U.S. Air Force networks and systems gather and process enormous amounts of data. This information requires significant communication bandwidth and data processing by Air Force staff. More computationally efficient methods for processing this data and extracting pertinent information could vastly reduce bandwidth consumption while also saving workforce time and money.

Meng Wang, an associate professor of electrical, computer, and systems engineering, recently received a grant through the Air Force Young Investigator Research Program to develop models, machine learning methods, algorithms, theoretical analyses, and numerical evaluations that can more efficiently recover data, extract information, and identify anomalies in the data.

Wang and her team will combine deep learning with low-dimensional models in order to develop fast-learning algorithms that can be applied in real time and across diverse datasets.

“Low-dimensional models exist widely in practical datasets due to the intrinsic data correlations. We can exploit these special structures to simplify large data processing and information extraction,” Wang said.

Cybersecurity is also of high importance to the Air Force, so these new approaches will be focused on cyber-resiliency.

Wang is one of 40 scientists and engineers from 30 research institutions and businesses who have been awarded an Air Force Young Investigator Research Program grant. The goal is to support “outstanding young investigators,” while also fostering “creative basic research in science and engineering.”