Laura Munoz Headshot

Laura Munoz

Associate Professor

School of Mathematics and Statistics
College of Science

585-475-2523
Office Hours
Fall 2023: Mon 11:00am-12:00pm, Wed Fri 2:00-3:00pm, and Fri 3:00-4:00pm. Location: GOS 3340. I may need to make one-time changes to office hours during weeks where scheduling conflicts arise. Email me for the latest info or if you want to set up an appointment for a specific time.
Office Location

Laura Munoz

Associate Professor

School of Mathematics and Statistics
College of Science

Education

BS, California Institute of Technology; Ph.D., University of California at Berkeley

585-475-2523

Areas of Expertise

Select Scholarship

Invited Keynote/Presentation
Munoz, Laura. "Controllability of a Nonlinear Dynamical Model of a Cardiac Myocyte." Dynamics Days. Georgia Institute of Technology. Atlanta, GA. 8 Jan. 2022. Conference Presentation.
Munoz, Laura. "Empirical Gramian Based Controllability of Alternans in a Cardiac Map Model." Computing in Cardiology. Computing in Cardiology. Brno, Czech Republic. 14 Sep. 2021. Conference Presentation.
Munoz, Laura. "State Estimation for Cardiac Action Potential Dynamics: A Comparison of Linear and Nonlinear Kalman Filters." Computing in Cardiology. Computing in Cardiology. Rimini, Italy. 15 Sep. 2020. Conference Presentation.
Munoz, Laura. "Observability Analysis and Estimator Design for a Cardiac Cell Model." Dynamics Days. Dynamics Days. Denver, CO. 6 Jan. 2018. Conference Presentation.
Munoz, Laura. "Control, Estimation, and Modeling of Cardiac Action Potential Dynamics." Kavli Institute for Theoretical Physics: Integrative Cardiac Dynamics Program. University of California, Santa Barbara. Santa Barbara, CA. 2 Jul. 2018. Conference Presentation.
Munoz, Laura. "Controllability Analysis of a Cardiac Cell Model." Society for Industrial and Applied Mathematics (SIAM) Conference on the Life Sciences. SIAM. Minneapolis, MN. 8 Aug. 2018. Conference Presentation.
Munoz, Laura. "Modeling and Estimation of Dynamical Variables in a Cardiac Cell Model." Society for Industrial and Applied Mathematics (SIAM) Conference on Dynamical Systems. SIAM. Snowbird, UT. 25 May 2017. Conference Presentation.
Munoz, Laura. "Applications of a Mathematical Model of Cardiac Action Potential Dynamics." Mathematical Association of America Seaway Section Spring Meeting. Mathematical Association of America (MAA). Geneseo, NY. 16 Apr. 2016. Conference Presentation.
Munoz, Laura. "Estimation of Dynamical Variables in a Cardiac Myocyte Model." Society for Industrial and Applied Mathematics Conference on the Life Sciences. Society for Industrial and Applied Mathematics (SIAM). Boston, MA. 12 Jul. 2016. Conference Presentation.
Munoz, Laura. "Data Reconstruction Algorithms for Cardiac Action Potential Dynamics." RIT Grant Writers’ Boot Camp Poster Session. RIT. Rochester, NY. 17 Nov. 2015. Conference Presentation.
Munoz, Laura M. "Predicting Arrhythmias with a Nonlinear Cardiac Fiber Model." Society for Industrial and Applied Mathematics (SIAM) Conference on the Life Sciences. SIAM. Charlotte, NC. 6 Aug. 2014. Conference Presentation.
Munoz, Laura M. "Forecasting Cardiac Arrhythmias with a One-Dimensional Fiber Model." Dynamics Days. Georgia Institute of Technology. Atlanta, GA. 1 Jan. 2014. Conference Presentation.
Munoz, Laura M. "Predicting Cardiac Arrhythmias with a One-dimensional Fiber Model." Upstate New York Cardiac Electrophysiology Society Annual Meeting. University at Buffalo - SUNY. Buffalo, NY. 3 Nov. 2014. Conference Presentation.
Munoz, Laura M. "Cardiac Arrhythmia Prediction Using a 1D Dynamical Model." Society for Industrial and Applied Mathematics (SIAM) Conference on Applications of Dynamical Systems. SIAM. Snowbird, UT. 21 May 2013. Conference Presentation.
Munoz, Laura M. "Kalman Filter Based Estimation of Ionic Concentrations and Gating Variables in a Cardiac Myocyte Model." Computing in Cardiology Conference. Computing in Cardiology. Zaragoza, ES. 23 Sep. 2013. Conference Presentation.
Munoz, Laura M. "Predicting Cardiac Arrhythmias with a 1D Nonlinear Dynamical Model." 4th New York Conference on Applied Mathematics (NYCAM). Cornell University. Ithaca, NY. 9 Nov. 2013. Conference Presentation.
Journal Paper
Munoz, Laura, Mark Ampofo, and Elizabeth Cherry. "Controllability of Voltage- and Calcium-driven Cardiac Alternans in a Map Model." Chaos 31. (2021): 23139. Print.
Vogt, Ryan, et al. "Controllability Analysis of a Cardiac Ionic Cell Model." Computers in Biology and Medicine 139. (2021): 104909. Print.
Guzman, Anthony, et al. "Observability Analysis and State Observer Design for a Cardiac Ionic Cell Model." Computers in Biology and Medicine 125. (2020): 103910. Web.
Munoz, Laura, et al. "Discordant Alternans Mechanism for Initiation of Ventricular Fibrillation In Vitro." Journal of the American Heart Association 7. (2018): e007898. Web.
Published Conference Proceedings
Munoz, Laura, Mark Ampofo, and Elizabeth Cherry. "Empirical Gramian Based Controllability of Alternans in a Cardiac Map Model." Proceedings of the Computing in Cardiology Conference, September 2021. Ed. Christine Pickett. Brno, Czech Republic: n.p., 2021. Web.
Munoz, Laura and Christopher Beam. "State Estimation for Cardiac Action Potential Dynamics: A Comparison of Linear and Nonlinear Kalman Filters." Proceedings of the Computing in Cardiology Conference, September 2020. Ed. Christine Pickett. Rimini, Italy: n.p., 2020. Web.
Munoz, Laura M. and Niels F. Otani. "Kalman Filter Based Estimation of Ionic Concentrations and Gating Variables in a Cardiac Myocyte Model." Proceedings of the Computing in Cardiology Conference, Zaragoza, Spain, September 22-25, 2013. Ed. Alan Murray. Zaragoza, ES: Computing in Cardiology, Print.

