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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 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 lead developer of the MOOC Replication Framework (MORF), an open source machine learning research framework. 

I received a BA in Philosophy from the University of Michigan, where I received the William Frankena Prize. I have worked as a data science Professional Practice Fellow at ProQuest, data science engineering and visualization intern at GoPro, and an educational data mining research intern with Ryan Baker at the Penn Center for Learning Analytics. Previously, I served for five years as a K-12 mathematics and technology educator in Miami-Dade and with KIPP New Orleans Schools.

News:

  • 3/2019: our paper Evaluating the Fairness of Predictive Student Models Through Slicing Analysis received the Best Full Research Paper Award at LAK19.

  • 3/2019: I will be giving an invited presentation at the FairLAK workshop at the International Conference on Learning Analytics and Knowledge (LAK19), in addition to a presentation of our paper Evaluating the Fairness of Predictive Student Models Through Slicing Analysis.

  • 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: 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: 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.

  • 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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