Yi Chen is an
associate professor and the Henry J. Leir Chair in Healthcare in the Martin Tuchman School of Management with a joint appointment in the
Ying Wu College of Computing Sciences at New Jersey
Institute of Technology (NJIT). Prior to joining NJIT, she was an associate
professor in Arizona State University.
She received her Ph.D. degree in Computer Science from the University
of Pennsylvania in 2005 and B.S. from Central
South University in 1999. She and her research group develop cutting-edge database and data mining techniques with applications in business, healthcare and Web. Some of her projects include information discovery on big data, social media mining for healthcare, computational advertising, social computing, workflow management, and information integration. She has served in the organization and program committees for prestigious conferences, including SIGMOD, VLDB, ICDE, CIKM and SIGIR, served as an associate editor for
INFORMS Journal on Computing,
Journal of Healthcare Informatics Research, as well as a general chair for SIGMOD'2012. Yi Chen is a recipient of a Peter Chen Big Data Young Researcher Award, Excellence in Research Prize (NJIT), Outstanding Faculty Researcher in Computer Science and Engineering (ASU), Google Research Awards, IBM Faculty Awards and an NSF CAREER Award. Her research is funded by NSF, Leir Charitable Foundations, Google, IBM, Science Foundation Arizona, and DoD.
positions available for highly motivated students with strong analytical
and programming skills.
Search and Knowledge Discovery in Online Health Forums We are investigating a
patient-centered approach for information extraction, classification, and
integration to support effective search and knowledge discovery in healthcare
forums. See more
Computational Advertising: A Study of Online Ad Revenues and User Behaviors We are developing both analytical and predictive models to study the impact of online Ads to publishers and users, as well as user behaviors. See more
XSEEK : an
Intelligent Search Engine for Semi-Structured Data We are developing a search engine for databases. We are
identifying a spectrum of problem space for supporting keyword search on
structured/semi-structured data, ranging from evaluation framework,
generating high-quality results, to helping users analyze results, and
developing techniques to address the open challenges. More
Information about XSEEK