Jared Beekman


Mr. Beekman has 7+ years of experience as a researcher, analyst, and consultant. Since 2014, Mr. Beekman has worked as an analyst and consultant for Innovative Decisions, Inc., with leading roles in projects involving data science, systems engineering, operations research, and decision analysis. In a supporting analyst position with Military Sealift Command, Mr. Beekman employed statistical modeling techniques to design and implement energy audit data collection tools, used to inform a model of ships’ energy consumption. As a lead analyst for Commander Naval Installations Command, Mr. Beekman maintained a manpower model for public safety program, incorporating cost modeling and risk analysis to influence resource allocation decisions. Mr. Beekman used deterministic modeling and queuing theory to optimize shift and staffing profiles for Navy Regional Dispatch Centers. Mr. Beekman also teaches an introductory training course on Bayesian Networks.

Prior to Mr. Beekman’s position at Innovative Decisions, he spent two years working as a researcher for the National Science Foundation. He received funding to conduct wind energy research using statistical analysis at the Technical University of Denmark in 2013. This project used Wavelet Decomposition and ARMA forecasting to predict wind dynamics over the Netherlands. An additional project applied machine learning algorithms to predict future likelihood of water main breaks in Baltimore. This research examined whether accurate and useful predictive models can be used in lieu of deterministic model data when predicting water main failures.

Through his education and experience, Mr. Beekman is an advanced programmer using Visual Basic for Applications, primarily within Microsoft Excel and Microsoft Access. Mr. Beekman is also proficient in Python, R, SQL, HTML, JavaScript, and GIS. His analytical techniques include multi-objective decision analysis, data mining, machine learning algorithms, Bayesian Networks, risk and decision analysis, spreadsheet application development, database development, data visualization, and statistical modeling and simulation.


M.S.     Analytics, Georgia Institute of Technology, expected 2021

B.S.      Environmental Engineering, Johns Hopkins University, 2014

Selected Publications and Presentations

Beekman, J.A., Woodaman, R.F.A., Buede, D.M. (2020), A Review of Probabilistic Opinion Pooling Algorithms with Application to Insider Threat Detection. Decision Analysis, vol. 17(1): 39-55. Feb 2020. DOI: https://doi.org/10.1287/deca.2019.0399

Buede, D.M., Liebe, R.M. and Beekman, J. (2019), Innovation in the Spirit of Design Thinking. INCOSE International Symposium, 29: 738-752. doi:10.1002/j.2334-5837.2019.00632.x

Chen, T., Beekman, J., Guikema, S.D., and Shashaani, S., “Statistical Modeling in Absence of System Specific Data: Exploratory Empirical Analysis for Prediction of Water Main Breaks,” Journal of Infrastructure Systems, vol. 25(2): 04019009. Jan. 2019. DOI: 10.1061/(ASCE)IS.1943-555X.0000482.

Beekman, J., R, Maiello, R. Baker, A. Hepler, and R. Liebe, “SafePOM Pricing/Performance Model for Safety and Occupational Health Program,” presentation to Military Operations Research Society Symposium, 2017

Beekman, Jared, “PCA for Wavelet Decomposition and ARMA Forecasting: A Case Study of the Netherlands,” presentation to WINDINSPIRE Wind Energy Intermittency; From Wind Farm Turbulence to Economic Management Workshop, 2013