In April 2021, Facebook launched the 2021 Statistics for Improving Insights, Models, and Decisions request for proposals live at The Web Conference. Today, we’re announcing the winners of this award.

At Facebook, our research teams strive to improve decision-making for a business that touches the lives of billions of people across the globe. Making advances in data science methodologies helps us make the best decisions for our community, products, and infrastructure.

This RFP is a continuation of the 2019 and 2020 RFPs in applied statistics. Through this series of RFPs, the Facebook Core Data Science team, Infrastructure Data Science team, and Statistics and Privacy team aim to foster further innovation and deepen their collaboration with academia in applied statistics, in areas including, but not limited to, the following:

• Learning and evaluation under uncertainty
• Statistical models of complex social processes
• Causal inference with observational data
• Algorithmic auditing
• Performance regression detection and attribution
• Forecasting for aggregated time series
• Privacy-aware statistics for noisy, distributed data sets

The team reviewed 134 high-quality proposals and are pleased to announce the 10 winning proposals below, as well as the 15 finalists. Thank you to everyone who took the time to submit a proposal, and congratulations to the winners.

Research award winners

Breaking the accuracy-privacy-communication trilemma in federated analytics

Ayfer Ozgur (Stanford University)

Certifiably private, robust, and explainable federated learning

Bo Li, Han Zhao (University of Illinois Urbana-Champaign)

Experimental design in market equilibrium

Stefan Wager, Evan Munro, Kuang Xu (Stanford University)

Learning to trust graph neural networks

Claire Donnat (University of Chicago)

Negative-unlabeled learning for online datacenter straggler prediction

Michael Carbin, Henry Hoffmann, Yi Ding (Massachusetts Institute of Technology)

Non-parametric methods for calibrated hierarchical time-series forecasting

B. Aditya Prakash, Chao Zhang (Georgia Institute of Technology)

Privacy in personalized federated learning and analytics

Suhas Diggavi (University of California Los Angeles)

Reducing simulation-to-reality gap as remedy to learning under uncertainty

Mahsa Baktashmotlagh (University of Queensland)

Reducing the theory-practice gap in private and distributed learning

Ambuj Tewari (University of Michigan)

Robust wait-for graph inference for performance diagnosis

Ryan Huang (Johns Hopkins University)

Finalists

An integrated framework for learning and optimization over networks

Eric Balkanski, Adam Elmachtoub (Columbia University)

Auditing bias in large-scale language models

Soroush Vosoughi (Dartmouth College)

Cross-functional experiment prioritization with decision maker in-the-loop

Emma McCoy, Bryan Liu (Imperial College London)

Data acquisition and social network intervention codesign: Privacy and equity

Amin Rahimian (University of Pittsburgh)

Efficient and practical A/B testing for multiple nonstationary experiments

Nicolò Cesa-Bianchi, Nicola Gatti (Università degli Studi di Milano)

Empirical Bayes deep neural networks for predictive uncertainty

Xiao Wang, Yijia Liu (Purdue University)

Global forecasting framework for large scale hierarchical time series

Rob Hyndman, Christoph Bergmeir, Kasun Bandara, Shanika Wickramasuriya (Monash University)

High-dimensional treatments in causal inference

Kosuke Imai (Harvard University)

Nowcasting time series aggregates: Textual machine learning analysis

Eric Ghysels (University of North Carolina at Chapel Hill)

Online sparse deep learning for large-scale dynamic systems

Faming Liang, Dennis KJ Lin, Qifan Song (Purdue University)

Optimal use of data for reliable off-policy policy evaluation

Hongseok Namkoong (Columbia University)

Principled uncertainty quantification for deep neural networks

Tengyu Ma, Ananya Kumar, Jeff Haochen (Stanford University)

Reliable causal inference with continual learning

Sheng Li (University of Georgia)

Training individual-level machine learning models on noisy aggregated data

Martine De Cock, Steven Golob (University of Washington Tacoma)

Understanding instance-dependent label noise: Learnability and solutions

Yang Liu (University of California Santa Cruz)