Research

My research interests lie primarily in Statistical Machine Learning and Learning under uncertainty. Here are some of my current projects:

Machine learning for astronomy

Applying advanced machine learning models to identify, classify, and analyse phenomena in astronomical datasets.

Learning from noisy labels

Developing robust learning algorithms that maintain high predictive performance despite imperfections in the training labels.

High-dimensional data analysis

Researching methodologies to efficiently manage and learn from datasets characterised by a massive number of features.

Research Group

Current Members

  • Aomsap Inkongngam (PhD Candidate)
  • Watcharakiart Insri (PhD Candidate)
  • Radchanon Plammanus (MSc Student)
  • Krittamet Promsen (MSc Student)
  • Paramate Phuengtrakul (MSc Student)
  • Nattanan Prompanya (MSc Student)
  • Natthaphong Tangchaiphat (MSc Student)
  • Sukanya Meethong (MSc Student)

Alumni

  • Putanyn Manee: Evaluating Constrained and Unconstrained Machine Learning Strategies for Credit Risk Optimization in Financial Institutions (2026)
  • Sun Da: Empirical Study on Using Random Class-Label Noise to Prevent Model Overfitting (2026)
  • Xiaofan Zhou: Video Sharing Platform Data Extraction: Transforming Images into Structured Data (2025)
  • Taned Singlor: Multi-view Globular Cluster Detection (2024)
  • Nathakit Kaewtoomla (co-supervised) working on sentiment analysis (2024)
  • Yosawimon Attawong (co-supervised) worked on fabric pattern analysis
  • Vasin Jinopong (co-supervised) worked on matrix factorisation for recommender systems
  • Todsapol Kuntharos: Thai Characters Recognition Through Hand Movements in Real-time Video Using Deep Learning Technique on Low-powered devices
  • Parinya Punsin: Optimising Misclassification Cost for Cost-sensitive Learning on Imbalanced Datasets for Patient Diagnosis Data
  • Watcharin Sarachai (co-supervised) worked on "Orchids images classification"
  • Piyawat Khunsongkiat (co-supervised) worked on "Content-based Image Retrieval"
  • Sangapong Punyakaew (co-supervised) -- "Reinforcement learning for stock price forecasting"
  • Nida Chinudomsub -- "Learning to classify counterfeit products from unreliable data"
  • Kraisorawat Punyo (co-supervised) -- "Online regression model for estimating public transport arrival times"
  • Pichayanart Reerak -- "Analysis of Factors Affecting the Robustness of Convolutional Neural Network Towards Random Noisy Label"