I am a PhD student in Information Science at Cornell Tech, working with Emma Pierson and Nikhil Garg.
My research leverages spatial machine learning methods and creates novel datasets to inform more equitable urban policies. I focus on addressing challenges related to sparse and biased spatial data: specifically, how to transform coarse, crowdsourced, and irregularly collected information into actionable insights for improved city resource allocation.
In recent work, I examined under-reporting bias in resident reporting systems (such as New York City's 311), migration patterns in the United States at scale, and disparate access to urban amenities. I rely on spatial analysis techniques combined with computational and statistical frameworks including Bayesian optimization, hierarchical modeling, and network analysis. You can find a full list of my past and ongoing work in the publications tab. I also co-organize a working group on Urban Data Science under EAAMO Bridges, and we are always looking for members or guest speakers!
Prior to joining Cornell, I earned a B.S. in Applied Mathematics and a B.A. in Urban Studies from Columbia University. I have written quite a lot about cities back then, and included some of this work under the fun tab.
My email is gsagostini@infosci.cornell.edu. Send me a message if you are interested in my research!