$ ls -1Gp projects/ Twitter Scraper (160 stars)/
SIFF Calendar Scraper/
Duolingo Analysis/
Anime Recommender System/
Pokémon Neural Network/
Markov-Chaining Twitter Bot/

$ ls languages/ Python    SQL            C++          Scala
R         Shell (BASH)   Java         LaTeX

$ cd experience/ && ls -RpG internships/     research/

./internships:
West Bend Mutual Insurance/      Arity (an AllState company)/      Capital One/

./internships/West Bend Mutual Insurance:
Software Engineer.intern          Summer2017

./internships/Arity (an AllState company):
Data Analyst Engineer.intern      Summer2018

./internships/Capital One:
Data Engineer.intern              Summer2019

./internships/Amazon:
Software Engineer.intern          Summer2020

./research:
Engelman.lab     2015-2017       Alzheimer's Disease      Machine Learning
Payseur.lab      2017            Mice Genetics            Automated Image Processing
Ong.lab          Summer2017      Cancer                   Data Visualization
McMahon.lab      2018-2019       Metagenomics             Big Data
Schwartz.lab     2020            Cancer Modeling          Linear Programming

$ perl -pe "s/\t/: /g" about_me.tsv Home state: Wisconsin
Born: 1996
Cats: 2
Hobbies: Lifting, bouldering, unnecessary automation, Overwatch

$ history In 2015 I began attending the University of Wisconsin, the shared alma mater of both my parents. In the first month, I became involved in a computational epidemiology lab that focused on Alzheimer's research. Much of my work revolved around the application of machine learning (ML) methods to human data. I worked with Random Forests to characterize gene-gene and gene-environment interactions with the goal of identifying predictors for Alzheimer's. I then worked with metabolomic data, implementing dimensionality reduction and clustering techniques. Under the direction of Dr. Engelman, I presented my work with metabolomics and RandomForests at the 2016 Undergraduate Research Symposium.

I then spent a semester working with a PhD student in a genetics lab where I wrote software to do semi-supervised image processing & annotation of mouse chromosomal data. Interning in Summer 2017 as a Software Developer at West Bend Mutual Insurance, I wrote data processing programs in C#, R, and VBA to manipulate some delightful legacy "databases" (Excel spreadsheets) and port them into a MySQL database. I had to touch on some front-end development and taught myself Python on the side.

Instead of returning to research right away, I got into competitive programming and the hackathon scene, competing with UW-Madison's ICPC and traveling across (and out) of the country to participate at HackMIT and Waterloo's Hack the North. I picked up an interest in bioinformatics and applied machine learning and began to self-teach myself data science, founding UW-Madison's Data Science club (which now sits at 300+ members and still sends out regular newsletters). I sought out relevant research for the follow semester and was offered a long-term position in a bacteriology research lab, where I maintained and enhanced a data pipeline for hundreds of gigabytes of metagenomic data. This helped me obtain a Data Analyst Engineering Internship in 2018 for Arity, the predictive analytics brainchild of AllState. I built a Spark pipeline to ingest and analyze millions of records, each day generating a data monitoring report.

I led the Data Science Club until my graduation, organizing workshops, contacting local companies and organizations to give lectures on special topics, and matching undergraduates with researchers who needed help with analytics work.

After graduating in 2019, I completed a Chaos Engineering internship with Capital One before proceeding to my graduate program at Carnegie Mellon University to study Computational Biology. Between serving as a teaching assistant for three courses and a 2020 internship with Amazon's graduate intern program, I kept busy as the global pandemic came into full swing. I graduated with a 3.97 GPA from Carnegie Mellon in 2021, and moved to Seattle to work for Amazon.

Thanks for reading! Feel free to look at some of my more recent projects on my GitHub.!