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    Research Summary

    1. Modelling and reasoning with uncertainty. This research is concerned with modelling, reasoning, and fusing uncertain information in decision support and intelligent systems. We particularly focus on Dempster-Shafer theory of evidence, fuzzy-set/possibility theory, and probability theory.
      • Modelling uncertainty, vagueness and imprecision using these formalisms.
      • Fusion and conflict/inconsistency analysis among multiple piece of information within these theories.
      • Application: recommender systems, multi-attribute/criteria decision making under uncertainty and imprecision, Kansei engineering.
    2. Data mining, Machine learning, Data analytics. This research is mainly concerned with developing Machine Learning and Data Mining algorithms to discover knowledge from data.
      • Explainable ML
      • Similarity measures and k-means like clustering algorithms for large categorical and mixed data.
      • Hybrid models (ARIMA, neural networks, etc.) for prediction with time series data.
      • Algorithms for sentiment analysis of customer reviews and recommender systems.
      • Algorithms for anomaly detection/prediction for intelligent decision support.
    3. Decision analysis. This research aims to develop decision methodologies that combine methods in operations research with CI techniques to support decision making under various types of uncertainty and imprecision.
      • Evidential reasoning approaches to multi-attribute decision making under uncertainties.
      • Target-based decision models.
      • Group decision making with linguistic information.
      • Application: personalized recommendation, screening innovations, supplier/partner evaluation and selection, etc.

    Research Grants

    • Co-PI: ONR Grant no. N62909-23-1-2058 “An Integrated Approach to Explainable Machine Learning” (06/2023 - 05/2026).
    • PI: ONR Grant no. N62909-19-1-2031 “Multi-Source Learning Coupled with Reasoning Capabilities for Large-Scale Decision Support Environments” (02/2019 - 01/2022).
    • PI: AOARD Grant no. FA2386-17-1-4046 “Integration of Clustering with Semantics Learning for Massive Categorical and Mixed Data” (08/2017 - 08/2019).
    • PI: “Random Set Based Modeling of Emotional Opinions in Kansei Evaluation Analysis”, JAIST's Research Grant (FY 2015) (700K JPY).
    • co-I: Grant-in-Aid for Scientific Research (A) No. 25240049 “Research on a Dynamic Service Value Co-creation System Model” from Japan Society for the Promotion of Science (FY 2013–2016) (36.1 M JPY).
    • PI: “A Knowledge Science Based Approach to Strategic Decision Analysis with Integrated Uncertainty”, JAIST's Research Grant (FY 2013) (1M JPY).
    • co-I: SCOPE-project “R&D of Kansei Information Transmission Techniques Aiming at Promotion of Traditional Industry in Ishikawa Prefecture” (FY 2010-11, PI: Prof. Y. Nakamori) granted by Ministry of Internal Affairs and Communications (17.6 M JPY).
    • co-I: Grant-in-Aid for Scientific Research (B) No. 22300074 “Target-Oriented Kansei Decision Analysis” from Japan Society for the Promotion of Science (FY 2010-13, PI: Prof. Y. Nakamori) (13.7 M JPY).
    • PI: Grant-in-Aid for Scientific Research (C) No. 20500202 from Japan Society for the Promotion of Science (FY 2008-10) (3.5 M JPY).
    • co-I: Grant-in-Aid for Scientific Research (C) No. 19500174 from Japan Society for the Promotion of Science (FY 2007-09, PI: Prof. Y. Nakamori) (3.1 M JPY)
    • PI: JAIST International Joint Research Grant FY 2008 (1M JPY).
    • PI: JAIST International Joint Research Grant FY 2006-07 (2.7M JPY).
    • PI: JAIST International Joint Research Grant FY 2004-05 (2.26M JPY).
    • JAIST Grant for Research Associates 2003-2006, 2008.

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