Professor Provost studies data mining, knowledge systems, and machine learning and about their alignment with business problems. He teaches graduate courses on information technologies and how they affect business and business strategy.
Prof. Provost is Editor-in-Chief of the journal Machine Learning. He won IBM Faculty Awards in 2000 and in 2001 for outstanding research in data mining and machine learning. He was elected as a founding board member of the International Machine Learning Society. He is a member of the editorial boards of the Journal of Machine Learning Research (JMLR) and the Journal of Artificial Intelligence Research (JAIR). In 2001, he co-chaired the program of the premier data mining conference (KDD-2001).
His special issue of the journal Data Mining and Knowledge Discovery, on eCommerce and Data Mining, is available as a book. A Machine Learning editorial essay of his has been recommended reading for authors of applied research papers.
Professor Provost advises small and large companies who are interested in extracting useful knowledge from their data. He served as an expert witness on a recent case involving customer profiling and fraud detection systems. He also advises the U.S. Government (National Science Foundation, NASA, DARPA, National Research Council, White House Office of Science and Technology Policy) on policy and investments in data mining research.
He has applied advanced technologies to a variety of business problems, including fraud detection and customer contact management. For the last ten years, his research has focused on the issues involved with aligning data mining technologies with real-world problems, making several notable technical advances including robust modeling in the face of imprecision in the business environment, and profiling/monitoring on-line activity.
