Currently, I am a postdoctoral researcher in the Department of Mathematics at the University of Arizona, though broadly, I would describe myself as an educator and applied mathematician. 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 techniques to model, analyze, and explore relational data. To learn more about my professional experience, along with my teaching philosophy, research background, and diversity stance, please hover over the nodes on the adjacent network!
Email email@example.com Office Telephone (520) 621-6870 Office MATH 319 Office Hours (Fall 2021) Mon: 2:30pm - 3:30pm (Zoom) Wed: 2:30pm - 3:30pm (Zoom) Thurs: 11am - 12pm (UDT; Teams) Or by appointment (All times are in MST)
Durón C. (2021). Linear Algebra, Computational. In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels). https://doi.org/10.1002/9781118445112.stat00459.pub2
Durón C. (2020). Heatmap Centrality: A New Measure to Identify Super-Spreader Nodes in Scale-Free Networks. PLoS ONE, 15(7): e0235690. doi: 10.1371/journal.pone.0235690
Durón C., Pan Y, Gutmann DH, Hardin J, Radunskaya A. (2019). Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes. Bulletin of Mathematical Biology, 81(9):3655‐3673. doi:10.1007/s11538-018-0526-z
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 (Loading...)
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.
Past Courses (Loading...)
Spring 2021 – University of Arizona
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.
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.
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.
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.
MATH 122B is a 4-unit course, during which students will be challenged to develop their calculus-related understanding, problem-solving, and modeling skills.
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.