Broadly speaking, my research is focused at the point of convergence where statistical and machine learning theory and methods support human endeavors enabled by computing and engineered systems.  Many of the problems on which I currently work involve the statistical analysis of network data, in collaboration with colleagues in bioinformatics, chemistry, computational neuroscience, mathematics, and signal processing. Much of my earlier work, and some of my current work, has focused on the statistical modeling of scale, motivated by problems in astronomy, epidemiology, geography, and signal and image processing.

  Some of my current research interests include:

  • Network modeling under noisy and/or dynamic conditions
  • Machine learning and artificial intelligence for chemistry and materials science
  • Statistical modeling and inference under differential privacy

To learn more about these projects, as well as past projects, please see below.