Foster Provost

Biography

Foster Provost is Professor of Information Systems and Andre Meyer Faculty Fellow at New York University's Stern School of Business. Professor Provost studies data mining, machine learning, social network analysis and their alignment with business problems. He has won several awards, including the 2009 INFORMS Design Science award for social network-based marketing, IBM Faculty Awards for outstanding research in data mining and machine learning, a President's Award from NYNEX Science and Technology, Best Paper Awards from the ACM SIGKDD conference, and awards in SIGKDD's annual KDDCUP data mining competition.

Professor Provost's research focuses on the issues involved with aligning data mining technologies with real-world problems. Currently he is focusing on mining social-network data, such as networks of consumers, where the connections can be important for predictive modeling. He also is focusing on economic considerations in data mining and machine learning; technical strategies may change when cost and benefit information is taken into account. Professor Provost has applied data mining technologies to a variety of business problems, including on-line advertising, fraud detection, network diagnosis, targeted marketing, counterterrorism, and others.

Professor Provost recently retired as Editor-in-Chief of the journal Machine Learning after 6+ years. He is a member of the editorial boards of the Journal of Machine Learning Research (JMLR) and the journal Data Mining and Knowledge Discover. He was elected as a founding board member of the International Machine Learning Society. He advises businesses and U.S. government agencies on policy and investments in data mining research, and on practical issues in applying data mining and machine learning.

Professor Provost has a B.S. from Duquesne University in Physics and Mathematics and an M.A. and Ph.D. in Computer Science from the University of Pittsburgh.

Research Interests

• Mining social network data
• Privacy-friendly on-line advertising
• Micro-outsourcing for knowledge discovery and data quality
• Active & costly data acquisition for modeling
• Machine learning
• Behavior profiling

Courses Taught

• Data Mining for Business Intelligence
• Networks, Crowds and Markets

Academic Background

Ph.D., Computer Science, 1992
University of Pittsburgh

M.S., Computer Science, 1988
University of Pittsburgh

B.S., Physics & Mathematics, 1986
Duquesne University