Adolfo R. Escobedo

Associate Professor

Adolfo R. Escobedo is an educator and researcher in the field of industrial engineering and operations research. He joined the Edward P. Fitts Department of Industrial and Systems Engineering as an associate professor.

Research Interests

Escobedo’s research interests are in the theory and application of mathematical programming and computing, specifically in the design and analysis of algorithms for power systems operations and planning, circular economy, computational social choice, and computational linear algebra.

Education

DegreeProgramSchoolYear
Ph.D.Doctor of Philosophy in Industrial EngineeringTexas A&M University2016
BAMBachelor of Arts in MathematicsCalifornia State University-Los Angeles2009

Awards and Honors

  • 2024 | NSF Faculty Early Career Development (CAREER) Award
  • 2023 | INFORMS Minority Issues Forum (MIF) Early Career Award
  • 2023 | IISE Operations Research Track Best Paper Award
  • 2021 | INFORMS Computing Society (ICS) Prize
  • 2020 | Top 5% Best Teacher (Arizona State University)
  • 2016 | U.S. Senator Phil Gramm Doctoral Fellowship (Texas A&M University)
  • 2015 | Finalist for the INFORMS Junior Faculty Forum Paper Competition
  • 2015 | Jimmy H. Smith, Ph.D., P.E. Graduate Scholarship (Texas A&M University)
  • 2014 | Energy Institute/Conoco Phillips Fellowship (Texas A&M University)
  • 2012 | Graduate Fellowship (Texas A&M University)
  • 2012 | LSAMP Bridge to the Doctorate Fellowship (Texas A&M University)

 

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Publications

Elicitation and aggregation of multimodal estimates improve wisdom of crowd effects on ordering tasks
Yoo, Y., Escobedo, A. R., Kemmer, R., & Chiou, E. (2024), SCIENTIFIC REPORTS, 14(1). https://doi.org/10.1038/s41598-024-52176-3
Approximate Condorcet Partitioning: Solving large-scale rank aggregation problems
Akbari, S., & Escobedo, A. R. (2023), Computers & Operations Research. https://doi.org/10.1016/j.cor.2023.106164
Assessing the Effects of Expanded Input Elicitation and Machine Learning-Based Priming on Crowd Stock Prediction
Bhogaraju, H., Jain, A., Jaiswal, J., & Escobedo, A. R. (2023). , . https://doi.org/10.1007/978-3-031-41774-0_1
Beyond kemeny rank aggregation: A parameterizable-penalty framework for robust ranking aggregation with ties
Akbari, S., & Escobedo, A. R. (2023), Omega. https://doi.org/10.1016/j.omega.2023.102893
Derivations of large classes of facet defining inequalities of the weak order polytope using ranking structures
Escobedo, A. R., & Yasmin, R. (2023), JOURNAL OF COMBINATORIAL OPTIMIZATION, 46(3). https://doi.org/10.1007/s10878-023-01075-w
Exact Matrix Factorization Updates for Nonlinear Programming
Escobedo, A. R. (2023, September 6), INFORMS JOURNAL ON COMPUTING, Vol. 9. https://doi.org/10.1287/ijoc.2021.0331
Software for "Exact Matrix Factorization Updates for Nonlinear Programming"
Escobedo, A. (2023), INFORMS Journal on Computing. https://doi.org/10.1287/ijoc.2021.0331.cd
An axiomatic distance methodology for aggregating multimodal evaluations
, (2022). To Appear in Information Sciences.
A New Binary Programming Formulation and Social Choice Property for Kemeny Rank Aggregation
Yoo, Y., & Escobedo, A. R. (2021), Decision Analysis, 18(4), 296–320. https://doi.org/10.1287/deca.2021.0433
A decision support tool for calculating waste collection needs
Kassem, Z., Gudivada, V. S., Escobedo, A. R., & Campbell, W. F. (2021), Institute of Industrial and Systems Engineers (IISE) Annual Conference, 944–949. Retrieved from https://www.proquest.com/openview/0fb7ed2195ee0bdaa5e4ed7bb1b35a70/1?pq-origsite=gscholar&cbl=51908

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Adolfo R. Escobedo