Probabilistic Programming for Advancing Machine Learning (PPAML) | Apply Online

Opportunity Summary:

DARPA's Probabilistic Programming for Advancing Machine Learning (PPAML) opportunity (Funding Opportunity Number: DARPA BAA 13-31) is a research solicitation focused on advancing probabilistic programming languages and the supporting toolchains needed to make them practical for building new machine learning applications. The central aim is to fund innovative R&D that makes it easier to express uncertainty-rich models in code, automate or simplify probabilistic inference, and reduce the specialized expertise typically required to develop robust probabilistic machine learning systems. In plain terms, DARPA is looking for ways to move probabilistic modeling from being a niche skill practiced by experts into something that can be used more broadly, more reliably, and more efficiently across many application areas.

The program is issued by DARPA's Information Innovation Office (I2O) under the broad agency announcement (BAA) mechanism, which is commonly used to seek cutting-edge research ideas rather than procure a specific product. The funding activity category is Science and Technology and other Research and Development, and it is associated with CFDA number 12.910 (Research and Technology Development). DARPA indicates that submissions may be supported through multiple award instruments, including cooperative agreements, other transactions, and procurement contracts, which generally means the agency can choose the contracting approach that best fits the type of work proposed and the level of collaboration anticipated. There is no cost sharing or matching requirement stated in the opportunity data, which typically lowers the barrier to participation for academic teams and smaller organizations.

PPAML is open to an unrestricted pool of applicants, meaning any type of entity can apply as long as it fits any eligibility clarifications contained in the full announcement. That generally includes universities, nonprofit research organizations, companies (including small businesses), and potentially teams combining multiple organizations. The posted date is April 1, 2013, with an original and current closing date of May 16, 2013, and an archive date of October 16, 2013, indicating the solicitation is no longer active but remains a useful reference point for the kinds of technical directions DARPA has sought in this area. The award ceiling and floor are listed as 0 in the structured fields, which usually means DARPA did not commit to fixed minimums or maximums in the summary metadata and instead specified expected funding levels, scope, or constraints in the full BAA document.

Technically, the solicitation emphasizes probabilistic programming as a foundation for the next wave of machine learning: languages that let developers write models as programs while the system handles inference, learning, and uncertainty reasoning. Alongside language design, DARPA explicitly calls out "accompanying tools," which typically includes compilers or interpreters, inference engines, debugging and diagnostic capabilities, performance optimization (for example, exploiting parallelism or hardware acceleration), and usability features that make these systems accessible for real-world workflows. The phrase "across a wide range of domains" signals an interest in general-purpose capability rather than a one-off demonstration, so strong proposals would usually be expected to show that the approach can transfer to multiple classes of problems and data types.

For administrative and access details, the opportunity points to an Additional Information link hosted through FedBizOpps (the federal contracting portal used at the time), and it provides a contact for assistance obtaining the full announcement: Dr. Kathleen Fisher, Program Manager at DARPA/I2O and the BAA Coordinator. In practice, that contact point is intended for questions about accessing the documentation and navigating submission logistics, while the full BAA PDF would contain the deeper specifics on technical areas of interest, evaluation criteria, deliverables, reporting expectations, and any submission formatting requirements.

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