From b09a317f3a45bc988b0ab80d2ac182b57b00b98c Mon Sep 17 00:00:00 2001
From: Tyler Hoffman <50304432+tdhoffman@users.noreply.github.com>
Date: Mon, 28 Oct 2024 15:53:59 -0400
Subject: [PATCH] Update index.md

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 ## About
-Coming from a background of math and computer science, I develop algorithms and statistical methods for spatial data science. My work aims to better represent and mathematically model spatial problems and to chip away at our understanding of core geographical concepts, such as process and scale. Through algorithm design, I also seek to advance the principled use of (spatial) data science in social science. For more information about my research, [click here](https://tdhoffman.com/research) (slightly outdated). In my spare time, I love watching movies, playing piano, and biking. I am a recipient of the [NSF Graduate Research Fellowship](https://www.nsfgrfp.org/) and I'm currently a Ph.D student at Arizona State University in the Spatial Analysis Research Center (SPARC). 
+Coming from a background of math and computer science, I develop algorithms and statistical methods for spatial data science. My work aims to better represent and mathematically model spatial problems and to chip away at our understanding of core geographical concepts, such as process and scale. Through algorithm design, I also seek to advance the principled use of (spatial) data science in social science. For more information about my research, [click here](https://tdhoffman.com/research) (slightly outdated). In my spare time, I love watching movies, playing piano, and biking. Previously, I was a recipient of the [NSF Graduate Research Fellowship](https://www.nsfgrfp.org/) and a Ph.D student at Arizona State University in the Spatial Analysis Research Center (SPARC). 
 
 ## News and Updates
 **28 Sep 2023:** My paper, ["Controlling for spatial confounding and spatial interference in causal inference"](https://doi.org/10.1080/19475683.2023.2257788), has been published in *Annals of GIS*! Check it out for a review of spatial causal modeling ideas and to explore relative performance of these models on different spatial datasets! (With Peter Kedron.)