Supporting PhD students engaged in innovative research
The Facebook Fellowship is a global program designed to encourage and support promising doctoral students who are engaged in innovative and relevant research in areas related to computer science and engineering at an accredited university.
The program is open to students in any year of their PhD study. We also encourage people of diverse backgrounds and experiences to apply, especially those from traditionally under-represented minority groups. Applications are evaluated based on the strength of the student’s research statement, publication record and recommendation letters.
Winners of the Fellowship are entitled to receive two years of paid tuition and fees, a $42,000 annual stipend to cover living and conference travel costs, a paid visit to Facebook headquarters for the annual Fellowship Summit, and various opportunities to engage with Facebook researchers.
Facebook Fellowship Program
Applications for the 2021-2022 academic year will open in August 2020.
Applications Open Aug 10, 2020, 12:00am PST
Applications Close Oct 2, 2020, 11:59pm PST
Winners Announced Jan 2021
Fellows - Emerging ScholarsSee all Fellows
Devendra Singh Chaplot
Carnegie Mellon University
Computer Vision - 2020
Machine Learning - 2020
University of Wisconsin–Madison
Structured Data Stores - 2020
University of Illinois, Urbana-Champaign
Programming Languages - 2020
University of Maryland
Economics & Computation - 2020
Instagram/Facebook App Well-being and Safety - 2020
The Facebook Fellowship Summit
As part of the program, current Fellows are invited to Facebook's headquarters in Menlo Park for the annual Fellowship Summit. At the Summit, Fellows can engage with other winners, share their current research, speak with Facebook researchers and teams, and learn more about research at Facebook.
Facebook Fellowship Award Includes
- Tuition and fees paid for the academic year (up to two years/four semesters)
- A $42,000 annual stipend to cover living and conference travel costs
- Paid visit to Facebook headquarters for the annual Fellowship Summit
- Applicants must be full-time PhD students currently involved in on-going research who are enrolled in an accredited university in any country
- Students' work must be related to one or more relevant disciplines (see research areas below)
- Students must be enrolled during the academic year(s) the Fellowship is awarded
- Students should not apply for Facebook Fellowships if they are actively being funded by Facebook through some other sponsorship or collaboration and/or if they are actively being supervised (or co-supervised) by a Facebook researcher. If in doubt, please email firstname.lastname@example.org.
Applications Must Include
- 500-word research summary which clearly identifies the area of focus, importance to the field and applicability to Facebook of the anticipated research during the award (reference the research areas below)
- Resume or CV, with email, phone and mailing address, and applicable coursework noted
- Two letters of recommendation. Please provide reference email addresses. One reference must be from an academic advisor
We would like to support students who are working on novel techniques in statistical modeling and inference. Areas of research include but are not limited to inference in high dimensions, causal inference, graphical models, multi-armed bandits, sparsity and compressed sensing, change detection, forecasting and time series analysis, optimization, regression, classification, clustering, graph partitioning, entity linkage and entity deduplication. Applications of interest include but are not limited to user modeling, detecting violations, experimentation, surveys and efficient sampling.
AR/VR Photonics and Optics
We would like to support students that are excited about developing technology in Photonics and Optics that can be applied to VR and AR visual systems. Topics of interest include functional planar optical elements, highly efficient light sources, optical and photonic imaging devices, unique optical materials and structures.
AR/VR Privacy and Ethics
We would like to support students whose research sits at the intersection of future technologies (augmented reality and virtual reality, specifically) and privacy and/or ethics. We are also considering applicants who have established research programs in AR, VR, or video presence technologies more broadly (either as a field of focus or as methodological medium). This includes but is not limited to research on the integration of emerging technologies in everyday life and in the workplace.
Blockchain and Cryptocurrency
We would like to support students working on novel techniques for building decentralized databases of programmable resources. Topics of interest include all areas related to high assurance, scalability and security of the technology, in particular blockchain, smart contracts, financial technologies and payments, transactions, distributed systems, concurrency and formal methods.
