Sigurdur (Siggi) Olafsson

Title(s):

Associate Professor
Industrial & Manufacturing Systems Engineering

Office

2144 Therkildsen Industrial Engineering Building
531 Bissell Road
Ames, IA 50011-1096

Information

Education

  • PhD, University of Wisconsin-Madison, Industrial Engineering, 1998
  • MS, University of Wisconsin-Madison, Industrial Engineering, 1996
  • BS, University of Iceland (Reykjavik), Mathematics, 1994

Interest Areas

Dr. Olafsson’s research area is in operations research and data analytics. Most of his research projects are heavily motivated by applications, especially in the plant breeding domain. Some key recent contributions include a novel approach that guides plant breeders in selecting planting locations that are more informative in terms of the discriminating the main phenotype effects of different commercial food crop cultivar, and several new methods for improving cultivar selection in commercial plant breeding. While much of his focus has been in plant breeding, he has worked in numerous other application areas, ranging from manufacturing defect detection to precision healthcare. 
 
In addition to applied work, Dr. Olafsson has made significant contributions to data analytics methods, this includes new methods for ensemble learning, data clustering, synthetic data generation, and feature selection. He is also the co-inventor of the nested partitions method, a metaheuristic that has been used to solve many large-scale discrete optimization problems. 

Publications

Sample of recent publications

Data Science Methods (sample of recent)

  1. G. Arwade* and S. Olafsson. 2025. Learning Ensembles of Interpretable Simple Structures. Annals of Operations Research 353, 841-869.  https://doi.org/10.1007/s10479-025-06674-w.
  2. H. Pham* and S. Olafsson. 2020. “On the Cesáro Averages for Weighted Trees of the Random Forest” Journal of Classification, 37: 223-236.
  3. S. Karimzadeh* and S. Olafsson. 2019. “Data Clustering using Proximity Matrices with Missing Data,” Expert Systems with Applications, 126: 265-276.
  4. H. Pham* and S. Olafsson. 2019. “Bagged Ensembles with Tunable Parameters,” Computational Intelligence, 35(1): 184-203.

Applications (sample of recent)

  1. S. Tohidi* and S. Olafsson. 2025. Probabilistic Ranking of Plant Cultivars: Stability Explains Differences from Mean Rank. Frontiers in Plant Science. doi:10.3389/fpls.2025.1553079 
  2. M. Kiaghadi* and S. Olafsson. 2025. Pareto Optimization for Selecting Discriminating Test Locations in Plant Breeding. Agronomy 15(4), 935. Doi: 10.3390/agronomy15040935
  3. R. Bijari*, H. Vemireddy* and S. Olafsson. 2025. A Method for Approximate Rank Confidence Intervals in Plant Breeding Experiments. Heliyon 11(5). doi.org/10.1016/j.heliyon.2025.e42973 
  4. H. Vemireddy¨ and S. Olafsson. 2023.  A regression approach to identify discriminating locations. Crop Science,  63,  598–612. https://doi.org/10.1002/csc2.20873

Highly cited older publications

  1. Olafsson, S., X. Li*, and S. Wu*. 2008. Operations research and data mining. European Journal on Operational Research, 187, 1429-1448.
  2. Shi, L., and S. Olafsson. 2000. Nested partitions method for global optimization. Operations Research, 48, 390-407.

* Student author

Primary Strategic Research Area

Resilient Infrastructures

Departments

Affiliations

Interests

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