My research interests include signal processing, machine learning, and large-scale data science. In particular, I have studied methods to leverage low-dimensional models in a variety of contexts, including when data are high-dimensional, contain missing entries, are subject to constrained sensing or communication resources, correspond to point processes, or arise in ill-conditioned inverse problems. This work lies at the intersection of high-dimensional statistics, inverse problems in imaging and network science (including compressed sensing), learning theory, algebraic geometry, optical engineering, nonlinear approximation theory, statistical signal processing, and optimization theory. My group has made contributions both in the mathematical foundations of signal processing and machine learning and in their application to a variety of real-world problems. I have active collaborations with researchers in astronomy, materials science, microscopy, electronic health record analysis, cognitive neuroscience, precision agriculture, biochemistry, and atmospheric science.

• AI & Science
• Machine Learning & Inverse Problems
• Machine Learning in Data Assimilation
• Contextual Bandits
• Overparameterized Neural Networks
• Change Detection
• Data-starved inference for high-dimensional point processes
• Nonlinear models in machine learning
• Poisson Compressed Sensing
• Computational imaging and spectroscopy
• Machine learning for large-scale data streams