Applications closed

Mathematical modeling and optimization for large-scale distributed systems request for proposals


Facebook’s mission of bringing the world closer together requires supporting one of the world’s most complex backend systems. Operating infrastructure at scale with a high level of efficiency and stability raises new and interesting challenges, providing a natural laboratory for developing highly impactful solutions at the intersection of applied math, probability, computer science, and other quantitative disciplines. Our teams of research data scientists are at work onboarding the latest in machine learning and more general mathematical modeling technologies to drive continual improvement in automation and management of these large-scale systems.

To foster further innovation in this area and to deepen our collaboration with academia, Facebook is pleased to invite faculty to respond to this call for research proposals pertaining to the aforementioned topics. We anticipate awarding 4–6 proposals for $50,000 awards each. Payment will be made to the proposer’s host university or institution as an unrestricted gift.

Award Recipients

Stanford University

Nicholas Bambos

University of Minnesota Twin Cities

Mingyi Hong

Virginia Polytechnic Institute and State University

Liting Hu

George Washington University

Tian Lan

Oxford University

Ho-Yin Mak

Duke University

Danyang Zhuo

Applications Are Currently CLosed

Application Timeline

Launch Date

September 27, 2021


October 27, 2021

Winners Announced

December 2021

Areas of Interest

Proposals should advance mathematical techniques with a clear, practical application to one area of interest in backend systems. The range of mathematical techniques includes, but is not limited to, the following:

  • Convex and nonconvex optimization
  • Stochastic control and optimization
  • Graph algorithms
  • Online algorithms
  • Reinforcement learning and adaptive control
  • Machine learning
  • Dynamic programming
  • Decisions under uncertainty
  • Scheduling and assignment
  • Hidden Markov models
  • Monte Carlo and simulation methods
  • Multi-agent learning and game theory
  • Randomized search heuristics
  • Semi-supervised and weak supervised learning

Areas of interest can be categorized as the following:

1. Data center and hardware operations

Both complex and large-scale decision-making arise in Facebook data center operations, such as strategic capacity planning, resource allocation, operations and scheduling (staffing, hardware, reactive and proactive maintenance), disaster readiness, and impact control. Solving such problems efficiently and near-optimally is crucial to the efficiency and reliability of Facebook infrastructure. Uncertainty (such as service demand, supply chain issues, and hardware failures) poses a significant additional layer of such challenges. Facebook is interested in research endeavors that combine optimization with probabilistic, statistical, and machine learning techniques to support decision-making under uncertainty in these large-scale settings.

2.Cloud computing and cluster management

Large-scale distributed computing infrastructure provides a range of interesting challenges in resource management and scheduling. From instrumenting interdependent microservices to planning and executing batch data and machine learning pipelines, there are several opportunities to improve the efficiency and scalability of these systems. Coordination among processes or machines — such as in the case of distributed training and federated learning — present complex multi-agent optimization problems, and various systems performance tuning tasks such as regional data placement, traffic routing, and cache admission and eviction policies benefit from online and reinforcement learning and other techniques.

3.Application tuning and optimization

Many infrastructure systems make automated decisions in real time. These decisions adapt system configurations to specific user environments to optimize performance, reliability, and efficiency. Videos should be played at the highest quality level that a user’s network condition can sustain without frequent rebuffering. Detecting when a long-running data pipeline or machine learning workflow is making slow progress or stuck gives the option of restarting these processes so that they can continue their progress with fewer wasted resources. The decision-making should be able to accurately predict the environment, select optimal alternatives under sometimes conflicting objectives, and adapt the decisions by observing from past actions and results.


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 U.S. 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

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.
  • Personal data submitted with a proposal, including name, mailing address, phone number, and email address of the applicant and other named researchers in the proposal may be collected, processed, stored and otherwise used by Facebook for the purposes of administering Facebook’s RFP website, evaluating the contents of the proposal, and as otherwise provided under Facebook’s Privacy Policy.
  • 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.