Signal processing represents a combination of mathematics, engineering, and computer science to produce algorithms and novel ways to analyze data streams from physical sensors in order to understand information contained within the data streams. Our expertise in signal processing is wide reaching. We develop algorithms to:
- Understand non-stationary signals (i.e. signals whose fundamental properties change with time)
- Sense signals in novel ways in order to reduce storage requirements.
- Apply machine learning techniques to biomedical signals.
- Characterize interactions among nodes in large networks.
Most of our work concerns medical signals, and includes diverse applications such as understanding swallowing difficulties or understanding walking instabilities in older adults. We also apply our algorithms to understand brain networks and other critical issues in neuroscience.
Below is a video series about up and coming careers in signal processing featuring Dr. Sejdic.