CTO | Panacea (Panacea-tech.com)
Austin, TX | Nov 2024 -- present

• Developing a platform with React Native that connects outpatients with personalized resources.
• Led technical development team of 4 through iterative improvements to code hygiene and detailed specifications.
• Set up the (Flask) REST API, MongoDB database, and AI-driven recommendation system on AWS.
• Published the app initially for 20 local users; scaling app with web-scraping pipeline for potential resources.

Computer Science Tutor | Cinco Learning
Katy, TX | Nov 2023 -- present

• Preparing students for the USACO bronze level by teaching competition programming logic and C++.
• Tutoring students in high school data structures and algorithms classes, including Java Swing Graphics.

Mobile App Developer | FleetSmart Apps (fleetsmart.biz)
Austin, TX | Nov 2023 -- May 2024

• Developed a mobile application for a self-growth platform merging web and native features.
• Launched the organization's first mobile platform.

Mobile App Developer | Catalyst Collective (purposepro.org)
Austin, TX | Mar 2023 -- Nov 2023

• Developed and published the mobile version of the Purpose Project platform on Google Play and the Apple Store.
• Integrated and improved existing codebase of 5000+ lines as the sole developer.

Full Stack Developer | EduBeyond (edubeyond.org)
Vancouver, Canada | Mar 2023 -- Feb 2024

• Developed an AI-integrated LMS for educational and corporate training using React, Next.js, AWS, and NextAuth with a team of 8.
• Implemented: natural-feeling announcements, messaging, document handling, generative AI integration, and authentication; supported by backend logic and (nonrelational) database schemas.
• Scaled the platform website (a prototype at the time of joining) to support the first 30 thousand paying customers.

Research Assistant | Renewable Power Grid Lab | University of Houston
Houston, TX | Dec 2022 -- Dec 2023

• Applied statistical machine learning techniques to forecast electrical loads on the ERCOT power grid using Python and TensorFlow.
• Tuned and optimized deep neural network algorithms to the time-series dataset, including experimentation with SVM, FCNN, LSTM, CNN, ablation studies, and encodings and activation functions.
• Publication: Jonathan Yang, Mingjian Tuo, Jin Lu, and Xingpeng Li, “Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting” 2024 IEEE Texas Power and Energy Conference (TPEC). ieeexplore.ieee.org/document/10472183