaining Entity Relationships. Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-Mei Hwu. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, 2022.
• Understanding Jargon: Combining Extraction and Generation for Definition Modeling. Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-Mei Hwu. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, 2022.
• Unified and Incremental SimRank: Index-Free Approximation With Scheduled Principle (Extended Abstract). Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin Chen-Chuan Chang, Hongtai Cao, Zhen Jiang, Minghui Wu. In 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12, 2022, 2022.
• Open Relation Modeling: Learning to Define Relations Between Entities. Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-Mei Hwu. In Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, May 22-27, 2022, 2022.
• Domain Representative Keywords Selection: A Probabilistic Approach. Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang, Yunyao Li, Lucian Popa, ChengXiang Zhai. In Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, May 22-27, 2022, 2022.
• Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach. Jie Huang, Kevin Chang, Jinjun Xiong, Wen-Mei Hwu. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL/IJCNLP 2021, (Volume 1: Long Papers), Virtual Event, August 1-6, 2021, 2021.
• On Analyzing Graphs With Motif-Paths. Xiaodong Li, Reynold Cheng, Kevin Chen-Chuan Chang, Caihua Shan, Chenhao Ma, Hongtai Cao. In VLDB 2021, 2021.
• MC-Explorer: Analyzing and Visualizing Motif-Cliques on Large Networks. Boxuan Li, Reynold Cheng, Jiafeng Hu, Yixiang Fang, Min Ou, Ruibang Luo, Kevin Chen-Chuan Chang, Xuemin Lin. In 36th IEEE International Conference on Data Engineering, ICDE 2020, Dallas, TX, USA, April 20-24, 2020, 2020.
• Geom-GCN: Geometric Graph Convolutional Networks. Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, Bo Yang. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, 2020.
• ROSE: Role-Based Signed Network Embedding. Amin Javari, Tyler Derr, Pouya Esmailian, Jiliang Tang, Kevin Chen-Chuan Chang. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020.
• Weakly Supervised Attention for Hashtag Recommendation Using Graph Data. Amin Javari, Zhankui He, Zijie Huang, Jeetu Raj, Kevin Chen-Chuan Chang. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020.
• M-Cypher: A GQL Framework Supporting Motifs. Xiaodong Li, Reynold Cheng, Matin Najafi, Kevin Chen-Chuan Chang, Xiaolin Han, Hongtai Cao. In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020, 2020.
• GraphEBM: Energy-based Graph Construction for Semi-Supervised Learning. Zhijie Chen, Hongtai Cao,, Kevin Chen-Chuan Chang. In ICDM 2020, 2020.
• Exploring Semantic Capacity of Terms. Jie Huang, Zilong Wang, Kevin Chang, Wen-Mei Hwu, Jinjun Xiong. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, 2020.
• Curvature Regularization to Prevent Distortion in Graph Embedding. Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.

Teaching Honors

• UIUC List of Teachers Ranked as Excellent by Their Students, Fall 2001, Spring 2004, Fall 2005, Spring 2006, Fall 2010, Fall 2011, Fall 2019, Spring 2022, Spring 2023.

Research Honors

• ICDE Influential Paper (10-Year Test of Time) Award, IEEE Computer Society, 2022.
• Best Demo Award,IEEE International Conference on Data Engineering (ICDE) 2019.
• Best Paper Award, International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019.
• Best-Papers Selections of Very Large Data Bases (VLDB) 2013.
• Academy of Entrepreneurial Leadership Faculty Fellow Award, 2008.
• IBM Faculty Award, 2005.
• IBM Faculty Award, 2004.
• NCSA (National Center for Supercomputing Applications) Faculty Fellows Award, 2003.
• National Science Foundation CAREER Award 2002.
• Best-Papers Selections of Very Large Data Bases (VLDB) 2000.
• Philips FMA Fellowships, 1996-1998.