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 multiscale 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 IndustryHealthcareGovernmentInternational 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 blindstudy.
APPM 1360  Calculus 2 for Engineers
Primary Instructor

Spring 2020 / Fall 2020 / Summer 2021 / Fall 2021 / Summer 2022 / Summer 2023
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 / Summer 2021 / Summer 2022
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.
CSCI 2820  Linear Algebra with Computer Science Applications
Primary Instructor

Summer 2023 / Spring 2024
Introduces the fundamentals of linear algebra in the context of computer science applications. Includes vector spaces, matrices, linear systems, and eigenvalues. Includes the basics of floating point computation and numerical linear algebra.
CSCI 3022  Introduction to Data Science with Probability and Statistics
Primary Instructor

Summer 2023
Introduces students to the tools methods and theory behind extracting insights from data. Covers algorithms of cleaning and munging data, probability theory and common distributions, statistical simulation, drawing inferences from data, and basic statistical modeling. Same as CSPB 3022.
CSCI 3656  Numerical Computation
Primary Instructor

Spring 2024
Covers development, computer implementation, and analysis of numerical methods for applied mathematical problems. Explores topics such as floating point arithmetic, numerical solution of linear systems of equations, root finding, numerical interpolation, differentiation, and integration.
CSCI 5502  Data Mining
Primary Instructor

Fall 2023
Introduces basic data mining concepts and techniques for discovering interesting patterns hidden in largescale data sets, focusing on issues relating to effectiveness and efficiency. Topics covered include data preprocessing, data warehouse, association, classification, clustering, and mining specific data types such as timeseries, social networks, multimedia, and Web data. Same as CSCI 4502.
CSCI 5622  Machine Learning
Primary Instructor

Spring 2023 / Spring 2024
Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Qlearning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended.
DTSA 5707  Deep Learning Applications for Computer Vision
Primary Instructor

Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024
Students will learn about Computer Vision as a field of study and research. They explore several Computer Vision tasks and suggested approaches, from the classic Computer Vision perspective. They'll be introduced to Deep Learning methods and apply them to some of the same problems. They will analyze the results and discuss advantages and drawbacks of both types of methods. Examples of Computer Vision tasks where Deep Learning can be applied include: image classification, image classification with localization, object detection, object segmentation, facial recognition, and activity or pose estimation.
INFO 4652  Statistical Programming in R
Primary Instructor

Fall 2022
This intensive course covers foundational data science tools and techniques in the R programming language, including acquiring, cleaning, exploring, and analyzing data, programming, and conducting reproducible research. The course will emphasize the use of data management best practices such as the tidyverse toolkit in R. Same as INFO 5652.
INFO 5652  Statistical Programming in R
Primary Instructor

Fall 2022
This intensive course covers foundational data science tools and techniques in the R programming language, including acquiring, cleaning, exploring, and analyzing data, programming, and conducting reproducible research. The course will emphasize the use of data management best practices such as the tidyverse toolkit in R. Same as INFO 4652.
MATH 1012  Quantitative Reasoning and Mathematical Skills
Primary Instructor

Fall 2018
Promotes mathematical literacy among liberal arts students. Teaches basic mathematics, logic, and problemsolving 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 firstorder scalar differential equations, nth order linear differential equations, and ndimensional linear systems of firstorder 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 / Fall 2021 / Spring 2022
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 realworld datasets.
STAT 3400  Applied Regression
Primary Instructor

Spring 2020 / Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022
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.
STAT 5010  Statistical Methods and Applications II
Primary Instructor

Fall 2022 / Spring 2023 / Fall 2023
Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 4010.
STAT 5600  Methods in Statistical Learning
Primary Instructor

Fall 2022
Provides an introduction to methods in the field of statistical learning. Topics include a review of multiple regression, assessing model accuracy, classification, resampling methods, model selection and regularization, nonlinear regression, treebased methods, support vector machines and unsupervised learning. Involves handson data analysis using the R programming language.