Faculty

Institution

Unit

Venue

Foster J. Provost

Professor of Technology Operations and Statistics Professor of Data Science

New York University

Affiliations

Arts & Science

Computer Science

Courant Institute of Mathematical Sciences

Computer Science

NYU Abu Dhabi

Division of Science

Foster J. Provost is currently serving as an Arts & Science Professor of Technology Operations and Statistics Professor of Data Science at New York University.

Honors

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Experiences

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Education

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Foster J. Provost

New York University

Professor of Technology Operations and Statistics Professor of Data Science

You can click a keyword to filter the displayed items

machine learning data science computer science data mining social sciences statistics knowledge discovery social science research information science big data data classification machine learning research artificial intelligence advertising social network analysis pattern recognition active learning decision science information management online advertising classification method fraud detection predictive modeling information technology data security supervised learning computational intelligence data-driven science data-driven decision-making data quality document classification information extraction data acquisition dimensionality reduction class distribution tree induction networked data active feature-value acquisition data-driven decision making empirical study electronic commerce semantic evaluation statistical methodology case studies ontology (information science) logistic regression information economics causality information discovery transfer learning intelligent information retrieval information filtering system collaborative filtering fault tolerance object categorization matrix factorization classifier performance cost distributions classifier learning classification models intelligent assistance audience selection on-line brand advertising inductive policy confidence bands roc curves knowledge management management science telecommunication networks human-computer interaction online information cost-sensitive machine learning decision tree learning methodology comparison statistical science information technology management business analytics applied artificial intelligence intelligent analytics technology multidisciplinary ai

Notable Scholarly Works

Notable Federal Grants

Collaborators

Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete?. arXiv (Cornell University) (2025)

Connor DouglasFoster J. ProvostArun Sundararajan

cooperationmachine learningalgorithmic game theoryalgorithmic biasnaive algorithmic collusionbandit learnerscompetition (economics)exploration v exploitationreinforcement learningmulti-agent learninggame theorycognitive sciencecomputer sciencebehavioral sciences

The Impact of Cloaking Digital Footprints on User Privacy and Personalization. Big Data (2025)

Sofie GoethalsSandra MatzFoster ProvostDavid MartensYanou Ramon

technologyinformation hidingdata hidinginformation securityuser privacy

Who’s Watching Tv?. Information Systems Research (2023)

Jessica ClarkJean-François PaiementFoster Provost

television studiescommunicationmass communicationinformation scienceinformation societyinformation managementbehavioral sciences

A Comparison of Methods for Treatment Assignment With an Application to Playlist Generation. Information Systems Research (2022)

Carlos Fernández-LoríaFoster ProvostJesse AndertonBenjamin CarterettePraveen Chandar

public healthinformation scienceepidemiologysurvival analysisstatisticstreatment assignment

Ethics and Interventions: A Commentary on How to “Improve” Prediction Using Behavior Modification. International Journal of Forecasting (2022)

Foster Provost

behavior analysisethicsbehavioral researchbehavioral experimentsbehavior predictionbehavior modificationforecastingbehavior change (public health)behavioral sciences

Explaining Data-Driven Decisions Made by AI Systems: The Counterfactual Approach. MIS Quarterly (2022)

Carlos Fernández-LoríaFoster ProvostXintian Han

ai systemsmanagementdecision sciencedata-driven decision-makingartificial intelligencedata-driven decisions

Rejoinder to “Causal Decision Making and Causal Effect Estimation Are Not the Same…and Why It Matters”. INFORMS Journal on Data Science (2022)

Carlos Fernández-LoríaFoster Provost

causal effect estimationdata science

Causal Classification: Treatment Effect Estimation vs. Outcome Estimation. Journal of Machine Learning Research (2021)

treatment effectscausal inferencetreatment effect estimationstatisticsstatistical methodologycausal models

A Comparison of Instance-Level Counterfactual Explanation Algorithms for Behavioral and Textual Data: Sedc, Lime-C and Shap-C. Advances in Data Analysis and Classification (2020)

Yanou RamonDavid MartensFoster ProvostTheodoros Evgeniou

textual datadata miningclassification methodcomparative methodologynatural language processingstatisticsdata classificationdata sciencemachine learningbehavioral sciences

Combining Observational and Experimental Data to Improve Large-Scale Decision-Making. International Conference on Information Systems (2020)

Carlos Fernández-LoríaFoster J. Provost

information scienceexperimental dataastronomydata sciencelarge-scale decision-making

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Foster J. Provost

Professor of Technology Operations and Statistics Professor of Data Science

New York University

Affiliations

Arts & Science

Computer Science

Courant Institute of Mathematical Sciences

Computer Science

NYU Abu Dhabi

Division of Science

Foster J. Provost is currently serving as an Arts & Science Professor of Technology Operations and Statistics Professor of Data Science at New York University.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Foster J. Provost

New York University

Professor of Technology Operations and Statistics Professor of Data Science

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete?. arXiv (Cornell University) (2025)

Connor DouglasFoster J. ProvostArun Sundararajan

The Impact of Cloaking Digital Footprints on User Privacy and Personalization. Big Data (2025)

Sofie GoethalsSandra MatzFoster ProvostDavid MartensYanou Ramon

technologyinformation hidingdata hidinginformation securityuser privacy

Who’s Watching Tv?. Information Systems Research (2023)

Jessica ClarkJean-François PaiementFoster Provost

television studiescommunicationmass communicationinformation scienceinformation societyinformation managementbehavioral sciences

A Comparison of Methods for Treatment Assignment With an Application to Playlist Generation. Information Systems Research (2022)

Carlos Fernández-LoríaFoster ProvostJesse AndertonBenjamin CarterettePraveen Chandar

public healthinformation scienceepidemiologysurvival analysisstatisticstreatment assignment

Ethics and Interventions: A Commentary on How to “Improve” Prediction Using Behavior Modification. International Journal of Forecasting (2022)

Foster Provost

behavior analysisethicsbehavioral researchbehavioral experimentsbehavior predictionbehavior modificationforecastingbehavior change (public health)behavioral sciences

Explaining Data-Driven Decisions Made by AI Systems: The Counterfactual Approach. MIS Quarterly (2022)

Carlos Fernández-LoríaFoster ProvostXintian Han

ai systemsmanagementdecision sciencedata-driven decision-makingartificial intelligencedata-driven decisions

Rejoinder to “Causal Decision Making and Causal Effect Estimation Are Not the Same…and Why It Matters”. INFORMS Journal on Data Science (2022)

Carlos Fernández-LoríaFoster Provost

causal effect estimationdata science

Causal Classification: Treatment Effect Estimation vs. Outcome Estimation. Journal of Machine Learning Research (2021)

treatment effectscausal inferencetreatment effect estimationstatisticsstatistical methodologycausal models

A Comparison of Instance-Level Counterfactual Explanation Algorithms for Behavioral and Textual Data: Sedc, Lime-C and Shap-C. Advances in Data Analysis and Classification (2020)

Yanou RamonDavid MartensFoster ProvostTheodoros Evgeniou

textual datadata miningclassification methodcomparative methodologynatural language processingstatisticsdata classificationdata sciencemachine learningbehavioral sciences

Combining Observational and Experimental Data to Improve Large-Scale Decision-Making. International Conference on Information Systems (2020)

Carlos Fernández-LoríaFoster J. Provost

information scienceexperimental dataastronomydata sciencelarge-scale decision-making

1
2
3
4
5
18
10 / page

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multidisciplinary ai

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