In the past few years, we have seen an explosion of interest in topics at the intersection of programming languages and machine learning. This is not a coincidence: there has been a growth in real-world applications that need probabilistic thinking. Additionally, the community has realized that probabilistic methods play a genuinely useful role in program analysis – for example, in ranking of deduced facts in static analyses, in type reconstruction, and in general to build explainable generative models. Machine learning techniques such as efficient automatic differentiation are no longer esoteric, and form the basis for popular deep learning frameworks such as Tensorflow and PyTorch and differentiable programming languages like Swift For Tensorflow and others. Deep learning also relies on compiler and code generation techniques to target GPUs and special-purpose accelerator hardware.
At Facebook, we are doing forward-looking research, as well as putting into production concrete results from several of these threads. We introduced HackPPL, which extends our internal PHP dialect into a full-fledged probabilistic programming language, and are creating extensions to Python to eliminate string-based API patterns. We have started various language-centric projects around acceleration and differentiable programming. We also have a portfolio of projects in the “big code” space, exploring several topics such as code search and recommendation, automatic bug fixing, and program synthesis using machine learning. Together, this work hopes to have impact across all of Facebook’s infrastructure.
To foster further innovation in these topics, and to deepen our collaboration with academia, Facebook is pleased to invite faculty and graduate students to respond to this call for research proposals pertaining to the aforementioned topics. We anticipate awarding a total of ten awards, each in the $50,000 range. Payment will be made to the proposer’s host university as an unrestricted gift.
We are interested in proposals that address fundamental problems at the intersection of machine learning, programming languages and software engineering, including:
- Differentiable programming
- Probabilistic programming
- Languages and tools for data science
- Programming tools built using “big code”
- Applications of machine learning to optimize systems and human workflows
Proposals should include
- Names of the researcher(s) involved in the proposed work with links to their DBLP and/or Google Scholar™ pages
- Host academic research institution with administrative and financial information
- Clear and concise statement of the scientific contribution and routes to eventual deployment (2 pages) and a proposed budget description (1 page) uploaded in a single PDF file
- Indication of any previous or current connections/collaborations with Facebook (in which case, please name any Facebook contacts)
Timing and dates
- Applications are now open. Deadline to apply is Friday, March 1st at 5:00 pm PST.
- Notifications will be sent by email to selected applicants by April 30th, 2019.
Winners will be invited to a Facebook event at PLDI 2019 and to the annual Programming Language Enthusiast Mind Melt (PLEMM) held in Seattle, WA in Fall 2019. Facebook will pay for the winners’ travel and accommodations to attend PLEMM (one representative per winning proposal).
We encourage the winners to openly publish any findings/insights from their work. Successful awardees will be listed on the Facebook Research website.