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In this talk, we discuss the opportunities and challenges of designing simulation-based optimization (SO) algorithms to tackle high-dimensional urban mobility problems. An important component in high-dimensional problems is the exploration-exploitation tradeoff. Our past work has focused mainly on improving the exploitation capabilities of SO algorithms. In this work, we focus on designing exploration techniques suitable for high-dimensional spaces. We consider a Bayesian optimization setting, and propose the use of a simple analytical traffic model to specify the covariance function of a Gaussian process. We show how this enables the Bayesian optimization method to more efficiently sample in high-dimensional spaces. We present validation experiments on synthetic low-dimensional problems. We then apply the method to a high-dimensional traffic control problem for Midtown Manhattan, in NYC.
Bio: Carolina Osorio is an Associate Professor in the Department of Decision Sciences at HEC Montreal, where Osorio holds the SCALE AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. Osorio is also a Visiting Faculty at Google Research. Osorio's work develops operations research techniques to inform the design and operations of urban mobility systems. It focuses on simulation-based optimization algorithms for, and analytical probabilistic modeling of, congested urban mobility networks. Osorio was recognized as one of the outstanding early-career engineers in the US by the National Academy of Engineering's EU-US Frontiers of Engineering Symposium, and is the recipient of a US National Science Foundation CAREER Award, an MIT CEE Maseeh Excellence in Teaching Award, an MIT Technology Review EmTech Colombia TR35 Award, an IBM Faculty Award and a European Association of Operational Research Societies (EURO) Doctoral Dissertation Award.