Context: Rural households often lack access to affordable energy assessments, making it hard to reduce costs or qualify for assistance programs.

 

Data: Local utility and housing data; publicly available energy efficiency benchmarks.

 

Methods: Independent design and development; stack TBD as build progresses.

 

Outcome: A web-based energy audit tool built for local community use in Lamoni, IA, designed to be accessible to residents without technical backgrounds.

 

So what: This tool puts actionable energy information directly in the hands of the people who need it most, supporting rural household resilience and potential qualification for assistance programs.

Context: Homeowners and community gardeners repeatedly lose plants to conditions they can't see: water pooling in one corner, running off another, soil and light varying across a single yard. Without that information, planting becomes trial and error, and the cost of failure falls hardest on households growing food on a budget.

 

Data: Public sources only: USGS 3DEP LiDAR elevation data, ArcGIS parcel boundaries, OpenAddresses address points, USDA SSURGO soil survey data, and a plant database assembled from USDA PLANTS, the National Wetland Plant List, and a regional roster of Decatur County natives.

 

Methods: Independent design and development. Database-backed R Shiny application built on watershed delineation from LiDAR-derived terrain models, classifying each parcel into four hydrology zones: where water sheds, collects, passes through, and sits in the middle. Architected to run fully offline on self-hosted infrastructure: every source is downloaded once and stored locally, with no network calls at runtime. Currently deployed publicly while local deployment is being finalized. v2 in progress adds soil texture and pH, seasonal shadow modeling, and expanded plant attributes for light, soil, water, and use.

 

Outcome: A web app where a resident looks up their address, sees how water actually moves across their own yard, and prints a Plant Card recommending species suited to each zone. v1 is functional and in use; v2 is under active development.

 

So what: This turns public data a household could never process on its own into something they can act on in their backyard. Building it offline and self-hosted keeps the tool and our community's data under local control rather than dependent on a vendor. By making site conditions legible to non-technical users, it lowers the barrier to successful food and habitat planting, a small but concrete piece of household food security and rural resilience.

Context: The relationship between diet and gene expression is central to understanding chronic disease, but nutrigenomic research is rarely made accessible outside academic circles.

 

Data: NCBI RefSeq, Ensembl, ClinVar, GWAS Catalog.

 

Methods: R, tidyverse; multi-database synthesis; written analysis.

 

Outcome: A data-driven investigation of dietary and genetic factors influencing immune-related gene expression, synthesized across multiple public genomic databases.

 

So what: Demonstrates the ability to ask new questions across complex datasets and to communicate findings in a way that connects molecular science to real human health outcomes.

Context: What parents eat before and during pregnancy has measurable effects on fetal development, but the research is scattered and underutilized in prenatal nutrition programs.

 

Data: Existing peer-reviewed literature; independent meta-analysis.

 

Methods: Systematic literature synthesis; independent research; academic writing.

 

Outcome: A solo meta-analysis investigating how maternal and paternal dietary patterns influence fetal gene expression, with direct relevance to prenatal nutrition program design and maternal health policy.

 

So what: This research has real implications for programs like WIC and prenatal care by translating scientific evidence into actionable guidance for community health practitioners.

Context: Irritable Bowel Syndrome affects millions of people, yet its relationship to gut microbiome composition is still being mapped. Understanding these patterns has direct implications for dietary intervention and clinical nutrition.

 

Data: Human Microbiome Project 2 (HMP2) dataset.

 

Methods: R; machine learning (classification and clustering); exploratory data analysis.

 

Outcome: A data-driven analysis identifying microbiome composition patterns associated with IBS, with implications for nutrition-based intervention strategies.

 

So what: Connects data science directly to the kind of diet-disease relationships that inform community nutrition programs and clinical care.

Context: Understanding relationships between genetic variation and clinical phenotypes requires tools that support flexible, exploratory analysis and that are accessible beyond a purely technical audience.

 

Data: ClinVar, GWAS summary data, Ensembl and RefSeq annotations.

 

Methods: R, tidyverse, ggplot2, ggplotly, R Shiny; reproducible preprocessing pipelines.

 

Outcome: An interactive Shiny application enabling dynamic exploration of isoform architecture, variant distributions, phenotypic associations, and gene-level summaries.

 

So what: Built to demonstrate that complex biomedical data can be made explorable and interpretable. The same principle applies whether the data is genomic or community health data.

Context: Nursing home quality data is publicly available but difficult for patients, families, and administrators to interpret without a technical background.

 

Data: CMS Provider Data Catalog  |  Facility-level quality metrics across nursing home chains nationwide.

 

Methods: Google Data Studio; data cleaning and preprocessing; dashboard design for non-technical audiences.

 

Outcome: An interactive dashboard surfacing quality and performance metrics across nursing home chains in a format accessible to patients, families, and healthcare administrators.

 

So what: Making complex health system data readable and actionable for the people it affects most.