Research Profile — Krid Jinklub

Summary

Krid Jinklub is a lecturer and Principal Investigator of the CAMT WEB3 Research Group at Chiang Mai University, working on distributed ledger technologies, tokenomics design, and AI theory. He is a Deputy Editor-in-Chief of the Journal of Experimental & Theoretical Artificial Intelligence (Taylor & Francis).

Research interests

His work connects the design of decentralized systems with the mathematical and ethical foundations of AI. It spans four listed areas of expertise:

• Distributed ledger technologies and tokenomics — value-aligned token economies and workflows for Web3 transformation planning, the core of the CAMT WEB3 group's gamification and tokenomics pillar.
• Quantum neural networks — co-author of work mapping quantum neural networks onto spin-networks so their behaviour can be analysed with topological quantum field theory.
• Ethical AI — the societal implications of automated and code-as-law systems.
• AI-assisted pedagogy — teaching generative AI for digital innovation to undergraduates.

Earlier work (2018) extended a grid-based clustering algorithm (HASTA) to time-series data for ARIMA forecasting. In 2024, he applied AI to indoor air quality, analyzing PM2.5 at Chiang Mai International Airport.

Selected publications

1. Jinklub, K., & Sangamuang, S. (2026). Designing Value-Aligned Tokenomic Workflows for Web3 Transformation Planning. 2026 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), pp. 172–175. IEEE.
2. Addazi, A., Belotsky, K., Beylin, V., Bikbaev, T., Chen, D., Fabrocini, F., Giagu, S., Jinklub, K., et al. (2026). Machine Learning for Multi-Messenger Probes of New Physics and Cosmology: Review and Perspective. Symmetry, 18(7), 1116. Preprint: arXiv:2604.22462.
3. Fabrocini, F., Jinklub, K., & Marcianò, A. (2026). Keeping with our historical roots: experimental and theoretical Artificial Intelligence. Journal of Experimental & Theoretical Artificial Intelligence, 38(1), 1–3. Editorial.
4. Jinklub, K., Srika, P., & Malang, C. (2025). Optimization Strategies for Enhancing the Usability of Location-based Mobile Application. 2025 5th International Conference on Electrical, Computer and Technologies (ICECET).
5. Jinklub, K. (2024). Toward Sustainable AI for Indoor Air Quality Management: A Case Study on PM2.5 Analysis at Chiang Mai International Airport. Journal of Asian Institute of Low Carbon Design, 10(1), 193–198.
6. Jones, D., Clemente, V., Corazzo, J., Lotz, N., Nielsen, L. M., et al. (2023). Learn X Design 2023 Editorial.
7. Marcianò, A., Chen, D., Fabrocini, F., Fields, C., Greco, E., Gresnigt, N., Jinklub, K., Lulli, M., Terzidis, K., & Zappala, E. (2022). Quantum Neural Networks and Topological Quantum Field Theories. Neural Networks, 153, 164–178.
8. Marcianò, A., Zappala, E., Torda, T., Lulli, M., Giagu, S., Fields, C., Chen, D., et al. (2022). Deep neural networks as the semi-classical limit of topological quantum neural networks: The problem of generalisation. arXiv:2210.13741.
9. Marcianò, A., Chen, D., Fields, C., Gresnigt, N., Jinklub, K., Lulli, M., Terzidis, K., et al. (2020). Deep neural networks as the semi-classical limit of quantum neural networks. arXiv:2007.00142.
10. Jinklub, K., & Geng, J. (2018). Hierarchical-grid clustering based on data field in time-series and the influence of the first-order partial derivative potential value for the ARIMA-model. Advanced Data Mining and Applications (ADMA 2018), pp. 31–41. Springer.