Kingsley UkanduBook a meeting

About

A mathematical path toward
disease dynamics and data.

My work has moved from mathematical foundations to infectious-disease models, then outward into scientific machine learning and explainable health-data analysis.

The path is broader now, but the habit underneath it is the same: understand the structure before trusting the output.

Portrait of Kingsley Chimuanya Ukandu

Where it began

Mathematics first gave me a language for structure.

I began my university mathematics training in Nigeria, earning a B.S. in Mathematics at the University of Nigeria, Nsukka. Graduate study at the African University of Science and Technology deepened that foundation through functional analysis, optimization, and related areas of applied mathematics.

Those subjects trained me to look for the shape of a problem: the spaces in which a solution lives, the constraints it must satisfy, and the conditions under which a conclusion is actually justified.

The research trajectory

One foundation, several connected questions.

  1. 01

    Nigeria

    B.S. in Mathematics

    University of Nigeria, Nsukka

  2. 02

    Graduate mathematics

    Functional analysis & optimization

    African University of Science and Technology

  3. 03

    UNLV

    Mathematical epidemiology

    Population movement, spatial heterogeneity, disease persistence, equilibria, and stability.

  4. 04

    Data analytics

    Scientific and data-informed learning

    An M.S. in Data Analytics adds statistical learning and computation to the mathematical foundation.

  5. 05

    Current extension

    Equation-informed and explainable models

    Disease-informed neural networks and careful interpretation of health-related classifiers.

The intellectual center

Movement changes the epidemic question.

At UNLV, my doctoral research asks how connected populations change the long-run behavior of SIS disease models. The mathematics is about equilibria, persistence, stability, and bifurcation; the motivating idea is that place and movement cannot always be averaged away.

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The expanding toolkit

Data adds evidence—and new ways to be wrong.

My data-analytics training has made model evaluation, leakage, class imbalance, and interpretability part of how I think. Scientific machine learning connects most directly to the equations; explainable health-data work provides another lens on model behavior and error.

View the projects

Teaching & community

Mathematics is also something I teach, discuss, and build community around.

My teaching at UNLV has progressed from calculus through differential equations and multivariable calculus to undergraduate and graduate real analysis. That range keeps me close to how mathematical ideas are first encountered and how they mature.

Leadership as President of the Society for Black Scientists complements that work through community-building, professional development, and STEM service.

Teaching & leadership record

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Beyond the equations

Quiet places leave room to think.

Away from research, I enjoy quiet environments, long walks, nature, movies, traveling when I get the chance, and learning new technologies. Those quieter moments give me space to reset, think, and stay curious beyond the mathematics.

01 / 02
Kingsley seated outdoors in patterned formal attire near water and trees
01A quieter setting—space for reflection and curiosity away from research.
Kingsley seated beside the waterfront in a red shirt with city buildings in the distance
02A waterfront pause at the close of a conference trip.