Computer Engineering student building useful systems from data to interface.
I enjoy connecting data preparation, backend development, machine learning, and clear user interfaces. My projects turn complex information into something people can inspect, understand, and act on—from air-quality dashboards to transaction review tools.
I take responsibility for the full project workflow: understanding the problem, building and testing the solution, deploying a demo, and documenting how it works.
Seeking: A full-time university co-operative education placement in data engineering, AI applications, backend development, or automation. Available 4 January–23 April 2027 and willing to relocate within Thailand. A longer placement can be discussed, subject to MFU approval.
B.Eng. in Computer Engineering · Mae Fah Luang University, Thailand
Fourth-year student · GPAX: 3.84
Coursework includes database systems, programming, networking, business data analytics, and big data technologies.
Student Work Assistant — Web & Data Projects
MLii Development Team · Mae Fah Luang University · May–July 2026
- Gathered requirements with nursing lecturers and wrote the Software Requirements Specification for a clinical scenario platform.
- Built a working interface prototype with Next.js, TypeScript, and Tailwind CSS.
- Designed the database schema, role-based access, API structure, and a proposed rule-and-LLM feedback workflow.
Delivered: Requirements documentation, UI prototype, and system design. Full backend and AI integration remain pending.
Senior project · Technical lead
A dashboard that helps users understand air-quality estimates, fire activity, and possible future scenarios through visualizations and plain-language AI advisory.
- My work: Data preparation and integration, weather and fire features, model comparison, dashboard development, and AI advisory integration.
- Workflow: Air4Thai + NASA FIRMS + weather data → prepared features → ML estimates → dashboard and contextual explanations.
- Built with: Python, LightGBM, XGBoost, Streamlit, GISTDA Sphere, and Gemini.
Model evaluation and interpretation
Adding fire features reduced LightGBM RMSE from 7.3237 to 6.9878 µg/m³ on the same 120 held-out days in January–April 2022.
These are historical evaluation results. The dashboard's current value is a model estimate; future daily outputs are scenario projections. The AI advisory explains supplied model and environmental context.
Personal project · Deployed demo
A review workspace that helps a reviewer inspect unusual transaction patterns, follow the evidence, and record checks and next steps.
- My work: Java REST APIs, SQL-backed review records, React interface, rule logic, AI explanation integration, deployment, and technical documentation.
- Workflow: Transactions → Java rule findings and priority score → evidence and optional AI explanation → human review.
- Built with: Java, Spring Boot, React, TypeScript, PostgreSQL/Supabase, Docker, Render, and Gemini.
Live demo · Source code · Technical documentation (PDF)
Uses fictional transactions. The score prioritizes review; it is not a probability of fraud. AI explanations support the reviewer, who makes the decision.
| Project | What I built | Links |
|---|---|---|
| AI Vision QC Inspector | MobileNetV2 defect-classification demo with a Gemini-assisted quality report. Recorded 89.4% validation accuracy in a training run. | Demo · Code |
| Chiang Mai Tri-Node PM2.5 | Exploratory geospatial dashboard combining fire and weather information with AI-assisted scenario explanations. | Demo · Code |
| Clinical Scenario & AI Feedback | Requirements, system design, and a Next.js interface prototype for nursing education. Backend integration pending. | Code & documentation |
| Area | Technologies and practices |
|---|---|
| Programming | Python, Java, SQL, TypeScript, JavaScript |
| Data | PostgreSQL, Supabase, data cleaning, feature engineering, API integration |
| Backend & Web | Spring Boot, REST APIs, React, Next.js, Tailwind CSS |
| ML & AI | LightGBM, XGBoost, scikit-learn, TensorFlow, Gemini integration, model evaluation |
| Delivery & Visualization | Git, GitHub, Docker, Render, Streamlit, technical documentation |
Coursework and certificate exposure: Hadoop, Spark, Kafka, and Google Cloud.
Currently learning: RAG systems, PyTorch, and workflow automation.
- IBM: Data Engineering Professional Certificate
- Google: Data Analytics Professional Certificate
- Google Cloud: Build Data Lakes & Data Warehouses
- UC Davis: SQL for Data Science
- Google: Prompting Essentials
- Google: AI Essentials