SOFTWARE
ENGINEER

Leading end-to-end development of mission-critical flight test applications, architecting scalable solutions, and managing continuous deployment pipelines that support multiple aircraft programs.

• Architect and deploy multiple enterprise-level web applications using .NET Core and Azure, implementing CI/CD pipelines and managing version control across distributed teams
• Spearheaded development of the team's flagship application, delivering weekly releases with new features and optimizations that significantly improved data processing efficiency
• Designed and implemented cross-platform data pipelines processing terabytes of flight test data, utilizing parallel processing and custom libraries for real-time analysis
• Built and maintained a suite of 20+ desktop applications supporting flight test operations, with a focus on system integration and automated deployment strategies
• Reverse engineered innovative 3D visualization tools for aircraft configuration, replacing legacy systems and saving $50K+ annually in licensing costs

Contributed to flight simulator control systems development, implementing features and optimizing infrastructure components while learning industry-standard practices.

• Implemented critical features and resolved 15+ bug fixes for WPF control panel application used in flight simulator operations
• Optimized runway data processing system by implementing parallel computing techniques, improving simulator initialization efficiency
• Gained hands-on experience with Linux-based simulation environments and bash scripting for testing automation
• Actively participated in cross-functional team collaboration using GitLab for version control and CI/CD workflows

Advanced reinforcement learning research, focusing on model optimization and performance analysis using high-performance computing resources.

• Enhanced model performance through sophisticated hyperparameter tuning and epoch optimization techniques
• Leveraged campus HPC cluster, BEOCAT, for large-scale model training and validation experiments
• Worked with a Proximal Policy Optimization (PPO) algorithm to optimize model performance, and proved the effectiveness of our Attention-based Partially Decoupled Actor-Critic (APDAC) Model
• Developed data visualization and analysis pipelines to cross-validate model performance and support research findings
• Implemented stable baselines 3 support into our codebase to allow support for gymnasium environments to further validate our reinforcement learning model.