Education and Professional Experiences.

Education

University of Georgia · Athens, GA · Expected Dec 2027

B.S. in Computer Science, Minor in Mathematics

  • Coursework: Data Structures, Algorithms, Data Science and Machine Learning, Systems Programming (C/C++), Linear Algebra, Statistics
  • Certificates in Applied Data Science and Artificial Intelligence

Software Engineer

UGA LLM Labs, V3RT3X Solutions · Athens, GA · Sep 2026 – Present

  • Built a degree-planning prototype used by 15 UGA students. It models 60+ courses and 100+ prerequisites as a directed acyclic graph and uses Kahn’s topological sort in TypeScript to order courses and detect cycles.
  • Generated multi-semester plans with zero prerequisite violations across 10 test scenarios. The planner works out which courses a student can take from what they’ve completed and highlights locked courses and critical dependency chains.

Software Engineer Intern

Adtran · Norcross, GA · Jun 2026 – Aug 2026

  • Cut regression test runtime from 8 hours to 1 hour by building a Groovy/Jenkins pipeline on my own that tests server nodes and network topology in parallel across 10 VMs. 20+ engineers across 6 teams use it every week.
  • Raised virtual node startup success from 20% to 90%. I traced Linux logs over SSH to a missing two-core CPU allocation, then worked with an international engineering team to verify and fix the configuration.
  • Cut pipeline setup time from 20 minutes to about 2 by letting users pick suite, branch, test case and artifact as parameters, and by calculating the optimal number of instances automatically.
  • Wrote 25 pages of documentation covering architecture, parallelization, troubleshooting and failure recovery, so other teams can set up and maintain their own pipelines.

Computer Vision Researcher

Georgia Tech VIP, FlameJackets · Atlanta, GA · Aug 2025 – Mar 2026

  • Collected and cleaned 1,000+ fire and smoke images into a labeled training set for the PyTorch YOLO models behind a wildfire detection system.
  • Raised precision from 75% to 91% while holding recall at 92%. I found the model was flagging red and orange objects as fire, then retrained and tuned the YOLOv8 model.
  • Raised mAP@50 from 80% to 94% by removing duplicate and blurry images, and documented the dataset quality metrics and training configurations the team uses.