I opened four pull requests to open source projects to complete the 2021 Hacktoberfest challenge. This year, I challenged myself to commit primarily to other’s projects and was successful in this pursuit. I added specifications for R and Rscript commands to Fig, a tool that brings autocomplete to the terminal (1). I also added the specification for Rscript to the command line tool tldr (2). My third PR was to add an example of fitting a spline with PyMC3 to the pymc3-examples repository (3). The fourth PR was to my own project where I added Section 5 notes and exercises to my repository for the course Bayesian Data Analysis (4). While these were the four PRs that counted for Hacktoberfest, I continued with several others including fixing a bug in snakemake (5), a typo in a file for the class I am working on (6), Section 6 to my course repo (7), and created a tutorial, blog post, and demo app using Appwrite and added these to the awesome-appwrite repo (8, 9).

My (first) Python package, boston311, for querying the Boston 311 reporting service API is available on PyPI.

We have published our analysis on the genetic interactions of KRAS alleles in four different cancer types in Nature Communications.

I completed both coding puzzles released on each of the 25 days before Christmas as a part of the Advent of Code challenge.

I opened four pull requests to open source projects to complete the Hacktoberfest challenge. The first two added iPhone-to-Watch connectivity and Workout Session capabilities to my Apple Watch telemetry-recording application (1, 2). The third PR added the Workout Session capability to my Workout Spinner Apple Watch app (3). Finally, the fourth PR merged the resubmission branch of the KRAS comutation project code repository (4).

My R package, mustashe, has been accepted to CRAN. It is ‘A simple system for saving and loading objects in R. Long running computations can be stashed after the first run and then reloaded the next time. Dependencies can be added to ensure that a computation is re-run if any of its dependencies or inputs have changed.’

To become comfortable with Python, I completed an hour (usually more) of deliberate practice. I primarily followed data science-centered tutorials, namely Python for Data Analysis and Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. I also followed the tutorials for the common plotting libraries, Matplotlib, Seaborn, and Plotly. I recorded my progress at the linked GitHub repository. I did not finish the Hands on ML book in the 100 days, but I continued working through it afterwords.

I opened four pull requests to open source projects to complete the Hacktoberfest challenge. I merged large features into my Germination and Plant Tracker apps (1, 2, 3). Also, I made a pull request to add documentation for a statsitcal test using by Hierarchical HotNet (Reyna, et al.. Bioinformatics. 2018) from the Raphael Lab at Princton University (4).

I challenged myself to do at least an hour of Swift every day for 100 days. I recorded my progress at the linked GitHub repository. Towards the end of the challenge, I started developing two iOS apps, a Plant Tracker and a Germinaton Tracker.

The Haigis Lab published on a biochemical, signaling, and computational description of the KRAS A146T allele.

I have been awarded an Honorable Mention in the 2019 National Science Foundation (NSF) Graduate Research Fellowship Program competition

My R package, ggasym, has been accepted to CRAN. It was further promoted by RStudio in their March edition of Top 40 New CRAN Packages and R Weekly (March 18, 2019).

My undergraduate reseach in the Lodoen Lab at UC Irvine on the interactions between the single-celled parasite, T. gondii, and human monocytes was accepted by JBC.