2021 Statistics for Improving Insights, Models, and Decisions request for proposals
Applications are now open
Launch Date April 21, 2021
Deadline June 30, 2021, 5:00 p.m. AOE
Winners Announced August 2021
Areas of Interest
Areas of interest include, but are not limited to, the following.
1. Learning and evaluation under uncertainty
- To protect people online and provide them with a meaningful experience, Facebook develops predictive models, which typically require training and evaluation data. Obtaining this data is oftentimes resource intensive, e.g., manual labeling, surveys and product interactions. It is also subject to noise and biases. We are interested in practical methodologies that address these challenges, estimate various biases, reduce the resources used for obtaining labels and produce calibrated estimations and predictions across cohorts of varying sizes.
2. Statistical models of complex social processes
- Facebook’s products help connect billions of people, and we often think of our products and systems as time-varying networks at scale. As these networks of connections evolve, various social processes also unfold on top of them: content spreads, social groups form and dissolve, people leverage their networks to organize events and support charitable causes, etc. Statistical models, both of the evolution of connection networks and of social processes on top of those networks, provide important input into Facebook’s efforts to design better products and to build safer and more meaningful communities. We invite proposals around the development, inference, and validation of such statistical models.
- Design and analysis of experiments - Facebook uses frameworks for randomized experiments to measure the benefits of the improvements we make to our products. We seek to maximize what we learn from these experiments by improving how they are designed and analyzed. We are interested in methodologies which allow us to extend or enhance the standard experimentation framework: variance reduction; measuring heterogenous or time-varying effects; estimating effects of many-valued or continuous-valued treatments; aggregating information across multiple related experiments; and correcting for selection bias when randomization is imperfect. We are also actively interested in research on adaptive experimentation such as Bayesian optimization and reinforcement learning.
3. Causal inference with observational data
- Often researchers at Facebook would like to answer causal questions even when it is not possible to conduct product tests. For instance, we may want to measure the effects of external events or understand the potential causes for anomalies that we observe in our data. Proposals in this area should improve our ability to suggest potential hypotheses for interesting phenomena or to credibly estimate the effects of known causes. Another application of interest is enabling FB to predict how key app performance and reliability metrics will change when upgrades are rolled out to the entire user base based on the treatment effect observed on a selected population during the test phase of the app. We are also interested in the related field of survey methodology - dealing with non-response or missing data.
4. Algorithmic Auditing
- Modeling and measuring feedback loop effects in ranking and recommender systems - ML systems make predictions that get reinforced by user feedback to obtain an accurate model of the user preferences. However, this feedback loop can influence user decisions, and narrow their interests, potentially resulting in outcomes that are suboptimal. We are interested in research that sheds light on identifying these feedback loops, and model the effects they may have on preferences amplification through theoretical or empirical techniques.
- Interpretability techniques for AI models - AI models have become increasingly complex, so it is important for both the AI practitioners and business stakeholders to have comprehensive evaluation and understanding of the AI algorithms. This can help simplify model development, and make sure we leverage AI responsibly. We are interested in interpretability techniques in (but not limited to) any of the following topics: feature attributions, aggregate attributions, feature interaction, accumulated local effects, and global/local surrogate.
5. Performance Regression Detection and Attribution
- In large-scale distributed systems, we often set up automated alerts to surface endpoints experiencing a sustained loss in performance, and investigate their underlying root cause by analyzing combinations and sub-partitions of dozens of potentially co-dependent factors gathered from both structured and unstructured data sets. We welcome submissions on improved statistical methods for monitoring and automation in this class of problems, ranging from detecting the origin node of a fault within a networked environment to tools which can aid in improving the efficiency of general root cause analysis investigations.
6. Forecasting for Aggregated Time Series
- Forecasting is a widely used tool for capacity planning, however it is frequently desirable to produce and analyze accurate forecasts at several layers of granularity. For example, we might want to forecast inbound traffic at the global, country, and regional level simultaneously. Emerging techniques in hierarchical forecasting and related domains offer new ways of producing forecasts which are not only consistent at different levels of aggregation, but also leverage latent information from covariance structures. We welcome submissions which extend our ability to forecast beyond well-known univariate time series methods.
7. Privacy-aware statistics for noisy, distributed data sets
- Statistical practice at Facebook can become more complex in the context of privacy-enhancing technologies. Differential privacy (DP) involves creating noisy datasets and frameworks such as federated analytics generate insights from distributed datasets. Developing appropriate statistical methods for these situations requires careful accounting for noise and can be limited by requirements for both secure and distributed computation. We are interested in research that improves the utility of noisy datasets produced via DP, as well as new statistical methods and algorithms for federated analytics.
