基本信息
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职业迁徙
个人简介
My research interest lies in data-driven decision-making. I strive to understand theoretically how to use most efficiently observed data to make decisions. The goal is to derive from these theoretical insights novel algorithms that tackle challenges in data-driven decision-making. My recent work focuses on designing novel robust approaches to Machine Learning problems (and more generally Stochastic Optimization) using Distributionally Robust Optimization. The goal is to understand what are the sources of overfitting (poor generalization) in data-driven stochastic optimization and design robust approaches precisely protecting against such sources. These approaches are typically provably optimal (i.e the best that can be done with the data) when a certain out-of-sample guarantee is desired.
研究兴趣
论文共 10 篇作者统计合作学者相似作者
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arXiv (Cornell University) (2023)
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arxiv(2023)
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Estee Y Cramer,Evan L Ray,Velma K Lopez,Johannes Bracher, Andrea Brennen,Alvaro J Castro Rivadeneira, Aaron Gerding,Tilmann Gneiting,Katie H House,Yuxin Huang, Dasuni Jayawardena,Abdul H Kanji,
Proceedings of the National Academy of Sciences of the United States of Americano. 15 (2022): e2113561119-e2113561119
Lecture Notes in Operations ResearchAI and Analytics for Smart Cities and Service Systemspp.254-268, (2021)
Social Science Research Network (2021)
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