Currently Teaching

MATH-231
3 Credits
This course is an introduction to the study of ordinary differential equations and their applications. Topics include solutions to first order equations and linear second order equations, method of undetermined coefficients, variation of parameters, linear independence and the Wronskian, vibrating systems, and Laplace transforms.
MATH-241
3 Credits
This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course.
MATH-326
3 Credits
This course provides an introduction to boundary value problems. Topics include Fourier series, separation of variables, Laplace's equation, the heat equation, and the wave equation in Cartesian and polar coordinate systems.
MATH-381
3 Credits
This course covers the algebra of complex numbers, analytic functions, Cauchy-Riemann equations, complex integration, Cauchy's integral theorem and integral formulas, Taylor and Laurent series, residues, and the calculation of real-valued integrals by complex-variable methods.
MATH-495
1 - 3 Credits
This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.
MATH-498
1 - 3 Credits
This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.
MATH-622
3 Credits
This course will introduce graduate students to the logical methodology of mathematical modeling. They will learn how to use an application field problem as a standard for defining equations that can be used to solve that problem, how to establish a nested hierarchy of models for an application field problem in order to clarify the problem’s context and facilitate its solution. Students will also learn how mathematical theory, closed-form solutions for special cases, and computational methods should be integrated into the modeling process in order to provide insight into application fields and solutions to particular problems. Students will study principles of model verification and validation, parameter identification and parameter sensitivity and their roles in mathematical modeling. In addition, students will be introduced to particular mathematical models of various types: stochastic models, PDE models, dynamical system models, graph-theoretic models, algebraic models, and perhaps other types of models. They will use these models to exemplify the broad principles and methods that they will learn in this course, and they will use these models to build up a stock of models that they can call upon as examples of good modeling practice.
MATH-731
3 Credits
This course covers an analysis of iterations of maps, symbolic dynamics, their uses, and fractals. It includes methods for simplifying dynamical systems (center manifolds and normal forms), Melnikov's method, and applications.