Josh is a computational biologist pursuing his PhD. at Harvard Medical School. There, he is co-advised by Professor Kevin Haigis and Professor Peter Park as he studies cancer genetics and evolution. Specifically, he is working to understand the tissue-specific impacts of KRAS mutations on the behavior of cancers.
In his free time, Josh enjoys learning about programming and computer science - his current project is creating an iOS app in Swift. Off the computer, his hobbies include running and caring for his plants.
BS in Biochemistry and Molecular Biology, 2017
University of California, Irvine
BS in Chemistry, 2017
University of California, Irvine
Proficient
Intermediate
Intermediate
Sufficient
Intermediate
Intermediate
KRAS is the most frequently mutated oncogene. The incidence of specific KRAS alleles varies between cancers from different sites, but it is unclear whether allelic selection results from biological selection for specific mutant KRAS proteins. We used a cross- disciplinary approach to compare KRASG12D, a common mutant form, and KRASA146T, a mutant that occurs only in selected cancers. Biochemical and structural studies demonstrated that KRASA146T exhibits a marked extension of switch 1 away from the protein body and nucleotide binding site, which activates KRAS by promoting a high rate of intrinsic and guanine nucleotide exchange factor–induced nucleotide exchange. Using mice genetically engineered to express either allele, we found that KRASG12D and KRASA146T exhibit distinct tissue-specific effects on homeostasis that mirror mutational frequencies in human cancers. These tissue-specific phenotypes result from allele-specific signaling properties, demonstrating that context-dependent variations in signaling downstream of different KRAS mutants drive the KRAS mutational pattern seen in cancer.
A web application that summarizing text down to an adjustable percentage of the the most important sentences.
A web application that solves [Sudoku puzzles] using linear integer programming.
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.
A Swift package for summarizing long text into the most important sentences or words.
The steps I have taken to learn how to conduct Bayesian data analysis.
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.
My attempt at growing Frailea castanea from seed.
A simple app to help me record data on my seedlings.
Documenting my journey from seed to Lithops.
‘ggasym’ (pronounced “gg-awesome”) plots a symmetric matrix with three different fill aesthetics.
An app to help my mom keep track of and care for her plants.