New information systems faculty enhance the Kelley School
Feb 25, 2013
BLOOMINGTON, Ind. - Lucy Yan and Jingjing Zhang joined the Kelley School’s Operations and Decision Technologies Department early last fall as assistant professors of information systems.
Lucy Yan holds a Ph.D. in business administration from the Foster School of Business at University of Washington. Her research deals with the interface of social media and online healthcare systems, and her empirical studies investigate how online healthcare communities affect disease management and impact the healthcare industry.
Yan teaches Social Media: Basic Components and Digital Strategy at both the undergraduate and graduate levels, as well as a doctoral seminar in information systems. She recently won the 2012 Young Researcher Award at University of Maryland’s Workshop on Health Information Technology and Economics for her research on collaborative information sharing and patients’ health education.
One of Yan’s recent projects deals with health condition dynamics among patients with chronic diseases and provided the first measurable evidence that participation in online health communities improves patient health. Yan and her colleagues examined users of an online health community who interact with others dealing with the same conditions and found that such interactions provide information, emotional support and self-identification.
Yan also studied how networks are formed and evolve, and the transformative impact of online social media in reshaping patients’ health beliefs. Her research has provided insights for patients participating in online health communities, for system designers producing web-based applications, and for healthcare practitioners seeking to create better healthcare delivery systems.
Jingjing Zhang received her Ph.D. in business administration, with a specialization in information and decision sciences, from the Carlson School of Management at the University of Minnesota. Zhang teaches Data Management and Introduction to Data Mining in addition to a doctoral seminar on data mining and personalization.
Her research focuses on utilizing algorithmic techniques from computer science and theories from social sciences to understand and address emerging issues in recommender systems. Zhang’s recent research develops the concept of stability for evaluating the performance of recommender systems.
In her paper – Stability of Recommendation Algorithms – she introduced and formally defined the stability metric to measure the extent to which a recommendation algorithm provides predictions that are consistent with each other.
She also empirically evaluated the stability of several popular recommendation algorithms in a broad range of settings and found that some popular collaborative filtering recommendation algorithms are highly unstable. To address the issue of instability, Zhang then developed two general meta-algorithmic approaches to improve the stability of these techniques based on the notions of collective inference and bagging.
She presented the latter research, “Maximizing Stability of Recommendation Algorithms: A Collective Inference Approach,” at the 2011 Workshop on Information Technologies and Systems and won the Best Paper Award.