Biohealth Network Science Lab
@ Biohealth Data Science, Yonsei University
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Welcome to the Biohealth Network Science Lab at Yonsei University, Seoul, Korea.
Our research aims to bridge probabilistic modeling, modern machine learning,
and emerging paradigms such as quantum computing for structured biomedical data.
Research Interests
• Multilevel and hierarchical models
• Tensor and graphical models for structured data
• Graphical, network, and hypergraph representations
• Single-cell and high-dimensional omics data
• Causal inference and representation learning
• Quantum computing and quantum-inspired methods for statistical learning
Selected Recent Publications ....
Joint Bayesian additive regression trees for multiple nonlinear dependency networks. (2025) L. Huang, CB Peterson, MJ Ha. Biometrics 81 (4).
Bayesian Multilayered Mediation Analysis for Cancer Pharmacogenomics. (2024) D Seo, V Baladandayuthapani, T Park, MJ Ha. Stat 13 (4).
Bayesian robust learning in chain graph models for integrative pharmacogenomics. (2024) M Chakraborty, V Baladandayuthapani, A Bhadra, MJ Ha. The Annals of Applied Statistics 18 (4) .
Estimating causal effects with hidden confounding using instrumental variables and environments. (2023) JP Long, H Zhou, KA Do, MJ HA. Electronic journal of statistics 17 (2).
A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes. (2023) L Huang, JP Long, E Irajizad, JD Doeke, KA Do, MJ Ha. Bioinformatics 39 (1)
A Bayesian precision medicine framework for calibrating individualized therapeutic indices in cancer. (2022) A Saha, MJ Ha, S Acharyya, V Baladandayuthapani. The Annals of Applied Statistics 16 (4).
Bayesian structure learning in multilayered genomic networks. (2021) MJ Ha, FC Stingo, V Baladandayuthapani. Journal of the American Statistical Association 116 (534)
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