The faculty of the Signal Processing and Machine Learning emphasis area explore enabling technologies for the transformation and interpretation of information. Signal processing—a traditional branch of electrical engineering—focuses on the modeling and analysis of data representations of physical events, lying at the heart of today's digital world. On the other hand, machine learning couples computer science and statistics to provide powerful predictions that are finding their way into more and more modern applications. Recently, machine-learning techniques have been applied to aspects of signal processing, blurring the lines between the two sciences and creating many shared applications between the two.
Specific Expertise of Faculty
- John E. Ball
Deep learning, advanced driver assistance systems (ADAS), digital signal/image processing, radar systems, remote sensing
- Dylan Boyd
Applied electromagnetics and signal processing, signals of opportunity reflectometry and transmissometry, forward and inverse microwave modeling, machine learning for remote sensing problems
- Jenny Q. Du
Hyperspectral remote-sensing image analysis, digital image processing, pattern recognition, data compression, neural networks, high-performance computing
- James E. Fowler
Computational imaging, diffusion models for inverse imaging, analysis and coding of hyperspectral imagery, random projections and compressed-sensing of imagery and video, image and video coding
- Robert Moorhead
UASs for environmental analysis
