Galit Shmueli is Distinguished Professor at the Institute of Service Science, National Tsing Hua University, Taiwan. She is co-author of the best-selling textbook Data Mining for Business Analytics, among other books and numerous publications in top journals. She has designed and instructed courses on forecasting, data mining, statistics and other data analytics topics at University of Maryland’s Smith School of Business, the Indian School of Business, National Tsing Hua University and online at Statistics.com.
Peter Bruce is the creator and president of The Institute for Statistics Education at statistics.com, the leading provider of online education in statistics; he also develops and markets statistical software. Previously he taught statistics at the University of Maryland, and served in the US Foreign Service.
Nitin Patel has been a member of the faculty at MIT's Sloan School and the Operations Research Center since 1995. Previously, he was a Professor at the Indian Institute of Management, Ahmedabad, and held visiting positions at Harvard, the University of Michigan, the University of Montreal and the University of Pittsburgh. Dr. Patel is a fellow of the American Statistical Association.
Mia Stephens is an Academic Ambassador with JMP, a division of SAS. She is a principal technical advocate for JMP in the academic markets and is responsible for seeking and supporting new customers.
Stephens is a long-time user of JMP in both her previous consulting and in teaching statisitcs. She has 15 years of experience in the application of statistical methods, project management, organizational development, organizational dynamics and team process. Her expertise includes Lean Six Sigma and Design for Six Sigma program deployment, training and support. She co-authored the book Visual Six Sigma.
Inbal Yahab is a faculty member at the Graduate School of Business Administration, Bar-Ilan University, Israel. Her interests lie on the interface between data mining and operations research.
Kenneth C. Lichtendahl Jr. is an Associate Professor of Business Administration at the University of Virginia’s Darden School of Business. He specializes in teaching data science to MBA students with R. He was recognized by The Case Centre as its 2015 Outstanding Case Teacher for his course Data Science in Business. His research focuses broadly on making, evaluating, and combining forecasts and has been published in leading academic journals such as Management Science.
Peter Gedeck is is at the forefront of the use of data science in drug discovery. He is a Senior Data Scientist at Collaborative Drug Discovery, which offers the pharmaceutical industry cloud-based software to manage the huge amount of data involved in the drug discovery process. Drug discovery involves the exploration and testing of huge numbers of molecule combinations, and much of that testing takes place analytically, hence the need for robust software to handle the data and provide a framework for analyzing it. Peter’s specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Prior to this, he worked for twenty years as a computational chemist in drug discovery at Novartis in the United Kingdom, Switzerland, and Singapore. His research interests include the application of statistical and machine learning methods to problems in drug discovery, clinical research and meta-analysis.