Christopher P. Email & Phone Number

At IXL, I was a member of the Teacher Experience team, which works on maintaining and building new features for educators on IXL's platform. My role involved full-stack development in Struts and React, working on an Agile team with biweekly standups and releases. Within my first six months at IXL, I had already led a full-scale project to allow instructors to pin skill plans for specific classes instead of their entire roster, for which I designed the back-end, wrote a detailed design doc, and led the team through implementation. One of the other big projects I worked on at IXL was a complete refactor of the back-end for personalized skill plans for exams like the SAT, ACT, and NWEA MAP, a feature used by more than 250,000 users. For this project, I designed the new back-end and did most of the implementation.

At Microsoft, I architected and engineered an open source Python auto-assessment solution called PyBryt that implements a unique autograding structure grounded in the philosophy that intermediate to advanced programming courses should not require students to follow a rigid scaffold for solving problems. I provided engineering support to a series of pilots of PyBryt in courses of up to 1,200 students per semester at UC Berkeley, Imperial College London, and Tel Aviv University, and developed and designed software on an Agile team with biweekly standup schedule emphasizing short feedback cycles and iterative development with weekly internal releases. I spent most of the first half of my time at Microsoft working on the initial release of and developing new features for PyBryt.In the latter half of my time at Microsoft, I shifted focus to work more on furthering the adoption of PyBryt beyond its pilots at UC Berkeley, Imperial College London, and Tel Aviv University (while still working on feature development). I created a GitHub Action to automate the use of PyBryt as a continuous integration tool for student repositories; I then used this Action to orchestrate a full-scale implementation of a real-world grading pipeline using GitHub Classroom for collecting different implementations of algorithms to be used to construct exercises for interactive Microsoft Learn modules. I also authored a blog post and two Microsoft Learn modules on an introduction to and advanced uses of PyBryt geared towards academics looking to adopt the solution.

Berkeley, Ca

I manage a team of 6 developers working on 3 projects using Agile methodologies and other technologies (e.g. JIRA, Trello) to manage development cycles and encourage parallelization of tasks. This involves performing code review for projects in order to ensure adherence to DevOps best practices and that business requirements are being met and leading weekly meetings and tri-weekly standups to keep leadership abreast of project statuses and developers on top of user stories to be completed during the sprint. I also participate in ongoing campus discussions surrounding standardizing autograding solutions and practices throughout campus for major courses, including presenting on two subjects for the 2020 National Workshop on Data Science Education, covering autograding and UC Berkeley’s existing solutions and pitching Otter Grader (see below) as the new UC Berkeley standard for autograding, and meeting with data science campus leadership and course instructors across institutions to discuss autograding solutions, pitch Otter Grader, and advise on setting up grading infrastructure.The main projects I work on relate to autograding solutions that are designed to reduce the barrier to entry for setting up data science courses. I built a serverless autograding solution, Otter Grader, that grades students’ notebooks locally and integrates with 3rd party autograding services that is used in UC Berkeley courses, including a class of over 1,000 students. My team also improved an existing autograding solution by adding telemetry to track students’ submissions and attempts for a massive open online data science course offered through EdX with more than 44,000 enrolled students.

Berkeley, California, United States

Responsible for teaching discussion sections, holding office hours, grading exams, and developing and maintaining infrastructure for a course of near 1,000 students. This includes managing the course JupyterHub distribution and developing new features for the class autograding solution, Otter Grader, which I built as a part of my position at the Data Science Education Program.

San Francisco, California, United States

As a part of my internship, I perform inspection and analysis on data from various bank financial reports utilized for analysis of economic activity by the Federal Reserve Board of Governors, including detecting and writing remarks for anomalous data points and assessing trends and issues by comparing against past financial data. This involves using internal databases to assess financial data reported to the Statistics function, managing data pipelines from various data sources in order to query, merge, shape, filter, and transform data to meet business needs, and applying cleaned data to business problems by creating data products that utilize the data and algorithms to reduce the workload of analysts.I built a remark prediction algorithm (a two-part program) to accompany anomaly detection performed on weekly balance sheet data from the FR 2644 for 65+ depository institutions. The first part pulls 52 weeks of historical data for the report from the current as-of date and merges the data queried from 4 tables into a usable form for predicting remarks and the second matches detected anomalies with the most recent viable remark to carry forward in order to auto-fill the remark for analyst.