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I am passionate about using machine learning to improve peoples’ lives. I am currently pursuing a PhD in computer science at the University of Washington's Paul G. Allen School of Computer Science & Engineering. Previously, received dual masters degrees in Applied Statistics and Information Science at the University of Michigan, where I worked with Christopher Brooks in the Educational Technology Collective (etc) and received the Margaret Mann Award. I received a BA in Philosophy from the University of Michigan, where I received the William K. Frankena Prize. I have worked as a data science engineering and visualization intern at GoPro, an educational data mining research intern with Ryan Baker at the Penn Center for Learning Analytics, and a data science Professional Practice Fellow at ProQuest. Previously, I served for five years as a K-12 mathematics and technology educator in Miami-Dade Country Public Schools and with KIPP New Orleans Schools.

News:

  • 6/2019: I started a summer research software engineering internship at Google/YouTube.

  • 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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