Technical practitioner in data science—Bayesian modeling, ML pipelines, and interpretable outputs. I write code that scales and explains.
A practical guide to Bayesian regression, showing how to encode prior knowledge for robust inference in small or outlier-prone datasets. In this post, I contrast frequentist and Bayesian approaches, emphasizing the advantages of informed priors and richer uncertainty estimates.
My Data Science master's degree capstone project aimed at developing an early-warning model for ICU HF Patients.
My interactive QRS Complex Annotation Tool for a graduate course project.
Describing my master's capstone project, where I'm utilizing deep learning and multi-modal data to predict readmissions for ICU heart failure patients.
Explaining one of the cornerstones of probability theory. A critical precursor to complex probabilistic models.
A brief writeup on the overlap and distinction between two of the buzziest topics of our time.
"Wrangling and Regression Modeling of Scraped Zillow Listings". An example from my early learning days back in 2020, that was hosted on my deprecated site. Shows an end-to-end project with scraping web data, collating and processing it, and analyzing it for rough prediction.