Alex Estes

About

Alex Estes is an Assistant Professor at the Robert H. Smith School of Business. He holds a PhD from the University of Maryland. His research involves exploring current and future paths in collaborative research, with a focus on initiatives involving Amazon Research and AWS. His work emphasizes research collaborations and the development of innovative programs in the field of business.

Research topics

• Computer Science
• Mathematics
• Mathematical optimization
• Machine Learning
• Operations research
• Data Mining
• Microeconomics
• Engineering
• Statistics
• Algorithm

Selected publications

• Smart Predict-then-Optimize for Two-Stage Linear Programs with Side Information

  INFORMS Journal on Optimization · 2023 · 12 citations

  1st authorCorresponding

  We study two-stage linear programs with uncertainty in the right-hand side in which the uncertain parameters of the problem are correlated with a variable called the side information, which is observed before an action is made. We propose an approach in which a linear regression model is used to provide a point prediction for the uncertain parameters of the problem. We use an approach called smart predict-then-optimize. Rather than minimizing a typical loss function for regression, such as squar…

  PublisherDOI

• Unsupervised prototype reduction for data exploration and an application to air traffic management initiatives

  EURO Journal on Transportation and Logistics · 2018-08-13 · 5 citations

  articleOpen access1st authorCorresponding

  We discuss a new approach to unsupervised learning and data exploration that involves summarizing a large data set using a small set of “representative” elements. These representatives may be presented to a user in order to provide intuition regarding the distribution of observations. Alternatively, these representatives can be used as cases for more detailed analysis. We call the problem of selecting the representatives the unsupervised prototype reduction problem. We discuss the KC-UPR method…

  PublisherDOI

• Data-Driven Planning for Ground Delay Programs

  Transportation Research Record Journal of the Transportation Research Board · 2017-01-01 · 5 citations

  article1st author

  This paper provides a model-based approach to planning ground delay programs. Previous research on automated planning of ground delay programs has involved the use of mathematical programming techniques. This paper proposes a data-driven method that models the problem of choosing a traffic management initiative by using the framework of the multiarmed bandit decision problem. This approach makes greater use of the available data, and suggestions made by this procedure can be shown along with dat…

  PublisherDOI

• Predicting performance of ground delay programs

  2017-01-01 · 4 citations

  article1st authorCorresponding

• Objective-Aligned Regression for Two-Stage Linear Programs

  SSRN Electronic Journal · 2019-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Michael O. Ball

  15 shared

• David J. Lovell

  5 shared

• Mark Hansen

  2 shared

• Yulin Liu

  2 shared

• Jean‐Philippe P. Richard

  University of Minnesota

  2 shared

• Ankur Mani

  2 shared

Labs

• Smith and Amazon Collaborative ResearchPI

Education

• Ph.D., Applied Mathematics & Statistics, and Scientific Computation

  University of Maryland at College Park

  2018

• Bachelor of Sciences, Mathematics

  University of Nebraska-Lincoln

  2013

Similar researchers at University of Maryland, College Park

• Bruce L. Goldenh-74
• Siva Viswanathanh-60