I research the dimension reduction problem, representing high-dimensional and noisy sequential data as a low-dimensional object that encodes relevant information. I apply my work to tasks from the interdisciplinary field of Music Information Retrieval (MIR), such as locating the chorus of a given musical song or finding all copies of a particular recording of a song. I also have started work in other areas of Cultural Analytics.
Currently, I am a Data Sciences Postdoctoral Fellow, affiliated to the Division of Applied Mathematics at Brown University, with Björn Sandstede as my postdoctoral mentor. From September 2014 to June 2016, I was a visiting assistant professor in the MSCS Department at Macalester College with Chad Higdon-Topaz as my postdoctoral mentor. I was the founder and Principal Investigator for the Data Science TRAIn Lab.
I am passionate about teaching and learning. My teaching style relies on creating a low-risk, supervised learning environment and using a variety of collaborative and active learning techniques.
In May 2014, I defended my Ph.D. dissertation, titled Aligned Hierarchies of Sequential Data, in the Department of Mathematics at Dartmouth College. I am a student of Scott D. Pauls, and also worked with Michael A. Casey. I received my B.A. in Mathematics from Wellesley College in 2008 and my M.A. in Mathematics from Dartmouth in 2010.
I believe in participating in organizations and environments that encourage and support women in STEM fields, such as the Women in Machine Learning Workshop (WiML). In 2013 at a high-school in New Hampshire, I helped create LadyHack, a programming club comprised of motivated, female students.
I love attending hackathons, especially Music Hack Days. I am originally from Maryland, most recently from Minnesota (and Vermont before that). As a result, I am torn as to whether Old Bay, Maple syrup, or tatchos makes all things better.