About me
My name is Tas Sukastid, and I am currently pursuing my studies at the Faculty of ICT with a strong focus on data science and artificial intelligence.
I am passionate about transforming raw data into actionable insights through data analysis, machine learning, and AI-driven solutions.
My academic and project experiences include building predictive models, applying deep learning techniques, and leveraging Python-based tools for data preprocessing, visualization, and evaluation.
I am looking forward to opportunities in Data Analyst, Data Scientist, or AI Engineer roles where I can apply my analytical skills, programming expertise, and innovative mindset to deliver impactful solutions and contribute to data-driven decision-making.
My Skills
Programming & Development
Languages: Python, Java
Web Development: HTML, CSS, JavaScript,NodeJS
Databases: MySQL
Artificial Intelligence & Data Science
Machine Learning: Model building with PyTorch, scikit-learn
Deep Learning: Computer Vision, Time-Series Forecasting (ConvLSTM, UNet, Transformers)
AI Tools: segmentation_models_pytorch, NumPy, Pandas, Matplotlib
Practical Applications: Rainfall classification from satellite imagery, data preprocessing, evaluation (IoU, Dice, F1 metrics)
Soft Skills
Critical Thinking & Problem Solving
Teamwork & Collaboration
Time Management
Leadership
Education
Bachelor of Science in Digital Science and Technology
Faculty of Information and Communication Technology, Mahidol university
2023 GPA 3.46
2024 GPA 3.38
GPAX 3.40
Experience
Laboratory Assistant - Faculty of Information and Information Technology
2024 - Present
Developed and delivered Python programming tutorials to students, enhancing their understanding of programming concepts and applications.
Subject : Python, Generative AI
Research Internship – National Central University, Taiwan
Jun 2024 – Aug 2024
Conducted research on Rainfall Classification using Himawari-8 satellite imagery
Built and compared deep learning models (U-Net, SegFormer, ConvLSTM)
Developed data preprocessing pipelines (multi-band slicing, time-series input)
Applied evaluation metrics (IoU, Dice, F1) and advanced loss functions to improve model accuracy
Prepared research documentation, reports, and final presentations
Rainfall Estimation from Satellite Images
(Rainfall Classification)
Traditional weather radars have limited coverage and are costly to expand.
This project shows how satellite-based deep learning can provide wide-area, cost-effective rainfall classification for Taiwan.
Why this project?
What’s inside
Dataset: Himawari-8 (10 IR channels, 10-min cadence, 500 m–2 km resolution)
Model: U-Net++ encoder variants + LSTM sequence module
Experiments: Encoder comparisons, hyperparameter tuning, ablation studies
Metrics: Accuracy, IoU, mA, mIoU
Predicting rainfall from Himawari-8 satellite imagery using U-Net++ for spatial features and LSTM for temporal patterns.
My Projects
Explore my innovative projects showcasing my tech skills and creativity.
Online shopping website
Web Developer (Team Project)
Designed and developed a website using HTML, CSS, JavaScript, and Node.js.
Integ rated a MySQL database to manage product and user data.
Built RESTful APIs for handling operations like adding, deleting, and editing products
Laboratory Assistant - Faculty of ICT
Developed and delivered Python programming tutorials to students, enhancing their understanding of programming concepts and applications.
Subject
Python
Generative AI
MoneyQuest is a personal finance management mobile application developed using Flutter for a smooth and cross-platform user experience. The app allows users to track income, expenses, and savings goals, providing clear visual insights into their financial habits.
Firebase is used as the backend database to store and synchronize user data in real time, ensuring seamless performance and secure data management.
The project focuses on creating a simple, intuitive interface that encourages users to manage their finances effectively.
Mobile Application – MoneyQuest
My Certificate
CCNAv7: Introduction to Network
Gained foundational knowledge of computer networks, including:
Understanding TCP/IP and OSI models.
Configuring and troubleshooting network devices (routers and switches).
Subnetting and managing IP addresses.
Applying basic network security practices.