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Jennifer Crodelle

Assistant Professor of Mathematics

 work(802) 443-3122
 Fall 2021: Mon & Wed 1-2:30pm in AXT 204, and by appointment.
 Warner Hall 315

I am an applied mathematician interested in using mathematical modeling to explore problems in neuroscience and propose plausible mechanisms underlying biological phenomena that may not be uncovered through experiment. 

Visit my website for more information.



Course List: 

Courses offered in the past four years.
indicates offered in the current term
indicates offered in the upcoming term[s]

MATH 0122 - Calculus II      

Calculus II
A continuation of MATH 0121, may be elected by first-year students who have had an introduction to analytic geometry and calculus in secondary school. Topics include a brief review of natural logarithm and exponential functions, calculus of the elementary transcendental functions, techniques of integration, improper integrals, applications of integrals including problems of finding volumes, infinite series and Taylor's theorem, polar coordinates, ordinary differential equations. (MATH 0121 or by waiver) 4 hrs. lect. DED

Fall 2021, Spring 2022

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MATH 0223 - Multivariable Calculus      

Multivariable Calculus
The calculus of functions of more than one variable. Introductory vector analysis, analytic geometry of three dimensions, partial differentiation, multiple integration, line integrals, elementary vector field theory, and applications. (MATH 0122 and MATH 0200 or by waiver) 3 hrs. lect./disc. DED

Fall 2020

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MATH 0225 - Topics in Linear Alg & Diff Eq      

Topics in Linear Algebra and Differential Equations
Topics may include diagonalization of matrices, quadratic forms, inner product spaces, canonical forms, the spectral theorem, positive matrices, the Cayley-Hamilton theorem, ordinary differential equations of arbitrary order, systems of first-order differential equations, power series, and eigenvalue methods of solution, applications. (MATH 0200 or by waiver) 3 hrs. lect./disc. DED

Fall 2020, Spring 2021

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MATH 0226 - Differential Equations      

MATH 0226, Differential Equations
This course provides an introduction into ordinary differential equations (ODEs) with an emphasis on linear and nonlinear systems using analytical, qualitative, and numerical techniques. Topics will include separation of variables, integrating factors, eigenvalue method, linearization, bifurcation theory, and numerous applications. In this course, we will introduce MATLAB programming skills and develop them through the semester. (MATH 0200 or by waiver) (formerly MATH 0225) 3 hrs. lect./disc. DED

Fall 2021

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MATH 0315 - Mathematical Modeling      

Mathematical modeling
An introduction into the process of developing and interpreting mathematical models within the framework of numerous applications. In this course, we will utilize discrete, continuous, and probabilistic approaches to explore applications such as population dynamics, epidemiology, and neuron activity. Time permitting, we may also introduce the derivation of spatiotemporal models. MATLAB will be used to implement and analyze several of these models. (MATH 0200 and MATH 0225 or MATH 0226, or by instructor approval) 3 hrs. lect./disc DED

Spring 2021

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MATH 0326 - Partial Differential Equations      

Partial Differential Equations
An introduction to partial differential equations (PDEs) with an emphasis on first and second-order linear equations. Using analytical, qualitative, and numerical techniques, we will study the Laplace, heat, and wave equations, as well as their applications. MATLAB will be used where applicable. (MATH 0223 and either of MATH 0225 or MATH 0226) 3 hr lect. DED

Spring 2022

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MATH 0500 - Advanced Study      

Advanced Study
Individual study for qualified students in more advanced topics in algebra, number theory, real or complex analysis, topology. Particularly suited for those who enter with advanced standing. (Approval required) 3 hrs. lect./disc.

Winter 2022

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NSCI 1230 / MATH 1230 - DataScience Across Disciplines      

Data Science Across Disciplines
In this course, we will gain exposure to the entire data science pipeline—obtaining and cleaning large and messy data sets, exploring these data and creating engaging visualizations, and communicating insights from the data in a meaningful manner. During morning sessions, we will learn the tools and techniques required to explore new and exciting data sets. During afternoon sessions, students will work in small groups with one of several faculty members on domain-specific research projects in Sociology, Neuroscience, Animation, Art History, or Environmental Science. This course will utilize the R programming language. No prior experience with R is necessary.
ENVS: Students will engage in research within environmental health science—the study of reciprocal relationships between human health and the environment. High-quality data and the skills to make sense of these data are key to studying environmental health across diverse spatial scales, from individual cells through human populations. In this course, we will explore common types of data and analytical tools used to answer environmental health questions and inform policy.
FMMC: Students will explore how to make a series of consequential decisions about how to present data and how to make it clear, impactful, emotional or compelling. In this hands-on course we will use a wide range of new and old art making materials to craft artistic visual representations of data that educate, entertain, and persuade an audience with the fundamentals of data science as our starting point.
NSCI/MATH: Students will use the tools of data science to explore quantitative approaches to understanding and visualizing neural data. The types of neural data that we will study consists of electrical activity (voltage and/or spike trains) measured from individual neurons and can be used to understand how neurons respond to and process different stimuli (e.g., visual or auditory cues). Specifically, we will use this neural data from several regions of the brain to make predictions about neuron connectivity and information flow within and across brain regions.
SOCI: Students will use the tools of data science to examine how experiences in college are associated with social and economic mobility after college. Participants will combine sources of "big data" with survey research to produce visualizations and exploratory analyses that consider the importance of higher education for shaping life chances.
HARC: Students will use the tools of data science to create interactive visualizations of the Dutch textile trade in the early eighteenth century. These visualizations will enable users to make connections between global trade patterns and representations of textiles in paintings, prints, and drawings. DED SCI WTR

Winter 2022

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Program in Neuroscience

McCardell Bicentennial Hall
276 Bicentennial Way
Middlebury College
Middlebury, VT 05753