Data Science · AI Systems · Research Infrastructure
OCR pipelines · geospatial decision tools · computer vision workflows · applied AI
Colorado School of Mines
Selected Systems
Three flagship case studies showing research infrastructure, geospatial analytics, and computer vision workflows.
Technical Capabilities
Organized by the kinds of systems I build: AI models, research data infrastructure, geospatial analysis, and reproducible tools.
Build and evaluate applied ML workflows with an emphasis on evidence, model behavior, and real-world data quality.
Design repeatable workflows for collecting, extracting, validating, and structuring research datasets.
Analyze geographic constraints, spatial relationships, and suitability criteria for decision-support projects.
Use statistical reasoning, scientific computing, and visualization to turn data into clear analysis.
Work comfortably across the command line, version control, databases, and production-oriented project structure.
Explain technical work clearly through documentation, teaching, literature review, and reproducible workflows.
Academic Background
A data science graduate path grounded in machine learning, statistics, research workflows, and business-focused communication.
Colorado School of Mines
Graduate study focused on data science, machine learning, statistics, and software workflows for real-world analysis.
Relevant Coursework
Colorado State University
Business and marketing background that supports technical communication, decision-making, and applied analytics.
Applied Experience
Practical work in research data workflows, technical feedback, and reproducible analysis.
Colorado School of Mines
Supporting a research project on the history of economic geology education across U.S. universities through Python-based data mining and archival processing workflows.
Colorado School of Mines
Reviewed technical projects and supported students working through data science, machine learning, and computer science concepts.
About
I'm a data science graduate student at Colorado School of Mines with a business background and a growing focus on research data infrastructure.
I care about building models and pipelines, but also about making data understandable and connected to real decisions. That is why many of my projects combine technical workflows with maps, validation, documentation, and decision-support outputs.
As a research assistant, I build Python workflows to discover, process, and extract information from historical course catalogs using web scraping, OCR, information extraction, validation, and dataset development.
I like projects where the hard part is turning inconsistent records into something reliable.
I care about analysis that helps people compare options, ask better questions, and act.
I document assumptions, edge cases, and workflow choices so the work can be reused.
I'm interested in roles where I can use data science, machine learning, AI, and software engineering to solve real problems, support better decision-making, and build systems that make complex data easier to use.
Contact
I'm interested in opportunities where I can work with real-world data, build reproducible systems, and turn complex information into useful insights.