I joined the residency after working as a technical consultant and data scientist in the electricity industry, where I supported power grid operators and policymakers on decisions related to the integration of renewable energy and emerging technologies. Prior to working in industry, I completed my Ph.D. in Engineering and Public Policy at CMU, where I researched quantitative methods for decision-making under uncertainty in climate and energy policy. My work in energy increasingly used methods from machine learning, which led me to have a greater interest in AI and its broader implications for society. The residency gave me an exciting opportunity to switch research fields while pursuing my core interests of decision-making under uncertainty and technology policy, and I am currently working on quantifying uncertainty in the predictions of deep neural networks. I hope to build a research program that better characterizes and systematizes uncertainty in deep learning, and develops methods that help enable safer, more reliable AI systems. The residency has been a great experience -- I’ve learned so much in the short time that I’ve been here, I really value the freedom to direct my own research, and it’s a privilege to collaborate with and learn from some of the best researchers in the field.