Publications

Martin, A., Nezdyur, M., Tanade, C., & Randles, A. (2025). Establishing a massively parallel computational model of the adaptive immune response (Accepted). Journal of Computational Science, 87. https://doi.org/10.1016/j.jocs.2025.102555

Tanade, C., & Randles, A. (2025). Real-time virtual intervention for simple and serial coronary artery disease using the HarVI framework (Accepted). Journal of Computational Science, 87. https://doi.org/10.1016/j.jocs.2025.102570

Geddes, J. R., King, T. D., Tanade, C., Ladd, W., Khan, N. S., & Randles, A. (2025). Impact of inlet velocity waveform shape on hemodynamics (Accepted). Journal of Computational Science, 87. https://doi.org/10.1016/j.jocs.2025.102579

Deelman, E., Dongarra, J., Hendrickson, B., Randles, A., Reed, D., Seidel, E., & Yelick, K. (2025). High-performance computing at a crossroads. Science (New York, N.Y.), 387(6736), 829–831. https://doi.org/10.1126/science.adu0801

Feiger, B., Jensen, C. W., Bryner, B. S., Segars, W. P., & Randles, A. (2024). Modeling the effect of patient size on cerebral perfusion during veno-arterial extracorporeal membrane oxygenation. Perfusion, 39(7), 1295–1303. https://doi.org/10.1177/02676591231187962

Tanade, C., Khan, N. S., Rakestraw, E., Ladd, W. D., Draeger, E. W., & Randles, A. (2024). Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins. NPJ Digital Medicine, 7(1), 236. https://doi.org/10.1038/s41746-024-01216-3

Chidyagwai, S. G., Kaplan, M. S., Jensen, C. W., Chen, J. S., Chamberlain, R. C., Hill, K. D., … Randles, A. (2024). Surgical Modulation of Pulmonary Artery Shear Stress: A Patient-Specific CFD Analysis of the Norwood Procedure. Cardiovasc Eng Technol, 15(4), 431–442. https://doi.org/10.1007/s13239-024-00724-3

Geddes, J., Randles, A., Tanade, C., Ladd, W., & Khan, N. S. (2024). Velocity Temporal Shape Affects Simulated Flow in Left Coronary Arteries (Accepted). Presented at the 24th International Conference on Computational Science, Malaga, Spain.

Vardhan, M., Tanade, C., Chen, S. J., Mahmood, O., Chakravartti, J., Jones, W. S., … Randles, A. (2024). Diagnostic Performance of Coronary Angiography Derived Computational Fractional Flow Reserve. J Am Heart Assoc, 13(13), e029941. https://doi.org/10.1161/JAHA.123.029941

Mavi, J. K., Tanade, C., Ladd, W., Geddes, J., Khan, N. S., & Randles, A. (2024). Hemodynamics comparison of an hour-long rest and activity state data in a human coronary digital twin. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (Vol. 2024, pp. 1–4). https://doi.org/10.1109/embc53108.2024.10782436

Huber, M., Jiang, J., Tanade, C., & Randles, A. (2024). Identifying When Steady-State Flow Simulations In Patient-Specific Coronaries Recapitulate Pulsatile Flow Dynamics. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (Vol. 2024, pp. 1–4). https://doi.org/10.1109/embc53108.2024.10781714

Ghorbannia, A., & Randles, A. (2024). Systematic characterization and automated alignment of coronary tree geometries. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (Vol. 2024, pp. 1–4). https://doi.org/10.1109/embc53108.2024.10781665

Vemulapalli, S., Adebiyi, A., Jensen, C., Weissler, E. H., Inohara, T., Chen, S. J., … Randles, A. (2024). Leveraging Computational Fluid Dynamics for Next-Generation Preoperative Planning in Vascular Surgery. In Annu Int Conf IEEE Eng Med Biol Soc (Vol. 2024, pp. 1–4). United States. https://doi.org/10.1109/EMBC53108.2024.10781932

Wu, R., Kabir, M. S., Truskey, G. A., & Randles, A. (2024). Investigating the impact of sickle cell disease on red blood cell transport in complex capillary networks. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference (Vol. 2024, pp. 1–4). https://doi.org/10.1109/embc53108.2024.10781578

Tanade, C., & Randles, A. (n.d.). HarVI: Real-time intervention planning for coronary artery disease using machine learning (Accepted). Presented at the International Conference on Computational Science (ICCS), Malaga, Spain.

Seidel, E., Randles, A., Arthur, R., Bergman, K., Carlson, B., Deelman, E., … Reed, D. (2024). 2024 Advanced Scientific Computing Advisory Committee (ASCR) Facilities Subcommittee Recommendations. USDOE Office of Science (SC). https://doi.org/10.2172/2370379

Geddes, J. R., & Randles, A. (2024). Optimizing Temporal Waveform Analysis: A Novel Pipeline for Efficient Characterization of Left Coronary Artery Velocity Profiles. ArXiv.

Martin, A., Liu, G., Joo, B., Wu, R., Kabir, M. S., Draeger, E. W., & Randles, A. (2024). Designing a GPU-Accelerated Communication Layer for Efficient Fluid-Structure Interaction Computations on Heterogeneous Systems. In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. https://doi.org/10.1109/SC41406.2024.00099

Nan, J., Roychowdhury, S., & Randles, A. (2023). Investigating the Influence of Heterogeneity Within Cell Types on Microvessel Network Transport. Cellular and Molecular Bioengineering, 16(5–6), 497–507. https://doi.org/10.1007/s12195-023-00790-y

Valero-Lara, P., Vetter, J., Gounley, J., & Randles, A. (2023). Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs. In ACM International Conference Proceeding Series (pp. 1697–1704). https://doi.org/10.1145/3624062.3624250

Yousef, A., & Randles, A. (2023). Enabling In Situ Visualization of Large-Scale Cellular Simulations. Presented at the ISAV23: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, Denver, CO.

Martin, A., Liu, G., Ladd, W., Lee, S., Gounley, J., Vetter, J., … Randles, A. (2023). Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations. In ACM International Conference Proceeding Series (pp. 1126–1137). https://doi.org/10.1145/3624062.3624188

Tanade, C., Rakestraw, E., Ladd, W., Draeger, E., & Randles, A. (2023). Cloud Computing to Enable Wearable-Driven Longitudinal Hemodynamic Maps. In International Conference for High Performance Computing, Networking, Storage and Analysis : [proceedings]. SC (Conference : Supercomputing) (Vol. 2023, p. 82). https://doi.org/10.1145/3581784.3607101

Roychowdhury, S., Balogh, P., Mahmud, S. T., Puleri, D. F., Martin, A., Gounley, J., … Randles, A. (2023). Enhancing Adaptive Physics Refinement Simulations Through the Addition of Realistic Red Blood Cell Counts. In International Conference for High Performance Computing, Networking, Storage and Analysis : [proceedings]. SC (Conference : Supercomputing) (Vol. 2023, p. 41). https://doi.org/10.1145/3581784.3607105

Randles, A., Draeger, E., & Yousef, A. (2023). Low-Cost Post Hoc Reconstruction of HPC Simulations at Full Resolution. 2023 IEEE 13th Symposium on Large Data Analysis and Visualization (LDAV), 17–21. https://doi.org/10.1109/LDAV60332.2023.00009