Signal Processing and Machine Learning

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
  • Jenny Q. Du
    Hyperspectral remote-sensing image analysis, digital image processing, pattern recognition, data compression, neural networks, high-performance computing
  • James E. Fowler
    Analysis and coding of hyperspectral imagery, random projections and compressed-sensing of imagery and video, representation and compression of big data, image and video coding
  • Ali Gurbuz
    Radar and array signal processing, digital signal and image processing, compressive sensing, machine learning, detection and estimation
  • Robert Moorhead
    UASs for environmental analysis
  • Bo Tang
    Statistical signal processing, machine learning, data mining, autonomous driving, smart grid
  • Junbo Zhao
    Big-data analytics, machine learning, robust statistical signal processing