All Projects

A comprehensive showcase of my work across different technologies and domains

Suitcube AI - Image 1
Live
Featured
Suitcube AI
2024
AI pose detection module that automates image capture to find the perfect suit size using Tensorflow.js and PoseNet
TensorFlow.js
PoseNet
React
TypeScript
Computer Vision

Key Features:

  • AI pose detection
  • Automated image capture
  • Perfect fit algorithm
ScreenCloud Player Service - Image 1
Live
ScreenCloud Player Service
2024
Mission-critical digital signage platform serving 250,000+ screens globally with 24/7 uptime
Electron
TypeScript
React
TailwindCSS
Docker
AWS
GraphQL

Key Features:

  • 250,000+ active screens
  • 1.2M concurrent users
  • 5,000 transactions/sec
Vaccine Distribution System - Image 1
Live
Featured
Vaccine Distribution System
2021
Scalable vaccine distribution system for Thai hospitals handling millions of concurrent users
React
TypeScript
GoLang
CloudFlare
Docker
GCP
LINE API

Key Features:

  • 1.2M concurrent users
  • 5,000 transactions/sec
  • Hospital integration
UP A Notch Steakhouse Delivery App - Image 1
Live
Featured
UP A Notch Steakhouse Delivery App
2024
Premium steakhouse delivery platform for UP A Notch, featuring seamless online ordering, real-time order tracking, and LINE integration. Built as a lightning-fast PWA with a modern, scalable cloud backend. Elevated the restaurant's digital presence and customer experience.
React
TypeScript
PWA
Firebase
GCP
LINE API

Key Features:

  • Progressive Web App: installable, offline support, push notifications
  • Integrated LINE API for seamless customer engagement
  • Real-time order management with Firebase
  • Cloud-native, scalable infrastructure on GCP
  • Elevated digital experience for a premium steakhouse brand
Voice for Blind - Image 1
Hackathon Winner
Featured
Voice for Blind
2017
Hackathon 2nd place winner (2017). An accessibility app that voices out the medication in front of the camera, empowering blind and visually impaired users to independently identify their medicine. Utilizes real-time image recognition and text-to-speech to provide instant audio feedback.
Android
Java
OpenCV
Tesseract OCR
Text-to-Speech
Google Cloud Vision

Key Features:

  • 2nd place at national hackathon for accessibility
  • Real-time medication recognition using camera
  • Text-to-speech for instant audio feedback
  • Empowers blind and visually impaired users
  • Lightweight and runs on standard Android devices
Sansri Worker On-Site Tracker & Visualization - Image 1
1st Place Winner
Sansri Worker On-Site Tracker & Visualization
2024
1st place winner (2024). A prototype app for Sansri, a leading building construction company, to solve pain points in worker management. The system enables real-time on-site worker tracking and visualizes building progress, streamlining construction operations and improving transparency.
React
TypeScript
Express.js
MongoDB
Docker
Docker Compose
Cytoscape.js

Key Features:

  • Built a prototype for Sansri to address on-site worker system pain points
  • Real-time worker tracking and building progress visualization
  • Utilized React, TypeScript, Express.js, MongoDB, Docker, and Cytoscape.js for graph visualization
  • Streamlined construction operations and improved transparency
  • Awarded 1st place for innovative solution in construction tech
PCOS Prediction ML Algorithm Benchmarks - Image 1
Open Source (GitHub)
PCOS Prediction ML Algorithm Benchmarks
2025
A comprehensive benchmark of machine learning algorithms for predicting Polycystic Ovary Syndrome (PCOS). Compared Logistic Regression, Random Forest, and Decision Tree models to identify the most effective approach for PCOS diagnosis using real-world clinical datasets.
Python
Jupyter Notebook
scikit-learn
Pandas
Matplotlib
Seaborn

Key Features:

