My primary research interest concerns applications of mathematics and machine learning to areas where the rigorous foundations are less than desirable, or where assumptions used in proving theorems and developing learning algorithms for commonly analyzed cases do not hold for data obtained in practice. This leads to a lot of experimental work, all with the aim towards better understanding these problems and developing deployable solutions.
Below, you’ll see links to the projects I’ve worked on throughout my career. I find all of these things interesting, and I hope you do, too.
Research in optimization has exploded due to increases in computational ability. Online optimization, where objective functions can change in time, is a field with vast potential for both interesting theoretical investigations as well as practical applications well outside areas traditionally considered in, e.g. control and learning communities. Read more
Partisan gerrymandering is a phenomenon in American politics that, unlike racial gerrymandering, is poorly understood. Read more
How much can we learn about biology by looking at anatomical surfaces? Potentially a lot, but there are many preprocessing issues to deal with before we can. Read more