Ronald Lee

Contact info: (214) 799-8936 · leeroddy17@gmail.com

Education: Computer Science, Texas A&M

Hey there! I'm a fourth-year computer science student at Texas A&M University. I'm broadly interested in network science and machine learning. Previously, I've worked at Meta as an ML Engineer Intern on the App Ads ML Team where I used multi-task learning to improve model performance for Facebook + Instagram ad recommendation. I've also researched shifting network dynamics during natural disasters at the Urban Resilience AI Lab . Whenever I have free time, I enjoy playing chess, reading novels, and practicing the acoustic guitar!

Currently, I'm doing research on gradient balancing in multi-task learning with Dr. James Caverlee as well as designing ML challenges for TAMU Datathon . I'm also currently looking for full-time opportunities anytime after May 2023.




Experience

Meta

ML Engineer Intern

May 2022 - August 2022

TAMU Datathon

Director for Challenges and Logistics

March 2022 - Present

infolab

Undergraduate Researcher

September 2021 - Present

Paycom

Software Developer Intern

At Paycom, I worked with the QA Team to develop a text based chat-bot with the capabilities to execute, analyze, queue, and edit regression tests. I implemented an NLP-based parser using ML.NET that could read in any text input and map keywords to the intended task, and perform that task efficiently. I also was able to use Paycom's internal API to implement the functionalities of the bot, such as execution and thorough analysis of tests. We used a C# backend for this project and a React Front-end component to integrate with Paycom's internal application. Furthermore, I also participated in a company hack-a-thon and used React to develop an interactive health leaderboard application.

May 2021 - August 2021

UrbanResilience.AI Lab

Research Fellow

At the UrbanResilience.AI Lab, my research focuses on network science, where I analyze complex human interactions. One of my lasting contributions in the lab is creating a data processing pipeline that filtered billions of rows of anonymized geospatial data (coordinates, timestamps, ids, geometries, etc) into local contact networks for target counties. Through this, we were able to identify different interactions between anonymized users and calculate metrics to identify hidden risk in regard to the spread of COVID-19. I was also able to use Statistical and Machine Learning based models to help us uncover metrics that contributed heavily to pandemic spread, and it allowed us to guide local policy actions to mitigate this risk. You can read more about this in my publications linked in my google scholar!

January 2020 - August 2021

Ansira Partners, Inc.

Human Resources Intern

At Ansira, I helped in the deployment “We University,” an internal learning library consisting of over 1,300 self-help courses.

June 2019 - August 2019

McAfee

Human Resources Intern

At McAfee, I partnered with Duke Corporate Education on the development and deployment of a global training program for all 6,000+ employees.

June 2018 - August 2018