I am a computational biologist pursuing a PhD. in computational biology at Harvard Medical School. My primary interest is in understanding the tissue-specific behaviors of oncogenes in cancers.
In my free time, I enjoy learning about programming and computer science - my current project is working on comparing AI/ML summarization methods on scientific publications. Off the computer, my hobbies include running, exercising, caring for my plants, and baking bread.
PhD in Computational Biology & Cancer Genetics, (in progress)
Harvard Medical School
BS in Biochemistry and Molecular Biology, 2017
University of California, Irvine
BS in Chemistry, 2017
University of California, Irvine
I contribute to open source projects.
Curing KRAS driven cancers.
Computational methods to understanding cancer biology.
The KRAS gene is often mutated at several hotspot codons in cancer, resulting in similar, yet distinct, functional impacts on the KRAS protein. Here, the authors examine the genetic interactions of the different KRAS mutations across multiple cancer types and discover that KRAS mutations have allele- and tissue-specific mutagenic origins, comutation patterns, and dependency interactions.
My own visualization and statistical analysis of the data from a replication study of this famous phsycology paper.
We trained a Progressive Growing GAN to produce realistic, yet novel, hand radiographs and explored its latent input space to identify an embedding of bone age.
A web application that summarizing text down to an adjustable percentage of the the most important sentences.
This links to my repository with my solutions to the Advent of Code 2020 programming challenges.
This links to my repository of
#TidyTuesday submissions. This is a series of notebooks and scripts that analyze a different dataset each week where I experiment with various modeling and data visualization techniques.
An iPhone app for tracking when plants have been watered. There is also an Apple Watch app that makes work in the garden a bit easier. (demo GIFs)
An Apple Watch application for recording telemetry data during a workout and uploading the data to iCloud Drive. Mathematical models are then fit to the data to identify the various stages of the exercise (e.g. the down and up positions of a push-up).
An Apple Watch application that randomly selects quick exercises using a Wheel-of-Fortune-like spinning wheel.
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.
‘ggasym’ (pronounced “gg-awesome”) plots a symmetric matrix with three different fill aesthetics.
Rscriptcommands to Fig, a tool that brings autocomplete to the terminal (1). I also added the specification for
Rscriptto 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).
boston311, for querying the Boston 311 reporting service API is available on PyPI.
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.’