This is the academic website of Danielle Van Boxel. 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. Danielle’s research focuses on new methods in machine learning and data science.

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.