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David Goldberg is an Adjunct Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.
Dr. Goldberg works in applied probability, interpreted broadly, on topics ranging from inventory control and queueing theory to distributionally robust and combinatorial optimization. Much of his work focuses on using ideas from probability theory to prove that high-dimensional complex systems can be well-approximated by much simpler systems, and using these insights to devise novel algorithms with provable performance guarantees. His work in inventory control has focused on applying this mantra to challenging problems in which there is a lead-time delay between when an order is placed and when it is received, such as lost sales models and dual-sourcing problems, for which Dr. Goldberg has derived some of the first nearly optimal efficient algorithms. His work in queueing theory has focused on the so-called stochastic comparison approach to multi-server queues, in which one proves that certain performance metrics (such as the probability of rare events) can be bounded above and below by those of more tractable models. He has also investigated robust approaches to optimizing inventory models with demand forecasting, as well as certain questions regarding the independent sets of large graphs.
He has received several honors for his work, including an NSF CAREER award, first place in the 2015 George Nicholson Student Paper Competition, second place in the 2015 JFIG Paper Competition, as well as finalist in the 2014 MSOM Student Paper Competition and 2010 George Nicholson Student Paper Competition.
Dr. Goldberg received his undergraduate degree in computer science at Columbia University, minoring in both industrial engineering/operations research and applied math. He completed his Ph.D. at the MIT Operations Research Center.