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My research lives at the intersection of computational mathematics and spatial science: building rigorous, mathematically grounded methods for how patterns, relationships, and flows organize themselves across geographic space. At its core, the work is optimization and movement: finding the structure in how things distribute, cluster, and travel. Current work centers on diffusion distance and Markov-chain measures of spatial autocorrelation, with applications in geospatial clustering, network analysis, and optimal transport.
My research lives at the intersection of computational mathematics and spatial science: building rigorous, mathematically grounded methods for how patterns, relationships, and flows organize themselves across geographic space. At its core, the work is optimization and movement: finding the structure in how things distribute, cluster, and travel. Current work centers on diffusion distance and Markov-chain measures of spatial autocorrelation, with applications in geospatial clustering, network analysis, and optimal transport.
Quick Links
Quick Links
A Geometry-Aware Method for Spatial Clustering Analysis and Detection
A Geometry-Aware Method for Spatial Clustering Analysis and Detection
I developed a geometry-aware approach to detecting and analysing spatial clustering that uses diffusion on graph-based representations of places. Framed as a Markov‑chain mixing process, the method measures how quickly an initial population distribution dissipates under local averaging to reveal geometric clustering and bottlenecks that classical statistics (e.g., Moran’s I) can miss. The framework provides a family of diffusion distances, comes with provable bounds on mixing and stability, and admits exact formulas under permutation null models; applied to Black population distributions across 100 U.S. cities, diffusion distance consistently identifies meaningful geometric structure beyond what Moran’s I detects. I presented this work at the 21st Regional Mathematics and Statistics Conference and UNC Greensboro’s Graduate Research Seminar (2025). Read more on LinkedIn
CHYF: AI‑Assisted Micro‑Planning for Streamlined Focus and Execution
CHYF: AI‑Assisted Micro‑Planning for Streamlined Focus and Execution
CHYF (Chidinma Helps You Focus) is an LLM‑assisted micro‑planning companion that harnesses large language models to support everyday focus and task execution. Given a free‑form or highly structured description of what someone wants to do, CHYF generates a refined, humane sequence of steps—adding buffers, re‑ordering for cognitive load, and suggesting small but meaningful adjustments rather than simply mirroring the input. It lives at the intersection of human‑computer interaction, productivity support, and calm, assistive AI. You are invited to explore the CHYF web app and read my reflection on LinkedIn.
SpartyWiz: Human-Centered AI Innovation
SpartyWiz: Human-Centered AI Innovation
SpartyWiz is an AI-powered campus assistant redefining how students access information. Via Retrieval-Augmented Generation (RAG) and Large Language Models, SpartyWiz connects UNCG’s internal knowledge bases with conversational AI to provide accurate, real-time, and inclusive 24/7 support for all students. SpartyWiz won second place in the 2025 UNCG AI Innovation Challenge and the People's Choice Award. Read more about it here: Campus Weekly, Math & Stats News, Research, LinkedIn.
Co-Mentoring Undergraduate Student Research
Co-Mentoring Undergraduate Student Research
I co-mentored two undergraduate students in the Computational Statistics REU program at UNC Greensboro. Working with Dr. Thomas Weighill, our research spanned pertinent areas in the discipline, including Topological Data Analysis (TDA), Geospatial Data Analysis, Optimal Transport, Statistics, and Machine Learning. This research was presented at the culmination of the program, where the participating students were awarded certificates for their research milestones.
REU 2025
REU 2025
Group Photo | UNCG Computational Stats. Research Experience for Undergraduates (REU)
Research Team
Research Team
Award Photo | Final Presentation
Innovative Instructional Methods Harnessing Geospatial Data
Innovative Instructional Methods Harnessing Geospatial Data
I developed Adaptive Socio-Spatial Learning (ASSL), a geospatially informed instructional method that integrates data analysis and computational techniques to improve learning outcomes across institutional levels. In classroom deployments at the middle‑school and undergraduate levels, ASSL increased student understanding, retention, and overall performance; middle‑school students taught with ASSL outperformed peers on a statewide exam. I presented these results at the 2025 North East Regional Computing Conference (NERCOMP), sponsored by EDUCAUSE, and the work was featured in UNCG’s Research Magazine for Social Innovation. I was also honored with the 2024 Tutor Spotlight Award for this instructional innovation.
