This is the academic website of Danielle Van Boxel. Her research focuses is on new methods in machine learning and data science as well as improving accessibility in these fields.

She’s a PhD grad of the University of Arizona Applied Math GIDP and Data Diversity Lab. Her PhD advisors were Dr. Xueying Tang and Dr. Cristian Román-Palacios.

Welcome!

I enjoy research in many fields of mathematics. By chance or by fate, I have done most of my research in machine learning. Originally I was a traffic engineer, but have long since generalized to arbitrary data sets. In addition to studying new methods in machine learning, I also enjoy analyzing data sets. Every data set has a story, and part of that story is what mistakes or obfuscations exist in it. Teasing those out is a delightful puzzle.

Note that I used to publish under a different first name (same initial). Thankfully, my last name is rare enough that something by “D. Van Boxel” in the United States since 2007 was likely me.