Dr. John Elder heads the United States' leading data mining consulting team, with offices in Charlottesville, Virginia; Washington, D.C.; and Baltimore, Maryland (www.datamininglab.com). Founded in 1995, Elder Research, Inc. focuses on investment, commercial, and security applications of advanced analytics, including text mining, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit scoring, market sector timing, and fraud detection. John obtained a B.S. and an M.E.E. in electrical engineering from Rice University and a Ph.D. in systems engineering from the University of Virginia, where he's an adjunct professor teaching Optimization or Data Mining. Prior… to 16 years at ERI, he spent five years in aerospace defense consulting, four years heading research at an investment management firm, and two years in Rice's Computational & Applied Mathematics Department.
Thomas Hill received his Vordiplom in psychology from Kiel University in Germany and earned an M.S. in industrial psychology and a Ph.D. in psychology and quantitative methods from the University of Kansas. He was associate professor (and then research professor) at the University of Tulsa from 1984 to 2009, where he taught data analysis and data mining courses. He also has been vice president for Research and Development and then Analytic Solutions at StatSoft Inc., where he has been involved for over 20 years in the development of data analysis, data and text mining algorithms, and the delivery of analytic solutions. Dr. Hill joined Dell through Dell's acquisition of StatSoft in April… 2014, and he is currently the Executive Director for Analytics at Dell's Information Management Group. Dr. Hill has received numerous academic grants and awards from the National Science Foundation, the National Institute of Health, the Center for Innovation Management, the Electric Power Research Institute, and other institutions. He has completed diverse consulting projects with companies from practically all industries and has worked with the leading financial services, insurance, manufacturing, pharmaceutical, retailing, and other companies in the United States and internationally on identifying and refining effective data mining and predictive modeling solutions for diverse applications. Dr. Hill has published widely on innovative applications for data mining and predictive analytics. He is the author (with Paul Lewicki, 2005) of Statistics: Methods and Applications, the Electronic Statistics Textbook (a popular on-line resource on statistics and data mining), a co-author of Practical Text Mining and Statistical Analysis for Non-Structured Text Data Applications (2012); he is also a contributing author to the popular Handbook of Statistical Analysis and Data Mining Applications (2009).
Dr. Robert Nisbet was trained initially in Ecology and Ecosystems Analysis. He has over 30 years' experience in complex systems analysis and modeling, most recently as a Researcher (University of California, Santa Barbara). In business, he pioneered the design and development of configurable data mining applications for retail sales forecasting, and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications Insurance, Banking, and Credit industries. In addition to data mining, he has expertise in data warehousing technology for Extract, Transform, and Load (ETL) operations, Business Intelligence reporting, and data quality analyses. He is lead author of the "Handbook of… Statistical Analysis & Data Mining Applications" (Academic Press, 2009), and a co-author of "Practical Text Mining" (Academic Press, 2012). Currently, he serves as an Instructor in the University of California, Irvine Predictive Analytics Certification Program, teaching online courses in Effective Data preparation, and co-teaching Introduction to Predictive Analytics.