NC A&T State University
NC A&T State University
Math Learning Specialist | Academic Recovery Workshops
Math Learning Specialist | Academic Recovery Workshops
As a Math Learning Specialist at North Carolina A&T State University, I worked with students enrolled in the Math Scholars Institute (MSI)'s academic recovery program, where students who had previously failed Calculus and College Algebra were given a second opportunity to pass the course. I designed a series of weekly academic workshops, coaching, and advising sessions, where I shared strategies for problem decomposition, understanding complex questions, and developing independent analytic skills in mathematics.
This 10-workshop series covered diverse applications of mathematics, ranging from language and engineering to cybersecurity and finance. Each workshop began by addressing the critical "Why?" question and included engaging topics like "Math Unveiled: Unlocking the Power of Numbers in Your Future." By framing mathematics as an exciting journey of problem-solving, I help students overcome common perceptions of the subject as "difficult" or "irrelevant."
MSI Academic Recovery Workshop Series Topics
MSI Academic Recovery Workshop Series Topics
North Carolina Agricultural & Technical State University | The Math Scholar's Institute - Academic Recovery Summer Program
Ezugwu, Chidinma. "Unlocking Your Math Potential: Embracing Your Learning Style." Workshop presented at the Math Scholars Institute, North Carolina A&T State University, Summer 2024.
Ezugwu, Chidinma. "Decoding Complex Problems: Analytical Skills Bootcamp." Workshop presented at the Math Scholars Institute, North Carolina A&T State University, Summer 2024.
Ezugwu, Chidinma. "Mastering Math Study Skills: Techniques for Effective Learning and Retention." Workshop presented at the Math Scholars Institute, North Carolina A&T State University, Summer 2024.
Ezugwu, Chidinma. "Mastering Math: Advanced Study Techniques for Complex Concepts." Workshop presented at the Math Scholars Institute, North Carolina A&T State University, Summer 2024.
North Carolina Agricultural & Technical State University | Aggie Success Academy S.I.S.T.E.R.S. - Female Students Mentorship Program in Mathematics
Ezugwu, Chidinma. "Math Unveiled: Unlocking the Power of Numbers in Your Future." Workshop presented at the Aggie Success Academy Residential Pre-College Summer Program, North Carolina A&T State University, Summer 2024.
Ezugwu, Chidinma. "Math Empowerment: Transforming Mindsets, Accelerating Skills." Workshop presented at the Aggie Success Academy Residential Pre-College Summer Program, North Carolina A&T State University, Summer 2024.
Ezugwu, Chidinma. "Math Fundamentals: Learning FUN-damentals." Workshop presented at the Aggie Success Academy Residential Pre-College Summer Program, North Carolina A&T State University, Summer 2024.
Ezugwu, Chidinma. "Math Refresher: Mastering Linear Inequalities in Two Variables." Workshop presented at the Aggie Success Academy Residential Pre-College Summer Program, North Carolina A&T State University, Summer 2024.
Ezugwu, Chidinma. "Math Refresher II: Navigating Quadratic Functions." Workshop presented at the Aggie Success Academy Residential Pre-College Summer Program, North Carolina A&T State University, Summer 2024.
NC A&T Center for Academic Excellence | S.I.S.T.E.R.S Summer of Success
MSI Final Workshop Series Presentation Title Slide (July 2024)
National Aeronautics and Space Administration (NASA) Student Launch Challenge
National Aeronautics and Space Administration (NASA) Student Launch Challenge
Image credit: NASA
As a member of the Aggie Rocketry team (North Carolina A&T), I led the computational analysis for the 2025 NASA Student Launch Challenge, developing methods to model 3‑D rocket velocity from IMU data. By fusing linear-acceleration measurements with a tailored prediction algorithm, I produced velocity estimates that closely matched flight telemetry and improved prediction accuracy through targeted optimizations. The team qualified for the finals on May 5th in Huntsville, Alabama, north of NASA's Marshall Space Flight Center at Bragg Farms in Tony, Alabama. Work completed Spring 2025; the project was part of the NASA Student Launch Challenge (2024–2025).