Computational Social Science
We would like to support students who are working on advancing research in the social sciences with computational approaches. Topics of interest include but are not limited to theoretic and practical models and analysis of social networks, information cascades and influence, causal inference in networks, computer-mediated communication in dyads and groups, norms and trust, political participation, location aware social networks and mobility, social processes after natural disasters and crises, well-being, social support, and mental health support.
We would like to support students who are working on advancing the state-of-the-art in computer graphics and efficient real-time rendering for augmented and virtual reality. Topics of interest include but are not limited to applications of machine learning methods, ray tracing and ray casting hardware, physically based shading, geometry processing and compression, image and video compression, perceptual rendering, high quality avatars, global illumination, scene prefiltering, and rendering complexity reduction.
We would like to support students who are working on advancing the state-of-the-art in computer vision. Topics of interest include, but are not limited to image and video recognition (classification, detection, and segmentation), vision and language (visual question answering and visual dialog), visual reasoning (forward prediction, understanding physics, and understanding affordance), large-scale and weakly supervised learning, and understanding humans (pose estimation and action recognition).
Compute Storage and Efficiency
We would like to support students who are working on novel techniques for improving the efficiency of large scale systems such as databases, file systems, caching systems, pub/sub systems. This includes novel exploring techniques to shift computation, memory and storage with the goal of optimizing power consumption and/or cost.
We would like to support students working on a broad set of topics related to all kinds of distributed systems, including but not limited to fault tolerance, reliability, system management, scale, performance, efficiency, and security.
Economics and Computation
We support students who are passionate about applied or theoretical work in the areas of game theory, optimization, operations management and econometrics. Example research topics of particular relevance include ad auction design, mechanism design for social good, applications of combinatorial and convex optimization at large scale, and the intersection of econometrics and machine learning, but we encourage and welcome applications from researchers doing work on other topics in the disciplines above.
Instagram/Facebook App Well-being and Safety
We would like to support students who are working on understanding how experiences with social technology play a role in the well-being and safety of communities and societies. This includes, but is not limited to, research that will help us understand problematic issues facing our communities, develop better content policies, assess possible interventions to protect our communities, or identify the mechanisms (e.g., social support, social comparison) through which technology usage directly impacts well-being.
We would like to support students who are working on advancing the state-of-the-art in machine learning. Topics of interest include but are not limited to reinforcement learning, deep learning, causality, non-convex optimization, multi-task learning, curriculum learning, learning embeddings and metrics, speech recognition, cost-sensitive learning, and structured prediction.
Natural Language Processing
We would like to support students with topics of interest that include: machine translation, multilingual learning, representation learning, named entity recognition, text classification, semantic parsing, summarization, dialog systems.
We would like to support students active in the research and development of scalable, performant, reliable, efficient, and secure wired network infrastructure across data centers, the wide area (IP and optical), and internet peering. The networking technologies span the entire networking stack (L1-L7); range from chip/interface/system hardware design to distributed systems for control, data, and management planes; and cover the whole network lifecycle, from planning/design/analytics, to provisioning/deployment/migration, to monitoring/troubleshooting/visualization. This also includes applications of related disciplines such as machine learning, optimization and algorithmic theory, and formal verification to the networking domain.
Privacy and Data Use
We would like to support students who are working on understanding privacy and data use, specifically how to make data more transparent to users and give people more control of their data. This includes, but is not limited to, research on transparency in online behavioral advertising.
Applications are welcome from students who are interested in the design and implementation of programming languages and related tools. Topics of interest include, but are not limited to: type systems, static analysis, optimizing compilation, runtimes, formal specification and verification, and high-level support for features such as concurrency, data privacy, control of side effects, and probabilistic and differentiable programming.
We would like to support students with established proficiency in the field and passion about solving complex security challenges. Topics of interest include but are not limited to: systems, software, and network security; privacy; cryptography; malware; abuse detection and mitigation; authentication and authorization.
Social and Economic Policy
We would like to support students who are working on advancing policy relevant research in social science, policy analysis, and legal analysis fields. Individuals pursuing research using both qualitative and quantitative methods are encouraged to apply. Topics of interest include but are not limited to online child and adult safety, relationship between online communities and international security and foreign policy issues, the role of social media and technology in political, economic, social systems, and the potential impacts of information (or misinformation) on health and well-being.
