Bio

Adolfo 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.

Area(s) of Expertise

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.

Publications

• Efficient Multi-Column LU Factorization Updates for Exact Linear Programming , Zenodo (CERN European Organization for Nuclear Research) (2026)
• Efficient Multi-Column LU Factorization Updates for Exact Linear Programming , Zenodo (CERN European Organization for Nuclear Research) (2026)
• Heuristic Methods for Top-k List Aggregation Under the Generalized Kendall Tau Distance , Lecture notes in computer science (2026)
• Why Districting Becomes NP-hard , SSRN Electronic Journal (2026)
• Data-driven robust transmission expansion planning against rising temperatures^* , Environmental Research Infrastructure and Sustainability (2025)
• Whose Wisdom? Human Biases for Decision Support System Source and Scale , Proceedings of the Human Factors and Ergonomics Society Annual Meeting (2025)
• Elicitation and aggregation of multimodal estimates improve wisdom of crowd effects on ordering tasks , Scientific Reports (2024)
• Predicting the composition of solid waste at the county scale , Waste Management (2024)
• Approximate Condorcet Partitioning: Solving large-scale rank aggregation problems , Computers & Operations Research (2023)
• Assessing the Effects of Expanded Input Elicitation and Machine Learning-Based Priming on Crowd Stock Prediction , Communications in computer and information science (2023)

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