More Works
More Works
Conferences
Conferences
2025
2025
21st Annual Regional Mathematics and Statistics Conference
Ezugwu, C. (2025). Diffusion Distance and Permutations for Spatial Autocorrelation Analysis. Presented at the 21st Annual Regional Mathematics and Statistics Conference, Greensboro, North Carolina.
UNCG Mathematics and Statistics | Graduate Research Seminar
Ezugwu, C. (2025). Diffusion Distance and Permutations for Spatial Autocorrelation Analysis. Presented at the Mathematics and Statistics Graduate Research Seminar, Greensboro, North Carolina.
NorthEast Regional Computing Program (NERCOMP) and EDUCAUSE Annual Conference 2025, Rhode Island, USA
Ezugwu, Chidinma. "Innovative Instructional Methods for Diverse Learners in a Post-Pandemic World: Adaptive Socio-Spatial Learning." Presented at the NERCOMP Annual Conference 2025. Graduate Teaching Associate, Department of Mathematics and Statistics, University of North Carolina at Greensboro
Links to Session Abstract and Poster.
2023
2023
19th Annual Regional Mathematics and Statistics Conference
Ezugwu, C. (2023). Reducing the Number of Node Expansions by DIBBS. Presented at the 19th Annual Regional Mathematics and Statistics Conference, Greensboro, North Carolina.
National Science Foundation Grant no. DMS-2324883.
Institute for Partners in Education & GCS Tutors Conference
Ezugwu, C. (2023). Momentous Little Efforts: Going the Extra Mile as a Tutor. Presented at The Inaugural Annual Tutoring Conference | Guilford County Schools & Institute for Partners in Education (Tutoring Collaborative), Greensboro, North Carolina.
2017
2017
International Women's Day Featured Speaker
Ezugwu, C. (2017). Keynote Speaker, “Every Woman Has a Story – Cross-Atlantic Educational Journey in STEM. Presented at International Women's Day Series | Castleton University, Vermont.
Graduate-Level Projects
Graduate-Level Projects
2024
2024
"Customer Churn Prediction" | Solving Problems with Data Analysis Professional Graduate-Level Course
High-level analysis of customer churn prediction using machine learning (ML) modeling techniques.
2023
2023
"Enhancing MRI Classification through Optimal Thresholding in Machine Learning" - Big Data and Machine Learning
This study investigates advanced methodologies for the classification of brain MRI scans, employing sophisticated image processing techniques and machine learning algorithms to differentiate between neoplastic and non-neoplastic cases. While previous research has focused on minimizing the F1 score for threshold selection, this approach may negatively impact other performance metrics. To address this, the study evaluates various thresholding methods to enhance overall model performance. Additionally, it incorporates principal component analysis (PCA) to create new feature spaces from the original data, allowing for a comprehensive exploration of informative features. By improving thresholding strategies and utilizing PCA, the study offers deeper insights into model performance and enhancing the accuracy of brain tumor predictions using machine learning algorithms.
2022
2022
“Performance Evaluation of Resampling Methods in Claims Reserving” - Statistical Computing Project at UNC Greensboro
In this paper, we evaluate the performance of prominent resampling methods in claims reserving. This graduate study focused on resampling Pearson residuals obtained from fitting a quasi-Poisson model to loss data and utilized the predicted future losses in computing future reserves. Via numerical analysis, we determine the more effective resampling methods based on computed prediction errors and other pertinent metrics.
2021
2021
“Reducing the Number of Node Expansions by DIBBS” - Southern illinois University Master's Thesis
This paper discovers and proves various interesting and useful properties of a new Bidirectional Heuristic Search (BHS) algorithm that dynamically improves the bounds during its execution. The algorithm, Dynamically Improved Bounds Bidirectional Search (DIBBS), has the property that it always terminates on or before the forward search meets the backward search. It also has the ability to solve certain problems while expanding fewer nodes than prominent BHS algorithms like GBFHS and MMe. Ultimately, we present a theorem that can be used in reducing the number of nodes expanded by DIBBS in solving a shortest path problem. SIUE, Spring 2021.
