Scholar
ChengXiang Zhai
Google Scholar ID: YU-baPIAAAAJ
University of Illinois at Urbana-Champaign
Citations & Impact
Citations
13,935
H-index
54
i10-index
200
Publications
20
Co-authors
1
list available
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- ACM SIGIR Gerard Salton Award (2021)
- Member of ACM SIGIR Academy (2021)
- ACM Fellow (2017)
- ACM Distinguished Scientist (2009)
- Presidential Early Career Award for Scientists and Engineers (PECASE, 2004, nominated by NSF based on NSF CAREER Award)
- Alfred P. Sloan Research Fellowship (2008)
- Three-time recipient of ACM SIGIR Test of Time Paper Award (for work on subtopic retrieval/diversity, smoothing methods, and Bayesian decision-theoretic framework in IR)
- Rose Award for Teaching Excellence, UIUC College of Engineering (2010)
- UIUC Campus Award for Excellence in Graduate Mentoring (2016)
- Listed multiple times (11 semesters between 2002–2018) on UIUC’s List of Teachers Ranked as Excellent by Their Students
- IBM Faculty Award (2009)
- HP Innovation Research Award (2011–2012)
- Author of 'Text Data Management: A Practical Introduction to Information Retrieval and Text Mining' (2016, translated into Chinese)
- Served as editor for multiple journals and program/co-chair for top conferences including WWW 2015, SIGIR 2009, WSDM 2018, CIKM 2016
Research Experience
- Donald Biggar Willett Professor in Engineering at the University of Illinois at Urbana-Champaign (UIUC), Department of Computer Science
- Affiliated with the Carl R. Woese Institute for Genomic Biology, Statistics, School of Information Sciences, and Personalized Nutrition Initiative
- Leads the TIMAN and DAIS research groups
- Teaches courses including CS591BAI (Biologically Plausible Artificial Intelligence) and CS410DSO (Text Information Systems)
- Offers two Coursera MOOCs: 'Text Retrieval and Search Engines' and 'Text Mining and Analytics'
Background
- General research interests lie in developing novel Intelligent Information Systems, such as intelligent search engines, recommender systems, text analysis engines, chatbots, and intelligent task agents
- Aims to help people manage and exploit large-scale data, especially natural language text data, to augment human intelligence
- Focuses on developing general models, theoretically sound algorithms, and systems for discovering latent knowledge and deriving insights from big data
- Applies these techniques to build innovative applications in healthcare, education, and scientific discovery
- Studies human-AI collaboration, including mathematical user modeling, sequential decision optimization for personalized interaction, and explainable AI via natural language processing
