Behind every data point is a person.

It started with science

My academic foundation began in biology and chemistry. Those disciplines taught me to think rigorously, question assumptions, and follow the evidence wherever it leads. That scientific mindset has never left me. It just found a new home in data.

A decade in healthcare operations

After completing my M.S. in Healthcare Administration at the University of Denver, I spent a decade working in healthcare, from frontline operations to regional management, most recently as a revenue cycle manager. I also worked with the State of Colorado, where navigating strict regulatory environments became second nature.

I understood how organizations run, where the inefficiencies hide, and what questions leadership actually needs answered. The information was always there.

The project that changed everything

During my master's program, I collaborated with Centura Health on a capstone project to evaluate and streamline clinical workflows using High-Reliability Organization principles. We built a system that processed real-time healthcare data from EPIC, patient monitoring devices, and administrative databases, incorporating predictive analytics and machine learning to forecast patient admissions, resource utilization, and potential complications.

The outcomes helped streamline ulcerative colitis protocol, optimize resource allocation during COVID-19, and reduce significant operational costs. That project ignited something in me and set the direction for everything that followed.

Going deeper at MIT

I pursued my Applied AI, Machine Learning, and Data Science certificate at MIT to sharpen my skills in machine learning, deep learning, and Python-based analysis. For my capstone, I analyzed and compared ANN (Artificial Neural Network) and CNN (Convolutional Neural Network) architectures to improve facial emotion recognition, achieving 91% accuracy across happy, sad, neutral, and surprised categories through careful tuning of dropout layers and training epochs.

The program reinforced my ability to work across multiple model types, analyze data patterns, and build systems that generalize well. It also completed a picture I had been building for years: deep domain knowledge, technical fluency, and a genuine drive to make data useful.

What I believe

My philosophy is simple: provide simple solutions to complex problems, leveraging data to establish a consistent and less fragmented experience for the people who depend on it. I value integrity above all else. Data can be shaped to tell many stories, and I am committed to telling the right one.

I am compassionate, quality-driven, and passionate about this work. Healthcare taught me that behind every data point is a person. That awareness does not leave you, and I think it makes for better analysis.