Hi, I'm Moksh Shah

Building the brain behind autonomous aircraft — I develop reinforcement learning systems that enable drones to navigate complex environments using real-time computer vision. My work has driven a 65% improvement in autonomous mission success rates through deep learning model optimization and systematic hyperparameter tuning. I architect the MLOps infrastructure that takes models from research notebooks to production-grade deployment, and push the boundaries of training data quality using neural radiance fields (NeRF) and Gaussian Splatting to generate photorealistic synthetic environments. More recently, I've been building full 3D reconstruction pipelines using COLMAP and Gaussian Splats, creating detailed visual models of real-world environments that support mission planning and spatial reasoning for autonomous systems

Designed and shipped UI enhancements and gamification features across client-facing fintech products, resulting in a 40% lift in user satisfaction and engagement. I also built an internal Angular application for automated REST API endpoint validation — a tool that enabled real-time monitoring and continuous integration testing, replacing hours of manual QA with a single dashboard.

Took ownership of performance engineering for enterprise-scale distributed systems, executing comprehensive load testing that validated 99% reliability under peak traffic conditions. I also architected and maintained Spring Boot microservices within the company's core API infrastructure, delivering modular backend solutions that directly supported critical business operations.

Conducted applied research at the intersection of game theory and computational economics, developing Python-based simulation frameworks to model strategic behavior in duopoly markets — specifically analyzing how competing firms optimize advertising-sponsored Wi-Fi provisions. In parallel, I built algorithmic pricing models to study consumer surplus dynamics in ride-sharing platforms, work that sharpened my foundation in mathematical modeling, optimization, and translating complex economic theory into code — skills that now directly inform how I approach ML system design.