Seth Bassetti

Hi! I’m Seth Bassetti, a non-tenure track professor at Utah State University from Cle Elum, WA. I have a deep passion for computer science, education, and artifical intelligence.

I discovered computer science through pure chance while living a nomadic van life in the southwestern US. On a semester off of my undergraduate, I met someone on the road who introduced me to coding, which convinced me to continue my studies in a new field!

Since then, I have worked as a python developer in fintech, an educator and product manager with AI Camp, a graduate researcher in computer vision at Western Washingon University! I recently began teaching at Utah State University to share my love of computer science with others! I continually find myself excited by new technologies and pushing the boundaries of what is possible with artifial intelligence.

selected publications

DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models

Seth Bassetti, Brian Hutchinson, Claudia Tebaldi, and 1 more author

Journal of Advances in Modeling Earth Systems, 2024

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Earth System Models (ESMs) are essential tools for understanding the impact of human actions on Earth’s climate. One key application of these models is studying extreme weather events, such as heat waves or dry spells, which have significant socioeconomic and environmental consequences. However, the computational demands of running a sufficient number of simulations to analyze the risks are often prohibitive. In this paper we demonstrate that diffusion models – a class of generative deep learning models – can effectively emulate the spatio-temporal trends of ESMs under previously unseen climate scenarios, while only requiring a small fraction of the computational resources. We present a diffusion model that is conditioned on monthly averages of temperature or precipitation on a 96×96 global grid, and produces daily values that are both realistic and consistent with those averages. Our results show that the output from our diffusion model closely matches the spatio-temporal behavior of the ESM it emulates in terms of the frequency of phenomena such as heat waves, dry spells, or rainfall intensity.