2019
2019
(Team) Solved the Institute of Industrial Engineers and Rockwell Automation’s Third Annual Contest Problem 3 | Graduate Advanced Simulation Course Final project
This work is a collaborative project with colleagues in the Advanced Simulation Modeling course at SIUE. Using ARENA Event Simulation software, we model, analyze, and solve –with remarkable results—the Institute of Industrial Engineers and Rockwell Automation’s Third Annual Contest Problem 3: Sally Model’s SM Pizza Shop. SIUE, Fall 2019.
2018
2018
“Cognitive Learning Preferences” | Applied Statistics with SPSS
This project (supervised by Dr. A. Rajia) used IBM SPSS to identify the cognitive learning preferences of Castleton University Applied Statistics students. Findings were analyzed to inform instructional strategies and better align course delivery with the diverse ways students process and retain quantitative information.
Market Research for Student Recruitment at Vermont State University, Castleton | Integrated Marketing and Communications Practicum, Mathematics Department
I was selected by the VSU University Dean of Advancement to conduct research on recruiting new mathematics majors. I developed and executed recruitment strategies in collaboration with the department head, including targeted engagement emails and announcements, managing the departmental email inbox, redesigning the website to attract prospective students, and optimizing overall math department student engagement.
2017
2017
“Economic Development Research” at The Chamber & Economic Development of the Rutland Region
While working as the Economic Development Intern, I conducted research, analyzing Rutland County census and demographic data to identify trends and business development opportunities to inform decision-making for regional improvement. I conducted stakeholder interviews, collaborated with community leaders — including the Paramount Theater Executive Director and university outreach managers — and led a semester-long, data-driven study on strategies to strengthen Rutland's economic sector. Additional contributions included executive report writing and a website content audit with optimization recommendations.
Papers & Articles
Papers & Articles
Ezugwu, C. C. (2017). Stagflation in Nigeria: A New Theory and Case Study. https://doi.org/10.13140/RG.2.2.23147.31528. Paper link
Ezugwu, C. C. (2021). Reducing the number of node expansions by DIBBS (M.S. thesis, Department of Mathematics and Statistics, Southern Illinois University, Edwardsville). Thesis link
Ezugwu, C. C. (2021, May 20). A Voyage for the Light [LinkedIn Article]. LinkedIn. Article link
Williams, C. C. (2024, November 24). Emerging Trends in Retail Credit and Spending [LinkedIn Article]. LinkedIn. Article link
Williams, C. C. (2024, November 24). Borrowed Time: Mapping the Geography of Household Credit Distress in the U.S. [LinkedIn Article]. LinkedIn. Article link
Williams, C. C. (2025, December 3). Launching CHYF: A Gentle Planner for a Loud World [LinkedIn Article]. LinkedIn. Article link
Software
Software
The following is a (non-exhaustive) list of software I have used in these domains:
Computational Mathematics
Mathematical Modeling
Geospatial Data Analysis
Simulation & Optimization
Big Data & Complex Data Analysis
Statistical Computing & Nonparametric Statistics
Machine Learning & Artificial Intelligence
Graph Theory & Network Analysis
Optimal Transport
Markov Chain Modeling
Software & Tools:
Languages & Environments: R, Python, MATLAB, SAS OnDemand
Statistical Software: IBM SPSS Statistics
AI & APIs: Groq, OpenAI, RAG (Retrieval-Augmented Generation)
Web & App Development: R Shiny
Simulation: ARENA Discrete Event Simulation
Blogs
Blogs
The Spirit-Filled Heart (SFH) Faith-Inspired Blog
The Spirit-Filled Heart is a passion dear to my heart. It is a warm, faith-inspired blog created to share uplifting content, encouragement, and Christ-centered reflections.
You are welcome to explore the blog here: Spirit-Filled Heart.
Adobe Education Institute (AEI) 2026 Session Portfolio
Editorial-style research content website created during the "Building Clear, Engaging Webpages with Adobe Express" workshop offered during AEI 2026.
View the portfolio here: Chidinma Williams' AEI 2026 Portfolio.
View the website here: Chidinma Williams' Adobe Website.
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