Continuum Computing Trustworthiness Research Team
We are conducting research on software science and technology to improve, evaluate, and certify the trustworthiness of continuum digital environments, with a particular focus on (1) continuum networks, (2) machine learning systems, and (3) software systems with uncertainty.
1. Trust management of continuum networks
Our first focus is to improve the trustworthiness of continuum networks. We develop methods for the trust management of networks, especially on network security management and operation.
2. Quality management of machine learning systems
Our second focus is on the quality of machine learning systems. We develop methods to improve and evaluate the implementations of machine learning algorithms, models, and systems from a software engineering perspective. We also develop the "Machine Learning Quality Management Guideline" to establish quality goals and development processes for products and services using machine learning.
- Machine Learning Quality Management Project (NEDO funded project)
[Publication: Machine Learning Quality Management Guideline]
3. Formal methods for software systems with uncertainty
Our third focus is to evaluate and certify the trustworthiness of software systems with uncertainty, such as cyber-physical systems. We develop formal methods for modeling and verifying software systems that deal with physical environments, probability, statistics, and so on. We also conduct foundational research on programming languages and interactive theorem provers.