Software Engineer

Ronald Lee

// Developer · Researcher · Problem Solver

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Ronald Lee
3+ Years Experience
01 — About

A little about me

Hi there! I'm a software engineer at Arrowstreet Capital working on simulation infrastructure for research systems. Previously, I interned at Meta as an ML intern on the App Ads ML Team where I used multi-task learning to improve model performance for Facebook + Instagram ad recommendation.

I received my B.S. in Computer Science from Texas A&M University, where There, I was a researcher with Dr. James Caverlee at the Infolab, and Dr. Ali Mostafavi at the Urban Resilience AI Lab.

Outside of engineering and research, I enjoy hiking, playing chess, and travelling. I'm always looking to connect! LinkedIn is best way to reach me.

Languages

Python C# C++ SQL

Technologies

Linux Kubernetes AWS Docker gRPC DuckDB Pandas Polars
Quant Finance
Writing code to assist researchers in discoving alpha opportunities.
Distributed Systems
Building fault-tolerant, high-throughput services with a focus on reliability.
Artifical Intelligence
Using AI to automate tasks and build cool things fast, including this website!
Connection
Always looking to connect with people from different backgrounds to learn more. Feel free to reach out!
02 — Experience

Where I've worked

Arrowstreet Capital
June 2023 — Present
Senior Simulation Developer, Research Systems
  • Writing code to help Researchers
TAMU Datathon
March 2022 - May 2023
Director for Challenges and Logistics
  • Designing and grading challenges for participants in Major League Hacking's first ML/Data Science based hackathon.
Infolab
September 2021 - May 2023
Researcher
  • Researched methods to boost multi-task learning in Recommender Systems via scaling gradients of auxiliary tasks during backpropagation. Mostly using large scale E-commerce data.
Meta
Summer 2022
Machine Learning Engineer Intern, App Ads ML
  • Prototyped multi-task recommendation models that integrated user-view signal (e.g., watch time) that achieved improved Ad Conversion rates on Facebook and Instagram users.
  • Deployed Python utility automating cross domain learning (train on Android user data, test on iOS user data) for ads models to combat signal-loss from new iOS privacy restrictions.
  • Collaborated with research scientists to improve conversion rates of ads on Instagram and Facebook.
Paycom
Summer 2021
Software Engineer Intern, Automated Regression Testing
  • Developed a text based chat-bot with the capabilities to execute, analyze, queue, and edit regression tests.
  • Won company hack-a-thon with React-based leaderboard application to track health metrics (i.e., number of steps taken) across employees.
UrbanResilience.AI Lab
January 2020 - August 2021
Researcher
  • Led human mobility research quantifying changes in social network patterns due to COVID-19 lockdown.
  • Authored academic papers and invited to consult local governments to influence pandemic response strategies.
Ansira Partners, Inc.
Summer 2019
Human Resources Intern
  • Deployed “We University,” an internal learning library consisting of over 1,300 self-help courses available for all employees
McAfee
Summer 2018
Human Resources Intern
  • Partnered with Duke Corporate Education on the development and deployment of a global training program for all 6,000+ employees.
Farmbyrd (now closed)
2018 - 2019
Line Cook
  • Learned to make a fantastic fried chicken.
03 — Publications

Selected research

2022
Equality of access and resilience in urban population-facility networks
C Fan, X Jiang, R Lee, A Mostafavi
npj Urban Sustainability 2 (1), 9
PDF
2022
Is the data suitable? the comparison of keyword versus location filters in crisis informatics using twitter data
B Rachunok, C Fan, R Lee, R Nateghi, A Mostafavi
International Journal of Information Management Data Insights
PDF
2021
Fine-grained data reveal segregated mobility networks and opportunities for local containment of COVID-19
C Fan, R Lee, Y Yang, A Mostafavi
Scientific Reports
PDF