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Bogdan Epureanu

Bio

Education

Affiliations

Preferred Pronouns

Personal Academic Website

http://www-personal.umich.edu/~epureanu/index.html

Topics of Interest/Expertise
 

Countries of Work/Collaboration

Projects

Papers

Ghadami A, Shim M, Ghadami A, Drake JM, Epureanu BI. (2026). Explaining complex dynamical systems using conditional SHAP analysis with application to multi-variant epidemic dynamics. Scientific reports

Drake JM, Bahl J, Cacciatore MA, Ghadami A, Graeden E, Han BA, Nowak G, Marty É, Robertson H, Rohani P, Sarkar S, Stott GL, Winter AK, Epureanu BI. Leveraging Systems-of-Systems Analysis to Strengthen Epidemic Intelligence for Preparedness and Response. Health security, 23(6)

García Pérez J, Epureanu B. (2024). Enhancing strategic decision-making in differential games through bifurcation prediction. Scientific reports, 14(1)

Kanda E, Epureanu BI, Adachi T, Sasaki T, Kashihara N. (2024). New marker for chronic kidney disease progression and mortality in medical-word virtual space. Scientific reports, 14(1)

Kanda E, Epureanu BI, Adachi T, Sasaki T, Kashihara N. (2024). Mathematical expansion and clinical application of chronic kidney disease stage as vector field. PloS one, 19(3)

Amin Ghadami, Eamon B. O’Dea, John M. Drake, Pejman Rohani, Bogdan I. Epureanu. (2023). Anticipating epidemic transitions in metapopulations with multivariate spectral similarity. Nonlinear Dynamics, (111)

Kanda E, Epureanu BI, Adachi T, Kashihara N. (2022). Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality. PLOS digital health, 2(1)

Ghadami A, Epureanu BI. (2022). Deep learning for centre manifold reduction and stability analysis in nonlinear systems. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 380(2229)

Ghadami A, Epureanu BI. (2022). Data-driven prediction in dynamical systems: recent developments. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 380(2229)

Chen S, Ghadami A, Epureanu BI. (2022). Practical guide to using Kendall's τ in the context of forecasting critical transitions. Royal Society open science, 9(7)

Li X, Ghadami A, Drake JM, Rohani P, Epureanu BI. (2021). Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics. Scientific reports, 11(1)

Chen K, Nam W, Epureanu BI. (2020). Collective intracellular cargo transport by multiple kinesins on multiple microtubules. Physical review. E, 101(5-1)

Kanda E, Epureanu BI, Adachi T, Tsuruta Y, Kikuchi K, Kashihara N, Abe M, Masakane I, Nitta K. (2020). Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan. PloS one, 15(5)

Ghadami A, Chen S, Epureanu BI. (2020). Data-driven identification of reliable sensor species to predict regime shifts in ecological networks. Royal Society open science, 7(8)

Saxena H, Ward KR, Krishnan C, Epureanu BI. (2020). Effect of Multi-Frequency Whole-Body Vibration on Muscle Activation, Metabolic Cost and Regional Tissue Oxygenation. IEEE access : practical innovations, open solutions, (8)

Drake JM, Brett TS, Chen S, Epureanu BI, Ferrari MJ, Marty É, Miller PB, O'Dea EB, O'Regan SM, Park AW, Rohani P. (2019). The statistics of epidemic transitions. PLoS computational biology, 15(5)

Chen S, O'Dea EB, Drake JM, Epureanu BI. (2019). Eigenvalues of the covariance matrix as early warning signals for critical transitions in ecological systems. Scientific reports, 9(1)

Mirzakhalili E, Epureanu BI, Gourgou E. (2018). A mathematical and computational model of the calcium dynamics in Caenorhabditis elegans ASH sensory neuron. PloS one, 13(7)

Ghadami A, Gourgou E, Epureanu BI. (2018). Rate of recovery from perturbations as a means to forecast future stability of living systems. Scientific reports, 8(1)

Mirzakhalili E, Gourgou E, Booth V, Epureanu B. (2017). Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies. Frontiers in neural circuits, (11)

Nam W, Epureanu BI. (2016). Dynamic model for kinesin-mediated long-range transport and its local traffic jam caused by tau proteins. Physical review. E, 95(1-1)

Nam W, Epureanu BI. (2016). Effects of Obstacles on the Dynamics of Kinesins, Including Velocity and Run Length, Predicted by a Model of Two Dimensional Motion. PLoS One, 11(1)

Chen S, Epureanu B. (2017). Regular biennial cycles in epidemics caused by parametric resonance. Journal of theoretical biology, (415)

Nam W, Epureanu BI. (2015). Highly loaded behavior of kinesins increases the robustness of transport under high resisting loads. PLoS computational biology, 11(3)

D'Souza K, Epureanu BI, Pascual M. (2015). Forecasting Bifurcations from Large Perturbation Recoveries in Feedback Ecosystems. PloS one, 10(9)

Nam W, Epureanu BI, Epurenau BI. (2012). Metrics for characterizing collective transport by multiple dimeric kinesins. Physical review. E, Statistical, nonlinear, and soft matter physics, 86(5 Pt 1)

Nam W, Epureanu BI. (2012). The effects of viscoelastic fluid on kinesin transport. Journal of physics. Condensed matter : an Institute of Physics journal, 24(37)

Krishnan A, Epureanu BI. (2011). Renewal-reward process formulation of motor protein dynamics. Bulletin of mathematical biology, 73(10)

Lim J, Epureanu BI. (2010). Forecasting a class of bifurcations: theory and experiment. Physical review. E, Statistical, nonlinear, and soft matter physics, 83(1 Pt 2)

Yin SH, Epureanu BI. (2006). Structural health monitoring based on sensitivity vector fields and attractor morphing. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 364(1846)

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