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Publications in VIVO

Onyejekwe, Osita Eluemuno Instructor


Research Areas research areas


research overview

  • Dr. Onyejekwe's research involves using multivariate regression models to estimate glacier changes as a means of explaining mountain glacier recession due to increased global temperatures. His model also predicts the location of glacier terminus over time, based on observed climate factors. Further, Dr Onyejekwe's research implemented a novel approach for noise reduction in signals corrupted with both gaussian and correlated noise via multi-scale kernel regression in conjunction with matched filtering. He then selected the bandwidth that yielded the highest Signal to Noise Ratio in order to estimate the unknown underlying signal. Dr. Onyejekwe also helped Burgio Enterprises Ltd; an Industry-Healthcare-Government-International research agency contribute to the company's first research finding on Degenerative Disc Disease in the Active Military Special Forces using a computerized data processing system to conduct a quadruple blind-study.


  • Climate Change, Mountain Glaciers, Statistical Analysis, Non-Parametric Regression, Multivariate Models, Signal Processing


selected publications


courses taught

  • APPM 1360 - Calculus 2 for Engineers
    Primary Instructor - Spring 2020 / Fall 2020
    Continuation of APPM 1350. Focuses on applications of the definite integral, methods of integration, improper integrals, Taylor's theorem, and infinite series. Degree credit not granted for this course and MATH 2300.
  • APPM 2360 - Introduction to Differential Equations with Linear Algebra
    Primary Instructor - Summer 2020
    Introduces ordinary differential equations, systems of linear equations, matrices, determinants, vector spaces, linear transformations, and systems of linear differential equations. Credit not granted for this course and both MATH 2130 and MATH 3430.
  • MATH 1012 - Quantitative Reasoning and Mathematical Skills
    Primary Instructor - Fall 2018
    Promotes mathematical literacy among liberal arts students. Teaches basic mathematics, logic, and problem-solving skills in the context of higher level mathematics, science, technology, and/or society. This is not a traditional math class, but is designed to stimulate interest in and appreciation of mathematics and quantitative reasoning as valuable tools for comprehending the world in which we live. Degree credit not granted for this course and MATH 1112.
  • MATH 2300 - Calculus 2
    Primary Instructor - Fall 2019
    Continuation of MATH 1300. Topics include transcendental functions, methods of integration, polar coordinates, differential equations, improper integrals, infinite sequences and series, Taylor polynomials and Taylor series. Department enforced prerequisite: MATH 1300 or MATH 1310 or APPM 1345 or APPM 1350 (minimum grade C-). Degree credit not granted for this course and APPM 1360.
  • MATH 2510 - Introduction to Statistics
    Primary Instructor - Fall 2018 / Spring 2019 / Summer 2019 / Fall 2019
    Elementary statistical measures. Introduces statistical distributions, statistical inference, hypothesis testing and linear regression. Department enforced prerequisite: two years of high school algebra.
  • MATH 3430 - Ordinary Differential Equations
    Primary Instructor - Spring 2019
    Involves an elementary systematic introduction to first-order scalar differential equations, nth order linear differential equations, and n-dimensional linear systems of first-order differential equations. Additional topics are chosen from equations with regular singular points, Laplace transforms, phase plane techniques, basic existence and uniqueness and numerical solutions. Formerly MATH 4430.
  • STAT 2600 - Introduction to Data Science
    Primary Instructor - Spring 2021
    Introduces students to importing, tidying, exploring, visualizing, summarizing, and modeling data and then communicating the results of these analyses to answer relevant questions and make decisions. Students will learn how to program in R using reproducible workflows. During weekly lab sessions students will collaborate with their teammates to pose and answer questions using real-world datasets.
  • STAT 3400 - Applied Regression
    Primary Instructor - Spring 2020 / Fall 2020 / Spring 2021
    Introduces methods, theory, and applications of linear statistical models, covering topics such as estimation, residual diagnostics, goodness of fit, transformations, and various strategies for variable selection and model comparison. Examples will be demonstrated using statistical programming language R.


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