index.qmd

---
title: "hierarchy"
author: "Kiran Gauthier"
listing:
  contents: posts
  sort: "date desc"
  type: default
  categories: true
  sort-ui: false
  filter-ui: false
page-layout: full
title-block-banner: true
categories: [intro]
---

## About `hierarchy`

Welcome to `hierarchy`, a blog about using regression, causal inference, machine learning, and hierarchical (multilevel) modeling to distinguish signal from noise in data; usually by thinking about the [data generating process](https://bob-carpenter.github.io/stan-getting-started/stan-getting-started.html).

I graduated with my PhD from Columbia University in 2022, where the focus of my [dissertation](https://clio.columbia.edu/catalog/16899459?counter=1) was the use of *computational statistics* to optimize experimental design of nonlinear, hierarchical models. Most of the work I do involves **Python** {`TensorFlow`, `PyTorch`, `PyMC`, `Scipy`}, **R** {`brms`, `cmdstanr`, `tidyverse`}, **Stan** (with `BridgeStan`), and a bit of **Julia** to answer the questions I have about my data. I'm passionate about statistical modeling to quantify uncertainty, causal inference, stochastic processes, game theory, and [scientific ML](https://sciml.ai/).

Find me [here](https://www.linkedin.com/in/kiran-gauthier/), or send me an [email](mailto:kiran.gauthier@columbia.edu)!