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Wesley J. Marrero

Assistant Professor of Engineering

Overview

Professor Marrero's research interest lies at the intersection of operations research and statistics, with an emphasis on stochastic simulation and optimization to support decision making in practice. His current work addresses various application areas, including substance use disorder, cardiovascular disease, and organ transplantation. Through this research, Marrero has ongoing collaborations with the Massachusetts General Hospital, the University of Michigan Medical School, the University of Michigan School of Public Health, and the US Department of Veterans Affairs. Before joining Dartmouth, he was a postdoctoral research fellow at the MGH Institute for Technology Assessment and Harvard Medical School.

Research Interests

Machine learning; inferential statistics; stochastic simulation; sequential decision making; health policy; medical decision making

Education

  • BS, Industrial and Management Engineering, University of Turabo 2014
  • MS, Industrial and Operations Engineering, University of Michigan 2017
  • MA, Statistics, University of Michigan 2021
  • PhD, Industrial and Operations Engineering, University of Michigan 2021

Awards

  • Institute for Operations Research and the Management Sciences Annual Meeting Minority Issues Forum Poster Competition Best Poster Award, 2020
  • Institute for Operations Research and the Management Sciences Judith Liebman Award, 2020
  • National Science Foundation Graduate Research Fellowships Program Award, 2017
  • Michigan Student Symposium for Interdisciplinary Statistical Sciences American Statistical Association Sponsored Best Poster Award, 2017
  • Rackham Merit Fellowship Award, 2015

Professional Activities

  • Member, Institute for Operations Research and the Management Sciences
  • Member, Society for Medical Decision Making
  • Member, Institute of Industrial and Systems Engineers

Selected Publications

  • Marrero, W. J., Lavieri, M. S., Guikema, S. D., Hutton, D. W., & Parikh, N. D. (2021). A Machine Learning Approach for the Prediction of Overall Deceased Donor Organ Yield. Surgery. http://doi.org/10.1016/j.surg.2021.06.004.
  • Marrero, W.J., Lavieri, M.S., & Sussman, J.B. (2021) Optimal Cholesterol Treatment Plans and Genetic Testing Strategies for Cardiovascular Diseases. Health Care Management Science. http://doi.org/10.1007/s10729-020-09537-x.
  • DeRoos, L.J., Zhou, Y., Marrero, W.J., Tapper, E.B., Sonnenday, C.J., Lavieri, M.S., Hutton, D.W., Parikh, N.D. (2020). National Organ Donation Rates and Organ Procurement Organization Metrics. JAMA Surgery, 48109:1-8. http://doi.org/doi:10.1001/jamasurg.2020.5395.
  • Marrero, W.J., Lavieri, M.S., & Sussman, J.B. A Simulation Model to Evaluate the Implications of Genetic Testing in Cholesterol Treatment Plans. Proceedings of the 2019 Winter Simulation Conference, N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, and Y.-J. Son, eds. http://doi.org/10.1109/WSC40007.2019.9004735.
  • DeRoos, L.J., Marrero, W.J., Tapper, E.B., Sonnenday, C.J., Lavieri, M.S., Hutton, D.W., & Parikh, N.D. (2019). Estimated Association Between Organ Availability and Presumed Consent in Solid Organ Transplant. JAMA Network Open, 2(10):e1912431. http://doi.org/10.1001/jamanetworkopen.2019.12431.
  • Marrero, W. J., Lavieri, M. S., Guikema, S. D., Hutton, D. W., & Parikh, N. D. (2018). Development of a Predictive Model for Deceased Donor Organ Yield. (2018). Transplantation, 102(8), e364. http://doi.org/10.1097/TP.0000000000002274.
  • Parikh, N.D., Marrero, W.J., Wang, J., Steuer, J., Tapper, E.B., Konerman, M., Singal, A.G., Hutton, D.W., Byon, E., & Lavieri, M.S. (2017). Projected Increase in Obesity and Non-Alcoholic Steatohepatitis-Related Liver Transplantation Waitlist Additions in the United States. Hepatology, (5), 1–36. http://doi.org/10.1002/hep.29473.
  • Parikh, N.D., Marrero, W.J., Sonnenday, C.J., Lok, A.S., Hutton, D.W., & Lavieri, M.S. (2017). Population-Based Analysis and Projections of Liver Supply Under Redistricting. Transplantation, 101(9), 2048–2055. http://doi.org/10.1097/TP.0000000000001785.
  • Schell, G.J., Marrero, W.J., Lavieri, M.S., Sussman, J.B., & Hayward, R.A. (2016). Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning. MDM Policy & Practice, 1(1). http://doi.org/10.1177/2381468316674214.
  • Marrero, W.J., Naik, A.S., Friedewald, J.J., Xu, Y., Hutton, D.W., Lavieri, M.S., & Parikh, N.D. (2016). Predictors of Deceased Donor Kidney Discard in the United States. Transplantation, 0(0), 1–8. http://doi.org/10.1097/TP.0000000000001238.
  • Parikh, N., Hutton, D., Marrero, W.J., Sanghani, K., Xu, Y., & Lavieri, M.S. (2015). Projections in Donor Organs Available for Liver Transplantation in the United States: 2014–2025. Liver Transplantation, 21(3), 855–863. http://doi.org/10.1002/lt.

Courses

  • ENGS 177: Decision-Making under Uncertainty
  • ENGS 93: Statistical Methods in Engineering
  • ENGG 193: Statistical Methods in Engineering
  • ENGG 177: Decision-Making under Uncertainty

Research Quick Takes

Prof Marrero accepts the award

Oct 31, 2024

INFORMS MIF Best Paper Award

Professor Wesley Marrero received the INFORMS MIF Best Paper Award as co-author of, "Interpretable Policies and the Price of Interpretability in Hypertension Treatment Planning" published in Manufacturing & Service Operations Management. The paper uses optimization to design clinically intuitive hypertension treatment protocols that greatly outperform clinical guidelines.

Professor Wesley Marrero headshot

Sep 07, 2023

Wellness in Med School

Professor Wesley Marrero will co-lead a project funded by the American Medical Association's ChangeMedEd Innovation Grant Program, which adopts the holistic "Precision Well-Being" approach to create personalized wellness solutions for optimal learning and wellness in medical school. The team includes Thayer PhD student Zequn Chen, Geisel professors Thomas Thesen and Matthew Duncan, and LeChauncy Woodard at the University of Houston College of Medicine.

NSF logo

Jun 22, 2023

NSF Engines Semifinalist

Professors Liz Murnane, Vikrant Vaze, and Wesley Marrero are lead faculty on a team selected as one of 34 semifinalists for the inaugural NSF Regional Innovation Engines competition—spanning nearly all key technology areas and societal and economic challenges highlighted in the "CHIPS and Science Act." A partnership with UMass Lowell, their proposed engine is titled, "Advancing Health Equity Through Digital Technologies, Data Infrastructure, and Artificial Intelligence." Each Engine could receive up to $160 million over 10 years.