I study statistical machine learning, focusing on spatiotemporal models and scalable inference methods. I like to be application-driven; my applied work ranges from problems in astronomy to healthcare to sports analytics. I am a member of the Harvard Intelligent Probabilistic Systems (HIPS) group (now LIPS), advised by Ryan Adams (now at Princeton). I also work with Luke Bornn in the statistics department (now at Simon Fraser).
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, and Prabhat
[abstract] (in submission)
Andrew C. Miller and Luke Bornn
Andrew Miller, Vishal Jain and Joseph L. Mundy
A julia library for astronomical source discovery and classification using approximate Bayesian inference (led by Jeff Regier).
Simple, lightweight dynamic time warping implementation (and visualization) in numpy/python/cython.
Sampyl is a package for sampling from probability distributions using MCMC methods. Similar to PyMC3 using theano to compute gradients, Sampyl uses autograd to compute gradients. However, you are free to write your own gradient functions, autograd is not necessary. This project was started as a way to use MCMC samplers by defining models purely with Python and numpy.