Facebook Fellowship Award Includes
- Tuition and fees paid for the academic year (up to two years/four semesters)
- $37K grant (one-time payment during each academic year)
- Up to $5,000 in conference travel support
- Paid visit to Facebook headquarters to 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
Applications Must Include
- 250-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
Applications will be accepted from students with research related to one of the following areas:
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.
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.
Networking and Connectivity
We would like to support students active in the research and development of scalable, fast, reliable, and efficient network infrastructure across all areas of connectivity, including: wired/wireless network domains such as data centers, backbones, peering, mobile core/backhaul, and access; WiFi to mmWave to optical; broadband connectivity for urban, peri-urban and rural society; the whole stack, from chip/interface/system hardware design to low-level firmware to distributed systems; and the whole network lifecycle, from planning/design, to provisioning/deployment, to monitoring/troubleshooting/visualization, to control stacks; including applications of machine learning in networking.
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.
Research outside the above: relevant work in areas that may not align with the research priorities highlighted above.