Current research projects are organized along three axes:

• Artificial intelligence (AI) and machine learning, including theory for deep learning that aims to make AI systems more energy efficient and data efficient
• Computational imaging to enable new kinds of sensing modalities
• Natural language processing, large language models, and AI for education data to close the learning feedback loop and improve learning outcomes

For all the latest results, see my Google Scholar page and the web pages of my fantastic PhD students and postdocs

Multi-university research projects based at Rice University include the ONR MURI on Foundations of Deep Learning

Previous research themes over the past 30 years have included time-frequency analysis, wavelet probabilistic modeling, complex wavelets, sparse representations, compressive sensing, single pixel cameras, bandits, manifold learning, sensor networks, communications, THz imaging, and network traffic analysis

Support for these projects has come from NSF, ONR, ARO, AFOSR, DARPA, IARPA, AFRL, DOE, NGA, EPA, NATO, and a number of industrial sponsors