Foster J. Provost

Biography

FOSTER PROVOST is Ira Rennert Professor of Entrepreneurship and Information Systems and Director, Fubon Center, Data Analytics & AI, at the Stern School of Business at New York University. He is also Professor of Data Science at NYU and former interim Director of the NYU's Center for Data Science. He previously was Editor-in-Chief of the journal Machine Learning and was elected as a founding board member of the International Machine Learning Society. From 2019-2022 Foster was a Distinguished Scientist (full-time) for real estate tech giant Compass. Foster stands out in data science for having made substantial contributions across research, practical applications, and business thought leadership.

Foster's research on data science and machine learning has won many awards, including (among others) the 2020 ACM SIGKDD Test of Time Award, the 2017 European Research Paper of the Year, Best Paper awards in the top research venues across four decades, the 2009 INFORMS Design Science Award from the top professional society for operations research, IBM Faculty Awards, and a President’s Award from NYNEX Science and Technology (now Verizon).

For more than 25 years, Prof. Provost has helped leaders in business and government understand how data science, artificial intelligence, and machine learning technologies can add value. His book Data Science for Business is required reading in many of the top business schools, and was listed as one of Fortune Magazine's "must read books for MBAs." He has designed AI/machine learning systems for some of the largest companies in the world and worked with the DoD on the application of AI/machine learning to counter-terrorism.

Foster also has had substantial experience helping to found startups. He was the founding chief scientist for adtech data science powerhouse Dstillery, designing the original machine learning algorithms and building the founding data science teams for both Media6Degrees and Everyscreen Media (which merged to form Dstillery). He also was a coFounder of Detectica (acquired by Compass), of Belgium's Predicube (acquired by Var), and Integral Ad Science.

Research Interests

• Machine Learning & AI
• Human-AI Integration
• AI & Data Science Strategy
• Causal Prediction
• Mining (Social) Network Data
• Advertising and AdTech

Courses Taught

• Data Mining, Managerial
• Data Science for Business Analytics
• Data Science Research Seminar
• Introduction to Business Analytics

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

Awards & Appointments

2020 ACM SIGKDD Test of Time Award
Best Student Paper Award Runner-up, WISE 2020
Finalist, 2019 INFORMS Case Competition
Finalist, 2018 Algorithm for Change AI/ML Competition
European Research Paper of the Year 2017
Best Paper Award, Information Systems Research, 2016
2014 NYU/Stern MSBA Best Teacher Award
Nominated for 2014 NYU/Stern Professor of the Year by the MBA student body
Nominated for 2013 NYU/Stern Professor of the Year by the MBA student body
Best Paper Award, ACM SIGKDD 2012, Industry Track
The INFORMS Design Science Award, 2009
Best Paper Award Runner-up, ACM SIGKDD 2008
IBM Faculty Awards, 2000 & 2001
Best Paper Award, KDD 1997
President's Award, NYNEX Science and Technology (now Verizon)
Former interim Director, NYU Center for Data Science
NYU Venture Fund Investment Review Board

Selected Publications

C. Fernandez & F. Provost. (2021)
“Causal Classification: Treatment effect estimation vs. outcome estimation.”
Accepted (minor revisions), Journal of Machine Learning Research

D. Chen, S. Fraiberger, R. Moakler & F. Provost (2017)
Privacy, transparency and control for predictive analytics from massive fine-grained personal data
Big Data 5(3): 197-212

Provost, F., D. Martens, and A. Murray (2015)
Finding Mobile Consumers with a Privacy-Friendly Geo-Similarity Network
Information Systems Research 26(2), 243-265. Best Paper Award – ISR 2016; INFORMS President's Pick.

Ipeirotis, P., F. Provost, V. Sheng, J. Wang (2014)
Repeated Labeling Using Multiple Noisy Labelers
Data Mining and Knowledge Discovery 28(2), 402-441

F. Provost and T. Fawcett (2013)
Data Science and its Relation to Big Data and Data-driven Decision Making.
Big Data 1(1), 51-59

F. Provost and T. Fawcett (2013)
Data Science and its Relation to Big Data and Data-driven Decision Making
Big Data 1(1), 51-59