Bio


Alexander D. Kaiser is an applied mathematician who researches modeling and simulation of heart mechanics. His doctoral work focused on the mitral valve. He currently works in the Stanford Cardiovascular Biomechanics Computation Laboratory, led by Alison Marsden, on modeling cardiac disease.

Honors & Awards


  • Benchmark Capital Fellowship in Congenital Cardiovascular Bioengineering, The Wall Center, Stanford University (7/2020)
  • Mechanisms and Innovation in Cardiovascular Disease, T32 training fellowship, National Heart Lung and Blood Institute, National Institutes of Health via Stanford CVI (6/2018)
  • Kurt O. Friedrichs Prize for Outstanding Dissertation in Mathematics, Courant Institute of Mathematical Sciences, New York University (4/2018)
  • Thomas Tyler Bringley Fellowship, Courant Institute of Mathematical Sciences, New York University (4/2016)
  • Math Master’s Thesis Prize, Courant Institute of Mathematical Sciences, New York University (4/2014)
  • NSF Graduate Research Fellowship, National Science Foundation (4/2013)

Boards, Advisory Committees, Professional Organizations


  • Postdoctoral scholar, Institute for Computational & Mathematical Engineering, Stanford University (2017 - Present)
  • Postdoctoral scholar, Cardiovascular Institute, Stanford University (2018 - Present)

Professional Education


  • Doctor of Philosophy, New York University, Mathematics (2017)
  • Master of Science, New York University, Mathematics (2013)
  • Bachelor of Arts, University of California, Berkeley, Mathematics (2009)

Stanford Advisors


Lab Affiliations


  • Alison Marsden, Cardiovascular Biomechanics Computation Laboratory (11/1/2017)

All Publications


  • A design-based model of the aortic valve for fluid-structure interaction. Biomechanics and modeling in mechanobiology Kaiser, A. D., Shad, R., Hiesinger, W., Marsden, A. L. 2021

    Abstract

    This paper presents a new method for modeling the mechanics of the aortic valve and simulates its interaction with blood. As much as possible, the model construction is based on first principles, but such that the model is consistent with experimental observations. We require that tension in the leaflets must support a pressure, then derive a system of partial differential equations governing its mechanical equilibrium. The solution to these differential equations is referred to as the predicted loaded configuration; it includes the loaded leaflet geometry, fiber orientations and tensions needed to support the prescribed load. From this configuration, we derive a reference configuration and constitutive law. In fluid-structure interaction simulations with the immersed boundary method, the model seals reliably under physiological pressures and opens freely over multiple cardiac cycles. Further, model closure is robust to extreme hypo- and hypertensive pressures. Then, exploiting the unique features of this model construction, we conduct experiments on reference configurations, constitutive laws and gross morphology. These experiments suggest the following conclusions: (1) The loaded geometry, tensions and tangent moduli primarily determine model function. (2) Alterations to the reference configuration have little effect if the predicted loaded configuration is identical. (3) The leaflets must have sufficiently nonlinear material response to function over a variety of pressures. (4) Valve performance is highly sensitive to free edge length and leaflet height. These conclusions suggest appropriate gross morphology and material properties for the design of prosthetic aortic valves. In future studies, our aortic valve modeling framework can be used with patient-specific models of vascular or cardiac flow.

    View details for DOI 10.1007/s10237-021-01516-7

    View details for PubMedID 34549354

  • Modeling the mitral valve. International journal for numerical methods in biomedical engineering Kaiser, A. D., McQueen, D. M., Peskin, C. S. 2019: e3240

    Abstract

    This work is concerned with modeling and simulation of the mitral valve, one of the four valves in the human heart. The valve is composed of leaflets, the free edges of which are supported by a system of chordae, which themselves are anchored to the papillary muscles inside the left ventricle. First, we examine valve anatomy and present the results of original dissections. These display the gross anatomy and information on fiber structure of the mitral valve. Next, we build a model valve following a design-based methodology, meaning that we derive the model geometry and the forces that are needed to support a given load, and construct the model accordingly. We incorporate information from the dissections to specify the fiber topology of this model. We assume the valve achieves mechanical equilibrium while supporting a static pressure load. The solution to the resulting differential equations determines the pressurized configuration of the valve model. To complete the model we then specify a constitutive law based on a stress-strain relation consistent with experimental data that achieves the necessary forces computed in previous steps. Finally, using the immersed boundary method, we simulate the model valve in fluid in a computer test chamber. The model opens easily and closes without leak when driven by physiological pressures over multiple beats. Further, its closure is robust to driving pressures that lack atrial systole or are much lower or higher than normal.

