Cynthia Zeng is an Assistant Professor at the NYU Stern School of Business in Abu Dhabi, and a research affiliate of the MIT Sloan School of Management. Her research addresses the challenges of climate adaptation and sustainable development through technological innovation. Cynthia specializes in developing artificial intelligence solutions to forecast and manage extreme weather events. Her PhD thesis, titled “Multimodal Machine Learning for Climate Adaptation,” includes selected works on hurricanes prediction, flood prediction, near-term wind prediction, and flood insurance pricing. In addition to academic pursuits, Cynthia gained valuable industry experience as a quantitative analyst at BlackRock Systematic Equities, generating alpha signals using data science; at the SoftBank Vision Fund, focusing on late-stage investments in AI tech unicorns. She holds a bachelor’s degree in mathematics from Imperial College London, and she completed her PhD in Operations Research from Massachusetts Institute of Technology (MIT).
Dr. Zeng’s research has been featured in top conferences and journals including INFORMS, NeurIPS, ICML, ICLR, and Nature Digital Medicine. Her publications explore both environmental and healthcare applications of machine learning, emphasizing data-driven solutions for global challenges. In addition to her academic career, Cynthia brings industry and venture experience from her time as a quantitative analyst at BlackRock Systematic Equities, where she developed alpha-generating models using data science, and at the SoftBank Vision Fund, where she evaluated late-stage AI technology investments. She currently serves as Head of Strategy at Fundamental Research Labs, an applied AI company building socially intelligent and collaborative agents, including shortcut.ai, the first AI-powered Excel agent.
Driven by a belief in technology’s power to save lives, Dr. Zeng’s work aims to equip vulnerable regions such as Pakistan, Sri Lanka, and the Pacific Islands with early-warning systems and data insights to improve resource allocation, disaster response, and urban planning in the face of escalating climate risks.