**About**

Currently, I am an Assistant Professor of Mathematics in the Natural Science Division of Seaver College at Pepperdine University, though broadly, I would describe myself as an educator and scholar. Teaching mathematics is my passion and I am continually striving to become a more effective teacher by applying new tools and skills to motivate students both inside and outside of the classroom. My research focus lies in network theory and network analysis where, specifically, I work on the development of computational and statistical techniques to model, analyze, and explore complex, relational data. To learn more about my **professional experience**, please hover over the nodes on the adjacent network!

**Contact**

**Email**

christina.duron@pepperdine.edu

**Office Telephone**

(310) 506 - 4832

**Office**

RAC 105

**Office Hours (Fall 2024)**

Mondays and Fridays: 11:30AM - 12:30PM

Tuesdays and Thursdays: 10:30AM - 11:30AM

Or by appointment

*(All times are in PST)*

**Recent Publications**

Kravitz H,

**Durón C.**, Brio M. (2024).*A Coupled Spatial-Network Model: A Mathematical Framework for Applications in Epidemiology*. Bulletin of Mathematical Biology, 86(132): 1 - 35.**Durón C.**, Swansen B, Kravitz H. (2024).*Difference Approximation*. In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels).Fider N,

**Durón C.**, Pfeffer D. (2023).*From Mirrors to Wallpapers: A Virtual Math Circle Module on Symmetry*. Journal of Math Circles: Vol 3: Iss. 1, Article 1.

**Dissertation:** **Durón C**. *The Distribution of Betweenness Centrality in Exponential Random Graph Models*. ProQuest on May 18, 2019.

Alternatively, my publications can be found on my Google Scholar profile.

**Current Course Information**

**Announcements**

Nothing yet.

**Course Overview**

Math 150 provides an introduction to first-semester calculus, from rates of change to integration, with an emphasis on understanding, problem solving, and modeling. Topics covered include key concepts of the derivative and definite integral, techniques of differentiation, and applications.

Syllabus

**Note**: The posted files (assignments and lecture notes) are only accessible to those within Pepperdine University.

**Assignments**

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Week 11

Week 12

Week 13

Week 14

Week 15

Week 16

Week 17

**Lecture Slides**

**Announcements**

Nothing yet.

**Course Overview**

Math 260 provides an introduction to Linear Algebra, covering much of Chapters 1 – 7. Topics covered include systems of linear equations and linear transformations; matrix determinant, inverse, rank, eigenvalues, eigenvectors, factorizations, diagonalization, singular value decomposition; linear independence, vector spaces and subspaces, bases, dimensions; inner products and norms, orthogonal projection, Gram-Schmidt process, least squares; applications.

Syllabus

**Note**: The posted files (assignments and lecture notes) are only accessible to those within Pepperdine University.

**Assignments**

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Week 11

Week 12

Week 13

Week 14

Week 15

Week 16

Week 17

**Lecture Slides**

**Past Courses**

Spring 2024 – Pepperdine University

**Course Overview**

Math 150 provides an introduction to first-semester calculus, from
rates of change to integration, with an emphasis on understanding,
problem solving, and modeling. Topics covered include key concepts
of the derivative and definite integral, techniques of
differentiation, and applications.

Syllabus

**Course Overview**

Math 450 provides an introduction to the basic mathematical properties
of statistical methods for data analyses including parametric estimation,
hypothesis testing, linear least square estimation, analysis of variance
and analysis of categorical data. Additional topics include include sampling,
standard error, methods or finding estimates (such as method of moments
and maximum likelihood) and analyzing their accuracy through analysis
bias, standard errors and confidence intervals, use of normal, t,
chi-square, and F distributions, large sampling methods, correlation,
along with common errors and problems in statistical reasoning and
experimental design.

Syllabus

Fall 2023 – Pepperdine University

**Course Overview**

Math 150 provides an introduction to first-semester calculus, from
rates of change to integration, with an emphasis on understanding,
problem solving, and modeling. Topics covered include key concepts
of the derivative and definite integral, techniques of differentiation,
and applications.

Syllabus

**Course Overview**

Math 350 provides an introduction to the theory of probability, the part of mathematics
that studies random phenomena. We model simple random experiments mathematically and learn
techniques for studying these models. Topics covered include probability spaces, random
variables, weak law of large numbers, central limit theorem, and various discrete and
continuous probability distributions.

Syllabus

Spring 2023 – Pepperdine University

**Course Overview**

Math 150 provides an introduction to first-semester calculus, from
rates of change to integration, with an emphasis on understanding,
problem solving, and modeling. Topics covered include key concepts
of the derivative and definite integral, techniques of differentiation,
and applications.

Syllabus

**Course Overview**

Math 260 provides an introduction to Linear Algebra, covering much of
Chapters 1 – 7. Topics covered include systems of linear equations and
linear transformations; matrix determinant, inverse, rank, eigenvalues,
eigenvectors, factorizations, diagonalization, singular value decomposition;
linear independence, vector spaces and subspaces, bases, dimensions; inner
products and norms, orthogonal projection, Gram-Schmidt process,
least squares; applications.

Syllabus

Fall 2022 – Pepperdine University

**Course Overview**

Math 150 provides an introduction to first-semester calculus, from
rates of change to integration, with an emphasis on understanding,
problem solving, and modeling. Topics covered include key concepts
of the derivative and definite integral, techniques of differentiation,
and applications.

Syllabus

Fall 2021 – University of Arizona

**Course Overview**

MATH 464 provides an introduction to the theory of probability, the
part of mathematics that studies random phenomena. We model simple
random experiments mathematically and learn techniques for studying
these models. Topics covered include probability spaces, random
variables, weak law of large numbers, central limit theorem, various
discrete and continuous probability distributions.

Syllabus

Spring 2021 – University of Arizona

**Course Overview**

DATA/MATH 363 will be using your background in the natural or social sciences, the humanities, or engineering and your previous knowledge of algebra, calculus and linear algebra to consider the issues of collection, model derivation and analysis, interpretation, explanation, and presentation of data. The objective of this course is to take advantage of the coherent body of knowledge provided by statistical theory having an eye consistently on the application of the subject. This approach will allow you to extend your ability to use methods in data science beyond those given in the course.

Syllabus

Fall 2020 – University of Arizona

**Course Overview**

This course is a continuation of MATH 122B or MATH 125 that will examine the techniques of symbolic and numerical integration, applications of the definite integral to geometry, physics, economics, and probability; differential equations from a numerical, graphical, and algebraic point of view; modeling using differential equations, approximations by Taylor series.

Syllabus

**Course Overview**

This course acts as an introduction to the basic techniques of numerical analysis and provides insight into both the theory and algorithms for fundamental mathematical problems associated with systems of equations, optimization, and approximation of functions. The course assumes familiarity with linear algebra and calculus, and requires the use of the programming language, MATLAB.

Syllabus

Spring 2020 – University of Arizona

**Course Overview**

Statistics is the field of study involving (1) the collection, summarization, and analysis of data; and (2) the drawing of inferences about a population from the examniation of a sample of the population. The goals of this course are to introduce each student to the practice of statistics and to provide an overview of common topics in statistical inference.

Syllabus

Fall 2019 – University of Arizona

**Course Overview**

MATH 122B is a 4-unit course, during which students will be challenged to develop their calculus-related understanding, problem-solving, and modeling skills.

Syllabus

**Course Overview**

This course is designed as a complement to MATH 120R. Students enrolled in the course will participate in a weekly problem session pertaining to material covered in MATH 120R. Concurrent registration in MATH 120R is required.

Syllabus