IEOR-DRO Seminar: Xi Chen (NYU)

April 25, 2017 | 1:10pm - 2:10pm

IEOR-DRO Seminar: Xi Chen (NYU)

Mudd Hall 303
 
Title: Optimal Sparse Designs for Process Flexibility via Probabilistic Expanders
 
Abstract:
We discuss the problem of how to design a sparse flexible process structure in production system to match supply with random demand more effectively. Our goal is to provide an optimal design, i.e., the sparsest design, to achieve the  (1-eps)-optimality of the fully flexible system.  To achieve this goal, we introduce a new concept called probabilistic graph expanders. We prove that a probabilistic expander with O(n \ln({1/\eps})) arcs guarantees (1-eps)-optimality with high probability (w.h.p.). For balanced and homogeneous demand models, easy-to-implement randomized and deterministic constructions of probabilistic expanders are provided. We further provided a thresholded proportionally probabilistic construction (TPPC) for unbalanced and heterogeneous demand models. This is the joint work with Tengyu Ma, Jiawei Zhang and Yuan Zhou.

Bio:
Xi Chen is an assistant professor at Department of Information, Operations, and Management Sciences at Stern School of Business at New York University. Before that, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley. He obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University (CMU); and his Masters degree in Operations Research from the Tepper School of Business at CMU.
 
He studies machine learning for crowdsourcing and high-dimensional statistics. He also studies operations research/management problems, such as the process flexibility, and data-driven revenue management. He received Simons-Berkeley Research Fellowship, Google Faculty Research Award, and was featured in 2017 Forbes list of “30 Under30 in Science”.
 


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