Spoken Language Processing and Audio Classification
We would like to support students working in speech and audio processing, particularly those advancing the state of the art in human-computer interaction, human-human interaction, and video content understanding. Topics of interest include but are not limited to speech recognition and synthesis, spoken language understanding, dialog systems, auditory scene analysis, acoustic event detection, audio-visual modeling and sentiment analysis.
Structured Data Stores
We welcome applications from students working on novel techniques for improving the efficiency and reliability of large scale systems such as databases, key/value stores, file systems, caching systems, pub/sub systems, and security for storage. This includes exploring novel techniques to shift work between CPUs, memory, and storage with the goal of optimizing power consumption, cost, or developer efficiency.
Systems for Machine Learning
We welcome applications from students working on interdisciplinary research to support machine learning at scale. Research topics of interest include, but are not limited to, hardware and software techniques to improve machine learning inference and training in the datacenter or at the edge. Examples of projects include research targeting machine learning hardware specialization, compiler technologies for deep learning platforms, and techniques for distributed training/learning. We are also interested in research focused on workload characterization and performance analysis of real-world machine learning applications.
Frequently Asked Questions
How do I apply?
The 2021 application will open August 10, 2020.
Are students studying outside of the United States eligible to apply?
Yes. The fellowship is open to students at all universities both in the United States and in other countries.
Do I need to be a PhD student to be eligible to apply?
Yes. You must be enrolled in a PhD program full-time to apply for a Facebook Fellowship.
When does the Facebook Fellowship award start?
The award funds are delivered during the Fall term of the awarded year(s) and conclude at the end of the Spring term. Stipend payment(s) are sent once a tuition invoice is received for the respective Fall term each year. Travel funds must be completed by the end of the school year (typically the Spring term).
If I currently have funding from somewhere else, am I still eligible to receive the Facebook Fellowship?
If you are selected to receive the award, you may be asked to decline funding (i.e. research projects, fellowships, internships, contractor work, employment) from another company or industry competitor. Usually funding from your university, Facebook internship, or related to a teaching requirement is allowed. Mandatory military employment or Facebook contractor positions are reviewed on a case-by-case basis.
Do I need to provide my recommendation letters with my application?
You will be asked to provide the names and email addresses of your two references. They will receive an email prompting them to upload their recommendation letters. If your application is a finalist, we may contact your references for confirmation.
What is the deadline for applications?
All application materials are due at the application close date. Your references will have additional time to submit their letters of recommendation.
If I only have 1 year left of school, can I still apply?
Yes, you may still apply. You will just receive the tuition and stipend while you are in school.
Do both letters of recommendation need to come from a Professor?
One letter needs to come from your PhD advisor, the other may come from another professor or an industry contact.
Where will the fellowship money be sent? The school or myself?
Tuition money will be paid directly to the school, the stipend will be sent directly to the fellow, unless otherwise specified. Stipends are taxable based on local law.
Does the 500 word limit for the research summary include references to papers mentioned, or can references go on an additional page?
References may be included with your CV/resume as a single uploaded file.
I am a Facebook AI Resident student, do I qualify for a Facebook Fellowship?
No, Facebook AI Resident students are ineligible for a Facebook Fellowship.
Will I be taxed on my Fellowship stipend award?
Yes. Your stipend award is taxable and based on local tax laws (where you are a resident). You will be asked to submit necessary tax information once accepting your award. Your tuition is sent directly to your university and is not considered personal income. Starting with 2020 winners, the $5,000 travel award will be bundled with the $37,000 stipend (which is taxable). The total stipend will be $42,000.
Am I eligible if I'm currently being supervised (or co-supervised) by a Facebook researcher?
Students should not apply for Facebook Fellowships if they are actively being funded by Facebook through some other sponsorship or collaboration and/or if they are actively being supervised (or co-supervised) by a Facebook researcher. If in doubt, please email email@example.com.