I mentor and consult for people and groups in industry and academia.

Large data or small data? Helping users make decisions under uncertainty? Deciding whether machine learning may even help in a given context?

I mainly consult via collaborations where the shared goal is an academic publication that develops intellectual property. Where needed, I bring in friends and collaborators in machine learning, full-stack development, user research, and design. Previous projects include:

  • For The Browser, developed an end-to-end machine learning solution to make the editorial process more efficient.

  • With data from the Lose It! app, built a food recommendation system that scales to hundreds of thousands of users and tens of millions of datapoints.

  • For a metabolomics lab, helped build performant and interpretable machine learning solutions to identify molecules in biological samples from mass spectrometry instruments. Applications include blood, stool, biomarker, and food analysis.

  • For arXiv.org data, developed state-of-the-art recommender system described in Chapter 4 of my thesis.

  • Contributed initial UI/UX design research and prototypes for CANImmunize, which is used for the roll-out of Canada’s COVID-19 vaccine.

  • Helped a friend who teaches in DC design a Google sheets solution to replace ClassDojo that scales to 1000 students and 100 teachers. We designed a week-long user study with 137 students and 8 teachers to test the prototype.

  • For clinical notes in hospital electronic health records, helped develop a system to predict readmission.

Feel free to reach out if you think we might work together: [email protected]. I’m especially interested in helping build machine learning solutions for areas in mental health and behavior change.