Announcing the winners of the 2020 request for proposals in applied statistics

In February 2020, Facebook launched the Statistics for Improving Insights, Models, and Decisions request for proposals (RFP). This RFP was designed to support research that addresses challenges in applied statistics that have direct applications for producing more effective insights and decisions for data scientists and researchers. Today, we’re announcing the recipients of these research awards.

View RFP

This program is a follow-up of the 2019 Statistics for Improving Insights and Decisions RFP, led by Facebook research teams working in Infra Data Science and Core Data Science. This year, we were particularly interested in the following topics:

  • Learning and evaluation under uncertainty
  • Statistical models of complex social processes
  • Causal inference with observational data
  • Efficient sampling and prevalence measurement
  • Design and analysis of experiments
  • Anomaly detection
  • Interpretability techniques for AI models

For descriptions of each topic, see the RFP application page.

“We are committed to enabling people to build safe and meaningful communities,” says Aude Hofleitner, Core Data Science Research Scientist Manager at Facebook. “This requires us to constantly innovate and push the state of the art of robust scientific methodologies. This commitment becomes all the more important in challenging economic and social times. We are looking forward to continuing to strengthen our engagements with the academic community and support research on these critical problems.”

“Facebook operates one of the largest and most sophisticated infrastructure among tech companies in the world,” says Xin Fu, Director of Research Data Science at Facebook. “We are excited about this opportunity to foster further innovation in research on statistical methodologies that can help improve the efficiency, reliability, and performance of large-scale infrastructure, from the detection of anomalies in services to advanced AI model interpretation techniques.”

We received 154 proposals from more than 107 universities. Thank you to all the researchers who took the time to submit a proposal, and congratulations to the award recipients.

Research award recipients

Adversarially robust temporal embedding models for social media integrity
Srijan Kumar and Duen Horng “Polo” Chau (Georgia Tech Research Corporation)

Learning from comparisons
Stratis Ioannidis, Deniz Erdogmus, and Jennifer Dy (Northeastern University)

Persistent activity mining in continually evolving networks
Danai Koutra (University of Michigan)

Personalized explanation of recommendations via natural language generation
Julian McAuley (University of California, San Diego)

Running experiments with unobservable outcomes: An invariant perspective
Andrea Montanari (Stanford University)

Towards transfer causal learning for average treatment effects
Bin Yu (University of California, Berkeley)

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