Currently, I am a postdoctoral researcher in the Mathematics Department at the University of Arizona, though broadly, I would describe myself as an educator and applied mathematician. Teaching mathematics is and will forever be my passion, and I am continually striving to become a more effective teacher by building and applying new tools and skills to motivate students both inside and outside of the classroom. To that end, I look to provide opportunities for students to gain research experience. Currently, I am working with two undergraduates, where my research is focused on network theory and network analysis. Specifically, I am looking to develop new computational techniques to model, analyze, and explore relational data. . If you would like to know more about my background or work, please check out my CV or publications. Additional information about my teaching philosophy, research background, and diversity stance can be found by checking out my statements.
Email firstname.lastname@example.org Office Telephone (520) 621-6870 Office MATH 319 Office Hours (Spring 2021) Tues: 1pm - 12pm (Zoom) Wed: 1pm - 2pm (Zoom) Thurs: 1pm - 2pm (Zoom) Or by appointment (All times are in MST)
Durón C. (2021). Linear Algebra, Computational. In /Wiley StatsRef: Statistics Reference Online/. Davidian, M., Kenett, R.S., Longford, N.T., Molenberghs, G., Piegorsch, W.W., and Ruggeri, F., eds. Chichester: John Wiley & Sons. 2021; Article No. stat00459.pub2. doi: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. Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes. Bulletin of Mathematical Biology. 2019; 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...)
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 | Normal Dist | Student's t Dist | F Dist | Chi-Squared Dist
Past Courses (Loading...)
Fall 2020 – University of Arizona
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.