Main Research of P.I.
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I was trained in Bayesian inference network modeling under the Evidence-Centered Design framework with Robert J. Mislevy and Russell G. Almond, and my doctoral work developed dynamic Bayesian network models for longitudinal psychological inference that integrate latent psychological measurement, causal inference, and intervention modeling.
Building on this foundation, my current work integrates psychometric latent state modeling with causal and decision-theoretic Bayesian networks to move beyond static prediction toward interpretative, intervention-relevant systems.
Early work: dynamic Bayesian inference for latent psychological states
Field shift: causality + decision science
Current work: unifying measurement + dynamics + intervention modeling
Current Work
Younyoung Choi (2011): Dynamic Bayesian inference networks and hidden Markov models for modeling learning progressions over multiple time points — supervised under Prof. Mislevy (University of Maryland).
Choi, Y. & Cho, Y. I. (2020): Learning analytics using social network analysis and Bayesian network analysis in sustainable computer-based formative assessment system. Sustainability
Choi, Younyoung & McClenen, C. (2020): Development of adaptive formative assessment system using CAT and dynamic Bayesian networks. Applied Sciences — CAFT 시스템 개발
Choi Y., & Mislevy R. J. (2022): Evidence-Centered Design framework and dynamic Bayesian network for modeling learning progression in online assessment systems. Frontiers in Psychology — dynamic BN + ECD 연결 연구.
Gyeongcheol Cho, Younyoung Choi, Sooyeon Suh (In press). Bayesian Networks with Interpretable Summary Indexes for Modeling Clinicians’ Decision-Making in Treatment Recommendation for Mental Disorders, Multivariate Behavioral Research
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