Exponential View, an independent research organization, is hiring its first Research Fellow—an economist to study how AI affects economic value, labor markets, and firm performance over six to twelve months in London. The Fellow will translate frontier economic theory into empirical analysis on compressed timelines, investigating questions like AI valuation, value chain distribution, and wage impacts. The successful candidate will produce publicly visible research outputs and gain exposure to a network of economists, technologists, and policymakers while developing applied research skills under real-world conditions.
A new AI workspace platform has been developed to help researchers conduct reproducible scientific research. The platform integrates AI tools to streamline workflows and improve documentation standards. This enables scientists to more easily share, verify, and build upon each other's work.
A specialized semismooth Newton method is proposed to solve kernel-based optimal transport problems more efficiently than the short-step interior-point method used by existing estimators. Each SSN iteration is made significantly cheaper in practice, and the method is shown to converge globally at O(1/√k). This reduces computation time enough to deliver substantial speedups on synthetic and real datasets, making the kernel-based OT estimators more scalable to larger sample sizes.
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