The Rice University DSP group has been conducting research in digital signal processing since 1968. Initially starting in filter design and FFT algorithms, the group has grown substantially over the past three decades, adding researchers working in image and video processing and coding, array processing, wavelets, computational imaging and photography, high-speed digital and analog circuits, and deep learning. A recent expansion has added new capabilities in machine learning and neuroengineering.
Current DSP research initiatives include:
- FlatCam - world's thinnest camera
- Universal microbial diagnostics - for microbial detection and classification
- Compressive signal processing and signal recovery
- Time of flight and phase retrieval imaging for seeing through scattering media and around corners
- Probabilistic models for deep learning
- Reverse engineering the algorithms of the brain using machine learning
Machine Learning for Education
Support for current DSP research comes from NSF, AFOSR, ARO, ONR, DARPA, IARPA, NGA, and IBM.