I’m a senior Computer Science student passionate about full-stack development, artificial intelligence, and automation. I enjoy building intelligent, scalable systems that solve real-world problems, from digital twins and computer vision to backend APIs and AI-powered applications. I’m driven by creating reliable technology that makes complex systems simpler, smarter, and more efficient.
ResumeMissouri University of Science and Technology
2024 – Present
As a Full-Stack Developer at Missouri S&T, I work on developing a distributed digital twin platform for advanced manufacturing systems, including CNC machines, laser cutting systems, and metal 3D printers. I build scalable backend services using FastAPI and develop responsive interfaces with React and TypeScript to provide real-time visibility into machine operations. I have implemented low-latency WebSocket communication for continuously streaming machine telemetry, production status, and manufacturing events. I also develop REST APIs and computer vision pipelines using OpenCV to support automated print defect detection and part-removal monitoring, helping improve manufacturing reliability and automation.
During my Software Engineering Internship at Missouri S&T, I developed a manufacturing data platform designed to manage and process large volumes of experimental and sensor data. I built scalable Python ETL pipelines capable of ingesting, validating, and processing more than 60,000 manufacturing files for machine learning, process optimization, and research analytics. I also developed a Flask and PostgreSQL backend for dataset management, metadata extraction, and data retrieval, reducing manual data entry by approximately 85%. Working closely with researchers and cross-functional teams, I helped design reliable data workflows supporting Design, In-Process, and Post-Process manufacturing analysis while testing APIs and pipelines to ensure consistent system performance.
As a Software Developer at Missouri S&T, I developed a MATLAB-based system for 3D modeling and visualization of soil specimens during triaxial shear testing. The application helped researchers analyze specimen geometry and deformation with measurement accuracy exceeding 99.9%. I automated the detection and correction of issues such as specimen misalignment, eccentricity, and leakage, improving the reliability of experimental data and reducing manual analysis. I also validated computational models against real-world test measurements to ensure accurate geotechnical simulations and stress-strain analysis.
Full-stack expertise across modern development, AI, and cloud infrastructure.
An autonomous system that transforms Figma designs into structured application representations — using design graphs, deterministic analysis, semantic reasoning, context slicing, schema-constrained inference, provenance tracking, and automated code generation.
A real-time digital twin platform for remotely monitoring and controlling CNC, FDM, and resin manufacturing machines from a centralized dashboard. Streams live machine telemetry over WebSockets, supports G-code execution and machine controls, provides live video monitoring, and integrates multiple physical machines through a scalable FastAPI backend.
A real-time AI safety system that analyzes camera and audio streams to detect falls, fights, collapses, and other emergencies. Combines computer vision, speech recognition, and multimodal AI to classify incident severity and route emergency information through a live monitoring dashboard. Built for the Google Build with AI Hackathon at Washington University in St. Louis.
A Deep Q-Network that learns to play Snake from scratch — an 11-feature state, relative turn actions, experience replay with a target network and shaped rewards — plus a Pygame app where you can play yourself or watch any trained model run.
A full-stack school transportation application built around safe commute booking — a Flutter mobile client, a Python/FastAPI backend, and separate rider and driver workflows.