Trainee Spotlight
Lee Dunnigan
Research focus: Soil plays a vital role in storing carbon, making it essential to understand the processes that influence carbon ecosystem cycling. Traditionally, the remains of plants and animals were thought to be the main drivers of soil carbon dynamics. However, new research suggests that microbial byproducts may have an even greater impact on carbon cycling. Studying these compounds is challenging due to the complexity of soil, but this research aims to improve chemical analysis and machine learning techniques to better identify and classify them. By doing so, it will provide deeper insights into how soil stores carbon over time to reduce global atmospheric carbon.
My future goals: My goal is to earn my PhD and pursue a career at the intersection of environmental science and machine learning. I’m particularly interested in applying data-driven approaches to improve our understanding of complex environmental systems and fundamental biogeochemistry at the molecular level. I ultimately hope to become an expert who can help address pressing climate and sustainability challenges.
Why AI4S NRT: The NRT program caught my interest because of its clear goal of building a community of grad students and faculty with shared interests in AI and sustainability. When I was encouraged to check it out, it immediately felt like the perfect fit. It is a rare chance to get connect with many members of Cornell whose research isn’t directly related to mine but who still offer incredible guidance, knowledge, and genuine support for the success of my work.
Christian Belardi
Research focus: My research explores a variety of topics in machine learning, particularly generative models, as well as how they can be applied to solve complex challenges in scientific domains. Though I work on a number of problems, my work enabled by the NRT fellowship focuses on developing approaches that will enhance the existing capabilities of microscopists. While capabilities for text and image domains continue to develop rapidly, models and methods for specialized scientific problems remain underdeveloped. Our objective is to fill this gap for Multislice Electron Ptychography (MEP). Our current work greatly improves atomic resolution reconstructions of crystal structures from MEP diffraction patterns.
My future goals: My primary goal is to contribute to the field of machine learning through fundamental research. Beyond this, I hope to help address humanity’s critical challenges by accelerating scientific discovery with machine learning and, more generally, computational methods.
Why AI4S NRT: The NSF NRT fellowship presents a unique opportunity to interact and collaborate with researchers beyond my own field. Our work on MEP is truly an interdisciplinary collaboration, made possible by unique programs like Cornell’s AI for Sustainability initiative. I’m grateful to be a part of this community developing innovative solutions for global sustainability challenges.