Peter Carr Seminar Series: Yuki Miura & Youssef Mroueh
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Yuki Miura
Title
Quantifying Climate Risk and Adaptation: From Physical Hazards to Financial Decision-Making
Abstract
Climate risks are increasingly material to financial systems; however, translating physical hazard dynamics into actionable risk and investment decisions remains a fundamental challenge. This talk presents a unified framework for identifying, measuring, and managing climate risk through the integration of engineering-based hazard modeling with financial and socioeconomic analysis.
The framework combines high-resolution physical models of hazards, such as flooding, with data-driven methods to estimate infrastructure impacts, economic losses, and cascading system disruptions. These approaches extend beyond traditional scenario analysis by enabling forward-looking, probabilistic stress testing of extreme events and adaptation strategies. A central focus is the linkage between physical risk and financial decision-making. Methods are presented to quantify the value of adaptation investments under uncertainty, accounting for interdependencies across infrastructure systems and non-market impacts.
Case studies from New York City and other urban systems demonstrate how these approaches can inform decision-making by governments, financial institutions, and infrastructure operators. The talk concludes by outlining key challenges at the intersection of climate science and financial engineering, including model validation under deep uncertainty, the integration of AI-driven methods, and the development of decision frameworks that remain robust under evolving climate conditions.
Bio
Dr. Yuki Miura is an Assistant Professor at New York University, with appointments in the Department of Mechanical and Aerospace Engineering and the Center for Urban Science and Progress. She also serves on the Faculty Advisory Board of the Volatility and Risk Institute at NYU Stern School of Business and is a member of the New York City Panel on Climate Change (NPCC5).
Her research focuses on quantifying and managing climate risk at the interface of physical systems and financial decision-making. She develops computational frameworks that integrate hazard modeling, infrastructure analysis, and economic impact assessment to support risk-informed investment and policy decisions. Her work emphasizes translating complex physical risks into decision-ready metrics for governments, financial institutions, and infrastructure operators.
Prior to academia, Dr. Miura worked at Morgan Stanley in climate risk management and quantitative strategy, where she contributed to firmwide risk identification and assessment efforts. She has collaborated with public agencies, including New York City and New York State. Her work has been published in leading journals and featured in major media outlets, including The New York Times and The New Yorker.
Youssef Mroueh
Title
Gromov-Wasserstein Gradient Flows
Abstract
The Wasserstein space of probability measures offers a rich Riemannian structure for gradient flow algorithms, but it may not always suit tasks where preserving global data structure is crucial. To address this, we explore gradient flows in the Gromov-Wasserstein (GW) geometry, which aligns more naturally with scenarios requiring global structural preservation. We focus on the inner product GW (IGW) distance, which retains data angles and provides analytical tractability. By proposing an implicit IGW minimizing movement scheme, we generate sequences of distributions that align in the GW sense. Our analysis reveals the intrinsic Riemannian structure of IGW geometry and establishes a Benamou-Brenier-like formulation for IGW flows, offering insights for potential biological applications. Numerical results demonstrate IGW's capacity for capturing global structures. Joint work with Zhengxin Zhang, Ziv Goldfeld, Kristjan Greenewald, and Bharath k. Sriperumbudur.
Bio
Youssef Mroueh is a Principal Research Scientist in IBM Research with the Human Centered Trustworthy AI department. He received his PhD in computer science in February 2015 from MIT, CSAIL, where he was advised by Professor Tomaso Poggio. In 2011, he obtained his engineering diploma from Ecole Polytechnique Paris France, and a Master of Science in Applied Mathematics from Ecole des Mines de Paris. He is interested in Optimal transport, Generative Modeling, Deep multimodal learning, Large Language models, trustworthy ML, Statistical Learning Theory, AI for scientific discoveries , and AI for social good.
