Machine Learning and Intelligent Systems (MLIS)

Research rationale

Machine learning and intelligent systems have made tremendous advancements in recent years, leading to a growing demand
for new and more advanced technologies. The primary reason for this is the increasing need for automated systems that can process and
analyze large amounts of data and make decisions based on that data. This is particularly important in industries such as finance, healthcare,
and transportation, where large amounts of data are generated and processed on a daily basis.

One of the main drivers of the advancement in machine learning and intelligent systems is the growing availability of data. With the increasing use of digital technologies, the amount of data being generated has increased dramatically, providing vast amounts of
information for machine learning algorithms to learn from. This has led to the development of more advanced algorithms that can handle large amounts of data and extract meaningful insights from it.

Another reason for the advancement in machine learning and intelligent systems is the increasing need for systems that can make decisions in real-time. This is particularly important in industries such as finance and healthcare, where quick and accurate decisions are critical. Machine learning algorithms have been developed that can process and analyze large amounts of data in real-time, enabling them to make decisions in a matter of milliseconds.

Finally, the advancement in machine learning and intelligent systems is also driven by the need for systems that can operate autonomously. There is a growing demand for systems that can work independently, without the need for human intervention. This is particularly important in industries such as transportation and logistics, where autonomous systems are needed to improve efficiency and reduce costs. Machine learning algorithms have been developed that can operate autonomously, making decisions and taking actions based on the data they receive.

Main research areas/topics

• Interpretability and Explainability of Deep Learning Models
• Adversarial Reinforcement Learning
• Multi-Modal Learning
• Adversarial Attacks and Defenses
• Pre-training and Transfer Learning in NLP
• Attention Mechanisms in NLP
• Explainable AI (XAI) in NLP
• Generative Adversarial Networks (GANs) for Image Synthesis
• Imitation Learning and Inverse Reinforcement Learning

Members of Research Group

Head of Reseach Group

Professor Sartra Wongthanavasu

Members

Contact person

Professor Sartra Wongthanavasu, wongsar@kku.ac.th