    View details for DOI 10.1002/cnm.3240

    View details for PubMedID 31330567

  • Patient-Specific Computational Fluid Dynamics Reveal Localized Flow Patterns Predictive of Post-Left Ventricular Assist Device Aortic Incompetence. Circulation. Heart failure Shad, R., Kaiser, A. D., Kong, S., Fong, R., Quach, N., Bowles, C., Kasinpila, P., Shudo, Y., Teuteberg, J., Woo, Y. J., Marsden, A. L., Hiesinger, W. 2021: CIRCHEARTFAILURE120008034

    Abstract

    BACKGROUND: Progressive aortic valve disease has remained a persistent cause of concern in patients with left ventricular assist devices. Aortic incompetence (AI) is a known predictor of both mortality and readmissions in this patient population and remains a challenging clinical problem.METHODS: Ten left ventricular assist device patients with de novo aortic regurgitation and 19 control left ventricular assist device patients were identified. Three-dimensional models of patients' aortas were created from their computed tomography scans, following which large-scale patient-specific computational fluid dynamics simulations were performed with physiologically accurate boundary conditions using the SimVascular flow solver.RESULTS: The spatial distributions of time-averaged wall shear stress and oscillatory shear index show no significant differences in the aortic root in patients with and without AI (mean difference, 0.67 dyne/cm2 [95% CI, -0.51 to 1.85]; P=0.23). Oscillatory shear index was also not significantly different between both groups of patients (mean difference, 0.03 [95% CI, -0.07 to 0.019]; P=0.22). The localized wall shear stress on the leaflet tips was significantly higher in the AI group than the non-AI group (1.62 versus 1.35 dyne/cm2; mean difference [95% CI, 0.15-0.39]; P<0.001), whereas oscillatory shear index was not significantly different between both groups (95% CI, -0.009 to 0.001; P=0.17).CONCLUSIONS: Computational fluid dynamics serves a unique role in studying the hemodynamic features in left ventricular assist device patients where 4-dimensional magnetic resonance imaging remains unfeasible. Contrary to the widely accepted notions of highly disturbed flow, in this study, we demonstrate that the aortic root is a region of relatively stagnant flow. We further identified localized hemodynamic features in the aortic root that challenge our understanding of how AI develops in this patient population.

    View details for DOI 10.1161/CIRCHEARTFAILURE.120.008034

    View details for PubMedID 34139862

  • Use of patient-specific computational models for optimization of aortic insufficiency after implantation of left ventricular assist device. The Journal of thoracic and cardiovascular surgery Kasinpila, P. n., Kong, S. n., Fong, R. n., Shad, R. n., Kaiser, A. D., Marsden, A. L., Woo, Y. J., Hiesinger, W. n. 2020

    Abstract

    Aortic incompetence (AI) is observed to be accelerated in the continuous-flow left ventricular assist device (LVAD) population and is related to increased mortality. Using computational fluid dynamics (CFD), we investigated the hemodynamic conditions related to the orientation of the LVAD outflow in these patients.We identified 10 patients with new aortic regurgitation, and 20 who did not, after LVAD implantation between 2009 and 2018. Three-dimensional models of patients' aortas were created from their computed tomography scans. The geometry of the LVAD outflow graft in relation to the aorta was quantified using azimuth angles (AA), polar angles (PAs), and distance from aortic root. The models were used to run CFD simulations, which calculated the pressures and wall shear stress (rWSS) exerted on the aortic root.The AA and PA were found to be similar. However, for combinations of high values of AA and low values of PA, there were no patients with AI. The distance from aortic root to the outflow graft was also smaller in patients who developed AI (3.39 ± 0.7 vs 4.07 ± 0.77 cm, P = .04). There was no significant difference in aortic root pressures in the 2 groups. The rWSS was greater in AI patients (4.60 ± 5.70 vs 2.37 ± 1.20 dyne/cm2, P < .001). Qualitatively, we observed a trend of greater perturbations, regions of high rWSS, and flow eddies in the AI group.Using CFD simulations, we demonstrated that patients who developed de novo AI have greater rWSS at the aortic root, and their outflow grafts were placed closer to the aortic roots than those patients without de novo AI.

    View details for DOI 10.1016/j.jtcvs.2020.04.164

    View details for PubMedID 32653292

  • Gaussian-Like Immersed Boundary Kernels with Three Continuous Derivatives and Improved Translational Invariance Bao, Y., Kaiser, A. D., Kaye, J., Peskin, C. S. arXiv preprint. https://arxiv.org/abs/1505.07529v3. 2017
  • Automated simplification of large symbolic expressions JOURNAL OF SYMBOLIC COMPUTATION Bailey, D. H., Borwein, J. M., Kaiser, A. D. 2014; 60: 120–36
  • A Principled Kernel Testbed for Hardware/Software Co-Design Research USENIX Workshop on Hot Topics in Parallelism Kaiser, A. D., Williams, S., Madduri, K., Ibrahim, K., Bailey, D. H., Demmel, J. W., Strohmaier, E. 2010
  • A Kernel Testbed for Parallel Architecture, Language, and Performance Research Strohmaier, E., Williams, S., Kaiser, A., Madduri, K., Ibrahim, K., Bailey, D., Demmel, J. W., Simos, T., Psihoyios, G., Tsitouras, C. AMER INST PHYSICS. 2010: 1297–1300

    View details for DOI 10.1063/1.3497950

    View details for Web of Science ID 000289661500347

  • TORCH - Computational Reference Kernels: A Testbed for Computer Science Research Kaiser, A. D., Williams, S., Madduri, K., Ibrahim, K., Bailey, D. H., Demmel, J. W., Strohmaier, E. Tech Report LBNL-4172E. https://escholarship.org/uc/item/8n36z5tn. 2010
  • Undetected Errors in Quasi-cyclic LDPC Codes Caused by Receiver Symbol Slips Proceedings of IEEE Global Conference on Communications Kaiser, A. D., Dolinar, S., Cheng, M. K. 2009