Proposals should include
- A summary of the project (1-2 pages), in English, explaining the area of focus, a description of techniques, any relevant prior work, and a timeline with milestones and expected outcomes.
- A draft budget description (1 page) including an approximate cost of the award and explanation of how funds would be spent
- Curriculum Vitae for all project participants.
- Organization details; this will include tax information and administrative contact details
- Proposals must comply with applicable US and international laws, regulations and policies.
- Applicants must be current full-time faculty at an accredited academic institution that awards research degrees to PhD students.
- Applicants must be the Principal Investigator on any resulting award.
- Facebook cannot consider proposals submitted, prepared or to be carried out by individuals residing in, or affiliated with an academic institution located in, a country or territory subject to comprehensive U.S. trade sanctions.
- Government officials (excluding faculty and staff of public universities, to the extent they may be considered government officials), political figures, and politically affiliated businesses (all as determined by Facebook in its sole discretion) are not eligible.
Frequently Asked Questions
Do you typically limit the salary of the PI in the gift?
Most of the RFP awards are an unrestricted gift. Because of its nature, salary/headcount could be included as part of the budget presented for the RFP. Since the award/gift is paid to the university, they will be able to allocate the funds to that winning project and have the freedom to use as they need. All Facebook teams are different and have different expectations concerning deliverables, timing, etc. Long story short – yes, money for salary/headcount can be included. It’s up to the reviewing team to determine if the percentage spend is reasonable and how that relates to the decision if the project is a winner or not.
Should the proposal be double- or single-spaced? Is there any required/expected font?
We are flexible, but ideally proposals submitted are single-spaced, Times New Roman, 12 pt font.
What is the award cycle or when does the funding year begin and end?
Research awards are given year-round and funding years/duration can vary by proposal.
Can award funds be used to cover a researcher's summer salary while conducting research?
Yes, award funds can be used to cover a researcher’s salary.
Can you please explain the budget breakdown in more detail?
Budgets can vary by institution and geography, but overall research funds ideally cover the following: graduate or post-graduate students’ employment/tuition; other research costs (e.g., equipment, laptops, incidental costs); travel associated with the research (conferences, workshops, summits, etc.); overhead for research gifts is limited to 5%
We are working as co-PIs and are at the same institution. Is it possible to list both of our names as PI for an RFP proposal?
One person will need to be the primary PI (i.e., the submitter that will receive all email notifications); however, you’ll be given the opportunity to list collaborators/co-PIs in the submission form. Please note in your budget breakdown how the funds should be dispersed amongst PIs.
Terms & Conditions
Facebook’s decisions will be final in all matters relating to Facebook RFP solicitations, including whether or not to grant an award and the interpretation of Facebook RFP Terms and Conditions. By submitting a proposal, applicants affirm that they have read and agree to these Terms and Conditions.
- Facebook is authorized to evaluate proposals submitted under its RFPs, to consult with outside experts, as needed, in evaluating proposals, and to grant or deny awards using criteria determined by Facebook to be appropriate and at Facebook’s sole discretion. Facebook’s decisions will be final in all matters relating to its RFPs, and applicants agree not to challenge any such decisions.
- Facebook will not be required to treat any part of a proposal as confidential or protected by copyright, and may use, edit, modify, copy, reproduce and distribute all or a portion of the proposal in any manner for the sole purposes of administering the Facebook RFP website and evaluating the contents of the proposal.
- Neither Facebook nor the applicant is obligated to enter into a business transaction as a result of the proposal submission. Facebook is under no obligation to review or consider the proposal.
- Feedback provided in a proposal regarding Facebook products or services will not be treated as confidential or protected by copyright, and Facebook is free to use such feedback on an unrestricted basis with no compensation to the applicant. The submission of a proposal will not result in the transfer of ownership of any IP rights.
- Applicants represent and warrant that they have authority to submit a proposal in connection with a Facebook RFP and to grant the rights set forth herein on behalf of their organization. All awards provided by Facebook in connection with this RFP shall be used only in accordance with applicable laws and shall not be used in any way, directly or indirectly, to facilitate any act that would constitute bribery or an illegal kickback, an illegal campaign contribution, or would otherwise violate any applicable anti-corruption or political activities law.
- Awards granted in connection with RFP proposals will be subject to terms and conditions contained in the unrestricted gift agreement (or, in some cases, other mechanisms) pursuant to which the award funding will be provided. Applicants understand and acknowledge that they will need to agree to these terms and conditions to receive an award.
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