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Opportunity at Air Force Research Laboratory (AFRL)

Robust Machine Learning for Trust

Location

Information Directorate, RI/Information Fusion and Understanding

RO# Location
13.20.92.C0124 Rome, NY 134414514

Advisers

Name E-mail Phone
Bennette, Walter D walter.bennette.1@us.af.mil 315-330-4957

Description

The need for increased levels of autonomy has significantly risen within the Air Force. Thus, machine learning tools that enable intelligent systems have become essential. However, analysts and operators are often reluctant to adopt these tools due to a lack of understanding – treating machine learning as a black box that introduces significant mission risk. Although one may hope that improving machine learning performance would address this issue, there is in fact a trade-off: increased effectiveness often comes at the cost of increased complexity. Increased complexity then leads to a lack of transparency in understanding machine learning methods. In particular, it becomes unclear when such methods will succeed or fail, and why they will fail. This limits the adoption of intelligent systems. This topic focuses on building trust in machine learning models by designing models that fail elegantly. Of particular interest are model calibration techniques for object detection and classification, novelty detection, open-set recognition, and post-hoc filters to identify instances prone to causing model failure. Other topics related to this area will also be considered.

Keywords:
Machine learning; Trust

Eligibility

Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral and Senior applicants

Stipend

Base Stipend Travel Allotment Supplementation
$76,542.00 $4,000.00

$3,000 Supplement for Doctorates in Engineering & Computer Science

Experience Supplement:
Postdoctoral and Senior Associates will receive an appropriately higher stipend based on the number of years of experience past their PhD.

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