As an AI research scientist + economist + data scientist all-in-one, Dan has over 9 years of experience across industry, academia, and government, ranging from the White House to the Federal Reserve, BlackRock, Twitter, NASA, MIT, etc. He has worked in areas such as public policy, international trade, finance, technology, quantum computing, social media, healthcare/biotech, security, drug discovery, and much more.
His research interests are in generative AI as well as efficient/robust learning in NLP, computer vision, and within deep learning optimization in general. Examples include (but not limited to): efficient multi-modal learning, sustainable and green A.I. with a focus on mitigating the computational/carbon costs of deep learning workloads, interactions between large language models (LLMs) and foundation models as agents, and distillation/interpretability of LLMs. He is also interested in the economic and societal implications of technological advancements in general.
In addition to research/publications ranging from machine learning conferences such as NeurIPS to engineering/distributed computing venues at IEEE/ACM as well as economic journals like Empirical Economics, and more, Dan also has extensive experience making fundamental contributions to ML‑powered systems that are still in use today---from models/algorithms powering asset-pricing engines to those optimizing deep learning training workloads at scale among others.
He received a BA from Williams College and did part of his PhD (ABD) in Economics at Yale University.