Galit Shmueli is Tsing Hua Chair Professor at the Institute of Service Science at the College of Technology Management, National Tsing Hua University, Taiwan. Before joining NTHU, she was the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business, and tenured Associate Professor at University of Maryland's Smith School of Business. She is the inaugural editor-in-chief of the INFORMS Journal on Data Science.
Dr. Shmueli's research focuses on statistical and data mining methods for contemporary data structures, with a focus on statistical strategy – issues related to how data analytics are used in scientific research. Her main fields of application are information systems and healthcare, with a focus on human behavior.
Dr. Shmueli's research has been published in the statistics, information systems, management, and marketing literature. She has authored over 100 journal articles, books, and book chapters, and is on the editorial boards of several journals. She presents her work nationally and internationally.
After receiving her PhD in Statistics from the Israel Institute of Technology in 2000, Dr. Shmueli was visiting faculty at Carnegie Mellon University's statistics department, where she became involved in the early research in biosurveillance. Between 2002-2012, she was faculty at University of Maryland's Smith School of Business, where she initiated with Dr. Jank the new research field "statistical methods in eCommerce" on the interface of statistics and information systems. This now highly active interdisciplinary field has generated important advancements in empirical research. Between 2011-2014, Dr. Shmueli joined the Indian School of Business in Hyderabad, India as the SRITNE Chaired Professor of Data Analytics, where she spearheaded multiple data analytics initiatives. Dr. Shmueli joined the Institute of Service Science at National Tsing Hua University in 2014 as Tsing Hua Distinguished Professor.
Dr. Shmueli has been designing and instructing various business analytics courses, including data mining, forecasting, visualization, data collection, and more. She is passionate about teaching data analytics and improving their application in the business environment. Her popular co-authored textbook Data Mining for Business Analytics is widely adopted in business schools worldwide.