Data Science
Gaining insights to deliver meaningful social interactions
Data scientists at Facebook conduct large-scale, global, quantitative research to gain deeper insights into how people interact with each other and the world around them.
Our findings directly inform decisions to improve people’s everyday experiences on Facebook, make it easier and more intuitive to use, and find ways to facilitate meaningful social interactions. Research efforts span a variety of disciplines, including computational social science, econometrics, operations research, market intelligence, survey science, and statistical computing. We employ a mixture of methods to accomplish our goals, including machine learning, field experiments, surveys, and information visualization. We also build scalable platforms for the collection, management, and analysis of data, and actively contribute our scientific findings to the academic research community.
For information about Core Data Science research at Facebook, visit the Core Data Science page.
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Latest Publications
All PublicationsEC - December 23, 2020
Matching Algorithms for Blood Donation
Duncan C. McElfresh, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P. Dickerson
CODE - November 20, 2020
Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment (Extended Abstract)
Mahnush Movahedi, Benjamin M. Case, Andrew Knox, Li Li, Yiming Paul Li, Sanjay Saravanan, Shubho Sengupta, Erik Taubeneck
CSCW - October 17, 2020
Country Differences in Social Comparison on Social Media
Justin Cheng, Moira Burke, Bethany de Gant
arXiv - October 9, 2020
Weights and Methodology Brief for the COVID-19 Symptom Survey by University of Maryland and Carnegie Mellon University, in Partnership with Facebook
Neta Barkay, Curtiss Cobb, Roee Eilat, Tal Galili, Daniel Haimovich, Sarah LaRocca, Katherine Morris, Tal Sarig
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Core Data Science at Facebook
3:09 | April 23, 2020
Open Source Projects
1:36 | August 16, 2019
Social Connectedness Index
1:11 | July 1, 2019
Social Good Projects
1:24 | July 1, 2019
Open Research Awards
View All Open Research AwardsRequest for proposals on sample-efficient sequential Bayesian decision making
With this RFP, we hope to deepen our ties to the academic research community by seeking out innovative ideas and applications of Bayesian optimization that further advance the field. We are committed to open source and will help awardees make the products of this RFP available to the public as part of BoTorch.