Research

The core of Maria's dissertation: using machine learning and GIS to understand where malaria strikes in Uganda and where the tools to fight it fall short. Comparing Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest models, Random Forest predicted district-level incidence most reliably — with antimalarial treatment, mosquito-net access, and temperature as the strongest drivers.

A second strand analyzed 260 weekly surveillance reports (2020–2024) and surfaced a "mismatch contradiction": as treatment (ACT) understocking eased, diagnostic-test (RDT) deficits stayed roughly tenfold higher — evidence that manual logistics cannot fix geographic maldistribution, and that AI-guided redistribution could.

Random Forest R² ≈ 0.88 · 260 weeks of surveillance data · 31 districts flagged as chronic-oversupply cold spots

The Random Forest paper → · The commodity-mismatch paper →

As a Research Fellow at CGU's Center for Information Systems & Technology, Maria develops the next generation of geospatial AI methods — and teaches them. She designed IST 371: GeoAI & Geospatial Deep Learning, a graduate course integrating machine learning, spatial analytics, and geospatial deep learning, and supervises doctoral research on the strategic adoption of generative GeoAI in organizations.

Beyond malaria, Maria applies spatial and statistical methods to broader health equity questions. A DESRIST 2025 chapter designed a geospatial tool for examining low-birth-weight disparities across California, weighing income, healthcare access, and education. A related study modeled the stress and demographic factors predicting mental-health challenges among postgraduate students at Makerere University.

Low-birth-weight geospatial tools → · Postgraduate mental health →

Since 2023, Maria has been curriculum consultant and institutional liaison for the Harvey Mudd College Clinic–Musizi University partnership, mentoring three student teams as they build computing and software-engineering curricula for Uganda's first private liberal arts university — aligning course content with the National Council for Higher Education's standards and Ugandan workforce needs. She contributed materials for 11 general-education and upper-level computing courses and co-authored an experience report for the 2026 Capstone Design Conference.

The Capstone experience report →

Across three degree levels and two universities, Maria has supervised student research spanning health information systems, GeoAI, and computing education.

Maria has also advised three Harvey Mudd College Clinic teams developing computing curricula for Musizi University (2023–2026) — described in the Computing Education thread above.