I am a postdoctoral research scientist at the Data Science Institute at Columbia University where I work on machine learning methods with a focus on health care applications. I work with John Cunningham and Dave Blei. Before Columbia, I completed my PhD in Computer Science at Harvard University, advised by Ryan Adams. My area of research is statistical machine learning, with a focus on probabilistic modeling and scalable inference methods. I like to be application-driven; my applied work ranges from problems in astronomy to health care to sports analytics. On applications in health care, I work closely with Ziad Obermeyer and Sendhil Mullainathan.
Andrew C. Miller, Ziad Obermeyer, and Sendhil Mullainathan
Proceedings of the AMIA Summit on Clinical Research Informatics (CRI), 2019
Andrew C. Miller and Luke Bornn
Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe,
Dustin Lang, Mr Prabhat, David Schlegel, and Ryan P. Adams
Advances in Neural Information Processing Systems (NeurIPS), 2015
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.