- Good at ML, AI, and Competitive Programming.
- Computer Software Engineering Auburn University. (2022-2026).
- Incoming Associate Engineer Intern at Chicago Trading Company (Summer 2025).
- Awarded Undergraduate Research Fellowship at Auburn University (Fall 2025).
- Honorable Mention for Outstanding Undergraduate Researcher Award from the Computing Research Association (2025).
- President of Auburn's Competitive Programming Team (Fall 2024 - Present).
- 1st Place at ICPC Regional at University West Florida Regional Competition (2023).
Experience
- Undergraduate Research Assistant - Machine Learning
- Auburn University | Mar. 2024 – Present
- - Working under Auburn's Dr. Pan He, focusing on computer vision and urban infrastructure optimization
- - Focused on deep learning, reinforcement learning, and computer vision using OpenAI Gym, PyTorch, OpenCV
- - Designing a hyperrealistic traffic simulator for AI-driven traffic control optimization
- - Investigated training-free solutions using Vision-Language Models
- Software Engineer Intern - Machine Learning
- PRADCO Outdoor Brands - Moultrie Mobile | May. 2024 – Aug. 2024
- - Designed ML model for animal re-identification using Python, Spark, MLflow, PyTorch
- - Implemented classification model handling 500+ images/second
- - Optimized ML inference speeds by 43% using Nvidia Triton Server and Kubernetes
- Software Engineer Intern - Backend
- PRADCO Outdoor Brands - Moultrie Mobile | May. 2023 – Aug. 2023
- - Reduced image load times by 71% through backend optimization
- - Developed scalable API endpoints using .NET Core and Azure
- Software Engineer Intern
- Chorus Smart Secure | May. 2022 – Aug. 2022
- - Developed internal tools for customer data management using React and Python
- - Designed a data analytics tool that located geographic regions for potential customers, resulting in a 12% increase in sales
Projects
- Person Re-Identification System
- Python, PyTorch
- Created a Person re-identification system using modified ResNet18 on Market-1501 dataset, achieving 93.48% rank-1 accuracy and 78.46% mAP
- View Project- Image Captioning with Multiple Decoder Architectures
- Python, PyTorch
- An evaluation of various decoder architectures (RNN, GRU, and LSTM) on the COCO 2017 dataset for image captioning
- View Project