About me
Hello and welcome!
I am an enthusiastic researcher holding a Ph.D. in Measurement & Statistics from the University of Washington (UW) College of Education. Currently, I am a postdoctoral fellow at the Harvard Graduate School of Education.
My primary research focus centers around advancing statistical methods for text analysis within randomized controlled trials (RCTs). I am deeply committed to pushing the boundaries of research methodologies to make comprehensive analysis and interpretation more accessible. Additionally, I am dedicated to reducing the human coding burden while preserving the statistical power of treatment effects in RCTs by developing innovative R packages for text analysis.
During my doctoral journey, I immersed myself in quantitative methods, with a strong emphasis on predictive models encompassing multilevel, latent variables, and social network analysis. My dissertation delved into the intersection of factor analysis and machine learning algorithms, particularly exploring the performance of novel exploratory factor modeling techniques using Monte Carlo simulation.
My proficiency extends to a wide range of software tools, including R, Python, SPSS, MPlus, Stata, SAS, SQL, and Tableau. These skills have been honed through my active involvement in various research projects and my role as a statistical consultant at UW’s CSSCR.
Beyond my methodological expertise, I am deeply passionate about educational and behavioral science research that advances equity and fosters social good. I firmly believe in the transformative power of research to drive positive change in society. In my free time, I enjoy spending time with family and friends, playing soccer, and learning new things.
Research Projects
Develops statistical methods for supporting text analysis in RCTs (see the related projects)
Extends factor analysis using insights from machine/deep learning methods (see the project slides)
Examines international students’ professional development and well-being and their racism and discrimination experiences
Compares the relative benefits of exponential random graph models with latent space models – two different approaches for predicting the formation of a tie in a network – including missing data handling (see the project slides)