  • Benchmarked Logistic Regression, Random Forest, and Decision Tree for PCOS prediction
  • Analyzed model performance using accuracy, precision, recall, and ROC-AUC metrics
  • Visualized feature importance and model comparison for clinical interpretability
  • Demonstrated end-to-end ML workflow: data preprocessing, training, evaluation, and reporting
  • Open-sourced for reproducibility and further research in women's health AI
Tic Tac Toe Deep Q-Learning with TensorFlow.js - Image 1
Live
Featured
Tic Tac Toe Deep Q-Learning with TensorFlow.js
2025
An interactive Tic Tac Toe game powered by Deep Q-Learning, built with React, TypeScript, TailwindCSS, and TensorFlow.js. The AI starts with zero knowledge and learns in real-time as users play, improving its strategy with every move. This project demonstrates reinforcement learning concepts in a fun, visual, and hands-on way directly in the browser.
React
TypeScript
TailwindCSS
TensorFlow.js
Deep Q-Learning
Reinforcement Learning

Key Features:

  • Implemented Deep Q-Learning agent that learns to play Tic Tac Toe from scratch through user interaction
  • Real-time training and model updates in the browser using TensorFlow.js
  • Visualizes the AI's learning progress and decision-making process
  • Built with React, TypeScript, and TailwindCSS for a modern, responsive UI
  • Showcases reinforcement learning concepts in an accessible, interactive format
LSEG Workspace: Real-Time Financial Data Platform - Image 1
Live
LSEG Workspace: Real-Time Financial Data Platform
2019
A high-performance, real-time financial analytics platform for professional traders and analysts, developed at LSEG. Workspace delivers millions of data points per minute with a seamless desktop experience, leveraging Electron, TypeScript, Redux, and IndexedDB for robust state management and offline capabilities. My contributions focused on optimizing data pipelines, reducing load times, and enhancing user experience for mission-critical workflows.
Electron
TypeScript
Redux
IndexedDB
CSS
WebSockets
High-Performance Data Streaming

Key Features:

  • Engineered real-time data ingestion and rendering for millions of financial data points per minute
  • Optimized application startup and data loading, reducing initial load time by 40%
  • Implemented advanced caching and offline support using IndexedDB for seamless user experience
  • Collaborated with cross-functional teams to deliver new analytics features and improve UI responsiveness
  • Ensured robust state management and data consistency with Redux in a complex Electron environment
  • Contributed to mission-critical workflows used by global financial professionals
RCST Webinar System - Image 1
Live
RCST Webinar System
2023
A comprehensive web platform for the Royal College of Surgeons of Thailand (RCST) to facilitate surgeon registration, examination scheduling, and license management. The system enables doctors to sign up, schedule their licensing examinations, and receive results. Administrators can manage users, educational resources, examination dates and venues, and publish pass/fail results. Built with a modern stack to ensure reliability, scalability, and security for critical medical certification workflows.
TypeScript
React
GCP
Firebase
PostgreSQL

Key Features:

  • Developed secure user registration and authentication for surgeon candidates and administrators
  • Implemented scheduling and management of examination dates, venues, and resources
  • Enabled real-time announcement of examination results (pass/fail) to candidates
  • Built robust admin dashboard for managing users, educational content, and exam logistics
  • Leveraged GCP and Firebase for scalable backend and real-time data updates
  • Integrated PostgreSQL for reliable data storage and complex queries
  • Ensured compliance with privacy and security standards for sensitive medical data
Roo Pai - Image 1
Prototype
Roo Pai
2019
Semifinalist (top 10 out of 2500+ teams globally) in the IBM Call For Code Hackathon. Invented and developed a pre & post worldwide disaster solution platform to aid communities before, during, and after disasters. The platform leverages IBM Cloud, Watson NLU, Android, Java, Docker, and MongoDB to provide real-time information, resource coordination, and communication for affected populations and responders.
IBM Cloud
Watson NLU
Android
Java
Docker
MongoDB

Key Features:

  • Designed and implemented a scalable disaster management platform for global use
  • Integrated real-time data feeds and communication tools for disaster response
  • Utilized IBM Cloud services and Watson NLU for backend infrastructure, deployment, and natural language understanding
  • Developed Android application for on-the-ground user access and reporting
  • Employed Docker for containerized deployment and MongoDB for flexible data storage
  • Leveraged Watson NLU to analyze and extract insights from user reports and disaster-related communications
  • Recognized as a top 10 semifinalist from over 2500 teams worldwide in the IBM Call For Code Hackathon