Galit Shmueli




Galit Shmueli is Tsing Hua Distinguished Professor at the Institute of Service Science, National Tsing Hua University, Taiwan. She is also Director of the Center for Service Innovation & Analytics at NTHU's College of Technology Management. Between 2011-2014 she was the SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business.

Dr. Shmueli’s research focuses on statistical and data mining methodology with applications in information systems and healthcare. She authors six books, including the popular textbook Data Mining for Business Intelligence and over 60 publications in peer-reviewed journals and books, including the top journals Management Science, Journal of the American Statistical Association, Journal of the Royal Statistical Society, Information Systems Research, MISQ, Marketing Science, Statistical Science and Technometrics. She has presented her work at multiple venues in the US and internationally.

After graduating from the Technion – Israel Institute of Technology in 2000, Dr. Shmueli was Visiting Assistant Professor at Carnegie Mellon University’s Statistics Department, where she first became involved in early biosurveillance research and efforts. Dr. Shmueli’s work in biosurveillance, BioSense Initiative to Improve Early Event Detection, in collaboration with the Johns Hopkins Applied Physics Lab received a 3-year award from the Centers for Disease Control & Prevention. Her co-authored 2010 paper “Statistical Challenges Facing Early Outbreak Detection in Biosurveillance” was the featured article in Technometrics. She has also been involved in data mining methods for improving kidney allocation.

Dr. Shmueli’s work in information systems started in 2002, when joining University of Maryland’s Robert H Smith School of Business. Her work focuses on electronic commerce and online auctions. In 2004, Dr. Shmueli co-founded the now annual symposium Statistical Challenges in eCommerce Research. Her research focuses on applying novel statistical methodology and adapting existing methods for modern data structures. Her papers “To Explain or To Predict?” and “Predictive Analytics in Information Systems Research” have attracted much attention and won several research and “best paper” awards.

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Oct 2nd 2017

Learn a scientific and practical approach for creating and evaluating forecasting solutions. Companies, governments and other organizations now collect and analyze huge amounts of data about suppliers, clients, employees, citizens, transactions, and much more. There are a number of ways organizations can use this data. Business analytics uses this data to make better decisions and forecasting is an arm of this predictive analytics. Forecasting especially can provide a powerful toolkit for analyzing time series data.

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