AI and Technical Game Research

Video games provide a controlled environment for AI research, offering well-defined problems, reproducible experiments, and opportunities to generate large datasets through simulated gameplay. Many AI techniques have been developed or evaluated through games before being applied to broader domains. This motivated my research in technical game development and AI.

I worked at the Intelligent Computer Entertainment Lab (ICE Lab) at Ritsumeikan University, focusing on game AI, motion analysis, health assessment, and behavioral modeling. My research included AI based on Monte Carlo Tree Search (MCTS), as well as systems for analyzing player behavior and promoting improved user experience and well-being. The lab is recognized for its contributions to technical game research [kmjn.org/game-rankings].

My academic and professional involvement in this field includes:

• Part-time research consultant and lecturer at Bangkok University.
• Technical Committee member for Entertainment and Gaming (ENT).
• Member of the Ritsumeikan Center for Game Studies.
• Program Committee member for IEEE SeGAH (2019–2021).
• Organizing member of the Fighting Game AI Competition at IEEE CoG/CIG (2016–2021).
• Organizing Committee member, Webmaster, Graphic Designer, and Special Session Chair at IEEE CoG 2020.
• TPC member for IEEE GCCE (2019–2020).
• Reviewer for numerous journals and conferences.

Algorithm Engineering and Data Analytics

Algorithm Engineering and Data Analytics are central to my research and development work, with a strong foundation in mathematical modeling, statistics, algorithm design, optimization, and data analysis. I develop algorithms and analytical methods to transform complex data into measurable insights, decisions, and practical system functions.

Algorithm and Mathematical Modeling: My experience spans search and optimization algorithms, statistical and mathematical models, classification, data mining, machine learning, and deep learning. I have designed algorithms for real-world problems involving imperfect and heterogeneous data, with emphasis on systematic evaluation, statistical validation, and practical performance.

Statistical Analysis: My background includes statistical testing, experimental design, advanced statistical analysis, and quantitative evaluation. During my research career, I have guided students in designing data collection methods, selecting appropriate datasets, defining evaluation metrics, and applying statistical methods to validate research results.

Data Engineering and Analytics: I have practical experience designing databases, data pipelines, data-processing workflows, and analytical systems, working with structured and unstructured data. My experience includes SQL and database systems such as MySQL, Firebase, and IBM Db2, as well as data from applications, sensors, human behavior, health, images, text, audio, business operations, and financial markets.

Applied Data Experience: I have worked with diverse datasets including game and application logs, human motion and behavioral data, biosignals and health data, image data, text and speech, and business and financial data. This broad experience supports my ability to select appropriate mathematical, statistical, and algorithmic approaches for di

Health Monitoring and Behavioral Analysis

Health Monitoring and Behavioral Analysis have been a major focus of my research, combining AI, data analytics, mathematical and statistical modeling, motion tracking, and interactive systems to understand human behavior and assess physical and mental well-being.

My master’s thesis, “Office Worker Syndrome Monitoring Using Kinect,” developed an intelligent health monitoring system for office workers who spend extended periods at computers. The system combined motion tracking, data-mining classification, mathematical models, and behavioral analysis to detect prolonged sitting and identify potentially unhealthy postures. I developed algorithms for health-risk assessment, health-data visualization, and automated health summary reports, together with a microcontroller-based health monitoring assistant. The work was presented at international conferences and featured on the ScienceTech program on ThaiPBS TV in 2014.

My subsequent research expanded from individual health monitoring to behavioral analysis, games for health, and intelligent interactive systems. At the ICE Lab, a half of my publications were related to using games and interactive media to promote health and well-being. This work explored applications including depression, dementia, physical activity, movement behavior, and assessment of driving abilities in post-stroke patients, including research conducted in collaboration with hospitals.

More recently, this research direction has expanded into VR-based health assessment, rehabilitation, medical training, and multimodal behavioral analysis. By combining motion, physiological and behavioral data, AI, and immersive technologies, I develop systems that can support health assessment, training, rehabilitation, and evidence-based understanding of human behavior.

Business and Investment

Business, Investment, and Financial Engineering complement my technical background and provide a foundation for understanding how technology, data, and intelligent systems create practical business value. My academic background spans Business Administration, Science, and Engineering, allowing me to approach problems from both business and technical perspectives.

I hold a Bachelor of Business Administration (BBA) in Business Information Systems (BIS) from Assumption University of Thailand, where I developed a strong foundation in business processes, finance, accounting, marketing, information systems, e-commerce, and data analysis. My studies emphasized the use of information technology and data to support business operations, decision-making, and optimization.

My interest in investment developed alongside my interest in business and technology. Rather than approaching investment primarily as a quantitative problem, I am particularly interested in fundamental analysis and understanding the factors that drive markets. This includes studying company fundamentals, financial statements, industries, macroeconomics, economic conditions, government and monetary policies, geopolitical factors, and market psychology. I find the interaction between economic systems, policy decisions, business performance, and human behavior especially interesting.

This is somewhat different from my technical work, where I naturally enjoy mathematics, statistics, algorithms, and systematic analytical methods. In investment, I am more interested in understanding the broader context behind the numbers—why businesses perform differently, how economic and policy environments affect markets, and how investor behavior influences prices.

Together with my engineering and data-science background, this business and investment perspective helps me connect technical solutions with business processes, economic objectives, financial considerations, and real-world decision-making. I am particularly interested in the intersection of business, economics, finance, technology, AI, and data, where multidisciplinary understanding can lead to more effective and practical solutions.