I am a current PhD student in the Statistics & Data Science department at Carnegie Mellon University. I received my M.S. from CMU in 2015 and graduated from the College of Saint Benedict in 2014, where I studied math and computer science.


My research interests are rooted in applications in the social sciences, with a current focus on forensic evidence and criminal justice reform.

I am currently involved with the center for statistics and applications in forensic evidence (CSAFE), and am focusing on adapting psychometric models for forensic science settings including lineup design and proficiency exams. I have also worked on probabilistic graphical models for combining evidence and the application of new statistical methodology and experimental design for eyewitness identification procedures.

I am also an active member of the statistics education (StatEd) research group at Carnegie Mellon. We are currently working on redesigning our introductory statistics course to include modern data science concepts, active learning, and adaptive material. Part of this redesign includes designing a more effective assessment to identify misconceptions among introductory-level students.

Broadly speaking, I am interested in data visualization, categorical data analysis, bayesian methods, and statistics education.


I was the instructor for the 2017 Summer I session of Introduction to Probability Theory (36-225) and 2016 Summer II session of Experimental Design for Social and Behavioral Sciences (36-309) through the Carnegie Mellon University statistics department. I have also worked as a teaching assistant for various undergraduate statistics courses, as well as an undergraduate teaching assistant for calculus at the College of Saint Benedict.

I also designed a series of introductory probability lectures as a supplemental program for forensics students participating in the Summer Undergraduate Research Program (SURE) in both 2016 and 2017. Lecture slides and board examples were developed for this program.

I’m an advocate for increasing diversity in STEM, particularly in statistics and data science. I am a founding member of CMU’s Women in Statistics group (WiS) and have organized and participated in events to increase both access and visibility of women in statistics and data science, from pre-college to graduate level studies.