Hi, I'm Andrew Sumner
Software Engineer, currently working in financial technology. I build tools for the communities I'm a part of.

Selected Highlight Project and Corresponding Design Documents
Parkscout.io - National Park Mapping Tool and Trip Planner
This ongoing project is intended to be a one-stop shop for all the data and tools needed to plan a trip to a U.S. National Park.
About Me
Software Engineer with ~4 years of experience, primarily backend-focused, utilizing Python, Typescript, and AWS.
My experiences at Capital One have allowed me to design, build, and deploy high-impact products from the ground up, including a real-time marketing decision platform that now handles over 100 million daily records and supports major campaigns. I also played a key role in developing a self-service kiosk for cashier's checks, guiding it through pilot testing and supporting its production deployment across multiple locations.
With a background in both computer science and physics, I bring strong analytical and problem-solving skills to each project, helping me navigate complex challenges across the full development lifecycle. I'm passionate about bringing practical, robust solutions to life.
In my personal time, I love staying up-to-date with new technologies and am currently building a web app focused on outdoor recreation, powered by large language models. If you're interested in connecting, let's chat!
Experience
Quantitative Software Engineer at Campbell & Company
December 2024 - Present
Senior Associate Software Engineer at Capital One
August 2021 - October 2024
Education
University of Maryland, College Park
BS in Computer Science & BS in Physics, 2021
Minor in Technology Entrepreneurship • GPA: 3.52
Skills
Programming Languages
Frameworks & Libraries
Tools & Platforms
Other Skills
Featured Projects
parkscout.io - National Parks Explorer
Interactive web application for visualizing and exploring U.S. National Parks data. Ongoing project.
Park Visitation Analysis
Data analysis and visualization of National Park visitor patterns over time, including seasonal trends and impact of various factors on park popularity.

Land Use Prediction
Built a convolutional neural network using TensorFlow to classify land use from satellite imagery with 85% accuracy on test data.

Panorama Image Stitching
Developed complete image stitching solution implementing advanced computer vision algorithms including SIFT features and RANSAC.

Galaxy Classification
Created CNN using PyTorch to classify galaxy types from 18,000 images, achieving 70% accuracy despite hardware limitations.