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Professor Cherry’s research involves modeling and simulation, high-performance computing, numerical methods, and machine learning. Her work is particularly focused on computational modeling of cardiac arrhythmias, including model development, validation, and parameter estimation; design and implementation of efficient solution methods; implementations on traditional parallel and GPGPU architectures; integration with experiments through data assimilation and machine-learning-based temporal and spatiotemporal prediction; and applications to predict, understand, and control complex dynamical states.
Professor Cherry is interested in teaching computing courses at the undergraduate and graduate levels, including classes in computational problem-solving, graduate studies in computing, and special-topics courses in emerging areas of mathematical modeling and scientific computing. Her teaching focuses on student success.
1. Cairns DI, Cherry EM. Efficient generation of populations of cardiac models. Computing in Cardiology 2025; in press.
2. Chiu C†, Molesworth G†, Toye M, Cherry EM, Fenton FH. VizCOM: A novel tool for advanced visualization and analysis of cardiac optical mapping data. Computing in Cardiology 2025; in press. †indicates co-first authors
3. Comstock MR, Fenton FH, Cherry EM. Fast parameterization of human ventricular ionic models using CardioFit. Computing in Cardiology 2025; in press.
4. Seshadri RL, Comstock MR, Cherry EM. Reproducing cardiac ionic model properties using a discrete-time model. Computing in Cardiology 2025; in press.
5. Welch CS, Cherry EM. Discovering cardiac action potential model equations using sparse identification of nonlinear dynamics. Computing in Cardiology 2025; in press.
6. Delshad A, Cherry EM. Predicting complex time series with deep echo state networks. Chaos 2025; 35: 093126. doi.org/10.1063/5.0283425
7. Cairns DI†, Comstock MR†, Fenton FH, Cherry EM. CardioFit: a WebGL-based tool for fast and efficient parametrization of cardiac action potential models to fit user-provided data. Royal Society Open Science 2025; 12: 250048. †indicates co-first authors doi.org/10.1025004898/rsos.
8. Weinberg SH, Mendez MJ, Cherry EM, Hoeker G, Poelzing S. Reconstructing ventricular cardiomyocyte dynamics and parameter estimation using data-assimilation. Biophysical Journal 2024; 123: 4050-4066. doi.org/10.1016/j.bpj.2024.10.018
9. Kaboudian A, Gray RA, Uzelac I, Cherry EM, Fenton FH. Fast interactive simulations of cardiac electrical activity in anatomically accurate heart structures by compressing sparse and uniform Cartesian grids. Computer Methods and Programs in Biomedicine 2024; 257: 108456. doi.org/10.1016/j.cmpb.2024.108456
10. Iravanian S, Uzelac I, Shah AD, Toye MJ, Lloyd MS, Burke MA, Daneshmand MA, Attia TS, Vega JD, El-Chami MF, Merchant FM, Cherry EM, Bhatia NK, Fenton FH. Complex repolarization dynamics in ex vivo human ventricles are independent of the restitution properties. Europace 2023; 25: euad350.
11. Badr S, Fenton FH, Cherry EM. Reconstructing cardiac voltage using data assimilation: Effects of observation distribution. Computing in Cardiology 2023; 50: 10364001.
12. Cairns DI, Comstock MR, Fenton FH, Cherry EM. Automated customization of cardiac electrophysiology models to facilitate patient-specific modeling. Computing in Cardiology 2023; 50: 10364104.
13. Comstock MR, Cherry EM. Speeding up cardiac simulations with parallel-in-time solvers. Computing in Cardiology 2023; 50: 10363831
14. Iravanian S, Toye MJ, Uzelac I, Bhatia NK, Cherry EM, Fenton FH. A combinatorial algorithm to detect higher-order dynamics in cardiac signals. Computing in Cardiology 2023; 50: 10363933.
15. Kaboudian A, Cherry EM, Fenton FH. GPU load balancing using sparse Cartesian grids: Making interactive WebGL simulations of complex ionic models even faster on 3D heart structures. Computing in Cardiology 2023; 50: 10363894.
16. Munoz LM, Marks AE, Santiago-Reyes JA, Ampofo MO, Cherry EM. Observability analysis of data reconstruction strategies for a cardiac ionic model. Computing in Cardiology 2023; 50: 10363874.
17. Rheaume E, Velasco-Perez H, Cairns D, Comstock M, Rheaume E, Uzelac I, Cherry EM, Fenton FH. A modified FitzHugh-Nagumo model that reproduces the action potential and dynamics of the Ten Tusscher et al. cardiac model in tissue. Computing in Cardiology 2023; 50: 10364094.
18. Marcotte CD, Hoffman MJ, Fenton FH, Cherry EM. Reconstructing cardiac electrical excitations from optical mapping recordings. Chaos 2023; 33; 093141. doi.org/10.1063/5.0156314
19. Berg LA, Martins Rocha B, Sachetto Oliveira R, Sebastian R, Rodriguez B, Alves Bonfim de Queiroz R, Cherry EM, Weber dos Santos R. Enhanced optimization-based method for the generation of patient-specific models of Purkinje networks. Scientific Reports 2023; 13: 11788. doi.org/10.1038/s41598-023-38653-1
20. Nieto Ramos A, Fenton FH, Cherry EM. Bayesian inference for fitting cardiac models to experiments: Estimating parameter distributions using Hamiltonian Monte Carlo and Approximate Bayesian Computation. Medical & Biological Engineering & Computing 2023; 61: 75-95. doi.org/10.1007/s11517-022-02685-y
21. He J, Pertsov AM, Cherry EM, Fenton FH, Roney C, Niederer S, Zhang Z, Mangharam R. Fiber organization has little effect on electrical activation patterns during focal arrhythmias in the left atrium. IEEE Transactions on Biomedical Engineering 2023; 70: 1611-1621 (selected as featured article). doi.org/10.1109/TBME.2022.3223063