A grad student at the University of Wisconsin, interested in optimization and passionate about skiing!


[email protected], [email protected], @stsievert

I can be found on GitHub, Twitter, StackExchange, Google Scholar, ORCID and LinkedIn

Research interests

Optimization, adaptive sampling theory, learning theory, large scale machine learning, data science

What if we have to collect human responses for some machine learning task? We’d like to pay for as little as possible, so we’d like this to be active. Connecting human responses to active learning algorithms is difficult in part because model updates have to run quickly. How quickly can these updates be performed with a large computer equipped with much memory and many cores?

Also, there’s a great talk by Prof. Jarvis Haupt on adaptive sampling that illustrates some of my interest. There’s also a Wired article about compressed sensing and images having many areas of the same color. How do you best “fill in the blanks?”


My current “course of life” or CV is available here.

University of Wisconsin-Madison

A grad student at the Wisconsin Institute for Discovery under the optimization theme! I am co-advised by Rebecca Willett and Rob Nowak.

University of Minnesota

Major: Electrical engineering.
Minor: mathematics
2010-2015 (graduated cum laude).

I worked with Prof. Jarvis Haupt to develop several projects, including an iPhone app, thermal camera and sign language recognition system.