I'm passionate about using data to improve our lives and our society through learning. I recently completed dual masters degrees in Applied Statistics and Information Science at the University of Michigan. I worked with Christopher Brooks in the Educational Technology Collective (etc), researching both methodology and applications of data-driven methods for supporting large-scale learning in 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 support 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 machine learning research framework in collaboration with Ryan Baker at the University of Pennsylvania.
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 completed dual masters degrees in Applied Statistics and Information Science. I will be entering the PhD program at the University of Washington's Paul G. Allen School of Computer Science and Engineering in fall 2018.
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 was also the VP of Education and a 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.
1/2019: MORF received an Amazon Web Services Cloud Credits for Research Grant! We are grateful to AWS for their support.
1/2019: LAK19 paper on Evaluating the Fairness of Predictive Student Models Through Slicing Analysis nominated for Best Paper Award.
11/2018: Two papers accepted to 2019 International Conference on Learning Analytics and Knowledge (LAK19).
7/2018: I will be attending the International Conference on Machine Learning in Stockholm, Sweden to share a poster of our work Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining at the Enabling Reproducibility in Machine Learning workshop.
6/2018: I will be attending the London Festival of Learning and presenting our work on replication of predictive models at Learning@Scale.
4/2018: I received the University of Michigan School of Information's Margaret Mann Award.
4/2018: I graduated from the University of Michigan with dual master's degrees in Applied Statistics and Information Science.
4/2018: Paper with Chris Brooks and Kaifeng Chen on How Gender Cues in Educational Video Impact Participation and Retention has been accepted as a crossover paper to be presented at the joint "Festival of Learning" co-hosted at the International Conference of Artificial Intelligence in Education (AIED), International Conference on the Learning Sciences (ICLS), and International Conference on Learning@Scale (L@S).
4/2018: Paper on Student Success Prediction in MOOCs with Chris Brooks has been accepted to User Modeling and User-Adapted Interaction.
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: The MORF API is now available on the Python Package Index. PyPi
11/2017: Paper on Network-Augmented Classification in University Coenrollment Networks with Chris Brooks was accepted to LAK18.
11/2017: Paper on MOOC Dropout Model Evaluation with Chris Brooks was accepted to EAAI-2018.
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.
Education Media Centre: How ‘smart’ online courses can help close the gender gap
University of Michigan: Professional Practice Fellows Test Classroom Lessons in Leading Companies
Teach Like A Champion: teachNOLA and a Whole New Approach to Practice