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I'm passionate about using data to improve our lives and our society through learning. I am a current graduate student pursuing dual masters degrees in Information Analysis and Retrieval and Applied Statistics at the University of Michigan. I work with Christopher Brooks at the University of Michigan Office of Academic Innovation, where we research both methodology and applications of predictive modeling and experimental intervention techniques for digital (MOOCs) and residential (on-campus) learning environments. My thesis research is focused on methods and tools for constructing predictive models in MOOCs, using Bayesian hierarchical modeling to drive automated inference about the full feature extraction, model-building, and hyperparameter tuning pipeline in large-scale machine learning experiments. I am also a developer of the MOOC Replication Framework (MORF), an open source large-scale machine learning research framework in collaboration with Ryan Baker at the University of Pennsylvania. 

Bio:

After graduating from the University of Michigan with a Philosophy degree, I worked as an educator and technologist serving students in public urban schools for five years, working with Teach For America - Miami-Dade and KIPP New Orleans Schools. I also served as an Operations Director at the Teach For America New York City Institute in 2011 and 2014, and worked as a Teacher Development Coach with TeachNOLA in 2013. Eager to utilize data science to expand the impact of my work, I returned to the University of Michigan and am currently pursuing dual masters degrees in Applied Statistics and Information Analysis and Retrieval.

I previously worked as a Professional Practice Fellow in the data science and digital insights team at ProQuest, and as a data science engineering and visualization intern at GoPro. In the winter of 2017, I was a Graduate Student Instructor for SI618: Data Manipulation and Exploratory Data Analysis with Ceren Budak. In the summer of 2017, I was an educational data mining research intern with Ryan Baker at the Penn Center for Learning Analytics, where I developed the computational framework and API for the MOOC Replication Framework, and the auctestr package for R, in collaboration with a research team at Penn and Michigan. I am also a Team Captain and Project Mentor with the Michigan Data Science Team.

In addition to my research, I'm passionate about running, and am a five-time Boston Marathon finisher.

News:

  • 1/2018: I will be presenting our work on MOOC Dropout Model Evaluation at the EAAI-18 Symposium at the Association for the Advancement of Artificial Intelligence (AAAI) in New Orleans on 2/4/2018. Conference Agenda.
  • 1/2018: We are excited to announce the alpha release of the MOOC Replication Framework (MORF) Platform! A preprint of the MORF software architecture paper is now available on arXiv.
  • 1/2018: The MORF API is now available on the Python Package Index. PyPi
  • 11/2017: My paper on Network-Augmented Classification in University Coenrollment Networks with Chris Brooks was accepted to LAK18.
  • 11/2017: My paper on MOOC Dropout Model Evaluation with Chris Brooks was accepted to EAAI-2018.
  • 11/2017:  My auctestr package is now officially available on CRAN. (The beautiful hex logo is only on Github)
  • 9/2017: I will be presenting our work Driving with Data: Modeling and Forecasting Vehicle Fleet Maintenance in Detroit with members of the Michigan Data Science Team at Bloomberg Data for Good Exchange in New York City.

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