Learning engineer and educational data scientist. I spend my days bringing together educational data and learning science research to support student learning, empower learning designers with data insights, and researching the effectiveness of learning experiences. With a strong background in industry-standard languages and infrastructure (e.g., SQL, R, Python, Tableau, Snowflake, and Google Cloud Platform), I am skilled in analyzing, interpreting, and communicating complex structured and unstructured data to provide meaningful insights to stakeholders. I am particularly interested in using data to support data-informed instructional design and empower learners and instructors to take a more systematic and measurement-focused approach to improving learning outcomes. My expertise in the learning sciences further allows me to design and support the implementation of data-driven interventions intended to drive student engagement and improve learner achievement. Past projects include building semi-supervised machine learning models to identify anomalous student learning behaviors, creating novel NLP algorithms to detect frustration and confusion in student discussions, leveraging generative AI APIs to create personalized learning tutors and provide teacher feedback, building course diversity dashboards to support educational equity, and using data to infer student learning strategies and predict course success.Additional tidbits: Published author and speaker at leading learning conferences. Awarded over half-a-dozen patents in educational technology and machine learning. An unaccredited Bayesian.If interested, you can check out my blog on Medium here: