CENTER OF EXCELLENCE: Efficient and Robust Machine Learning (ERML)

Funder: Air Force Office of Scientific Research

Opportunity: FOA-AFRL-AFOSR-2017-0002

Due Date: November 30th, 2017

Budget: $4,000,000

The purpose of this ERML University Center of Excellence (UCoE) is to establish the foundational principles of design, development, and employment as well as critical technologies to support machine learning (ML) methods and procedures. The Air Force’s autonomy vision includes intelligent machines that utilize data, leverage learning techniques, and deliver content for a variety of operations. Machine learning methods must be employed with efficiency and robustness for effective results given data challenges such as variety, volume, velocity, veracity, and value. Leveraging ML methods and techniques should incorporate contextual considerations, operational environments, and hardware implementation. To achieve the USAF vision, ML techniques must consider transferability, usability, efficiency and robustness for mission success. Foundational science and technology is needed to understand machine learning employment challenges including efficiency (data and computational), robustness (practical and adversarial), as well as relevancy (situational and contextual).

Proposals are sought for basic and applied research towards the efficiency, robustness, and usability of machine learning methods, as well as understanding of the content, context, and considerations of machine learning to process, exploit, and disseminate results based on performance requirements. Desirable proposals will address data types and machine learning concerns (e.g., transfer, zero-shot). In additional, desirable proposals will seek to understanding and develop complex, adaptive machine learning techniques that support software/hardware implementation.

A broad range of research specializations will be needed in which researchers can interact in an interdisciplinary setting to perform cutting-edge research in a collaborative environment composed of government and academic personnel. Multi-investigator teaming across departments and disciplines is highly encouraged. Proposals should describe contributions of the proposed research to the basic understanding and principles of human-machine teaming, as well as contributions to revolutionary advances in the technology of data analytics and autonomous systems. The collaboration effort should be designed to focus on long-term technical community sustainment through expertise development and experience opportunities that are actively disseminated between groups and individuals. The center should expose students and academic researchers to the research and development environment of AFRL, transfer knowledge between AFRL and academia, and enable interaction between other academic researchers at AFRL.

Proposals should address ways to develop a new generation of data analytics and autonomous systems experts and increase the education, expertise, and experience of current researchers, while instilling an appreciation of the interdependency of researchers and disciplines. Applicants are advised that routine access of educational institution researchers to AFRL/RI buildings and facilities may be limited to US citizens and permanent residents. Individuals eligible for access are subject to background checks.
Proposers are highly encouraged to confer with the designated points of contact as soon as possible. Their contact information can be found at the end of this announcement. Coordination with the AFOSR and AFRL/RI prior to proposal submission is highly encouraged but not required.

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