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Gian-Gabriel Palaci Garcia

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Assistant Professor
Industrial & Systems Engineering

Pronouns: he/him

Biography

Gian-Gabriel Garcia joined ISE in fall 2025 as an Assistant Professor. Previously, he was an Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech from 2021-2025 where he held the Harold E. Smalley Early Career Professorship from 2024-2025. From 2020-2021, he was a Postdoctoral Research Fellow in the Institute for Technology Assessment at the Massachusetts General Hospital and Harvard Medical School. 

Education

  • PhD, Industrial and Operations Engineering, University of Michigan, 2020
  • MS, Industrial and Operations Engineering, University of Michigan, 2016
  • BS, Industrial Engineering, University of Pittsburgh, 2014

Previous appointments

  • Assistant Professor, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology (2021-2025)
  • Postdoctoral Research Fellow, MGH Institute for Technology Assessment, Harvard Medical School

Research Statement

My research interests are in the design, analysis, and optimization of data-driven frameworks at the intersection of prediction and decision-making as motivated by high-impact problems in Medical Decision-Making and Health Policy. My recent work emphasizes the design of frameworks that are interpretable, fair, and robust so that they are acceptable to key stakeholders at the patient, physician, operational, and policy levels. This research area draws problems which have both practical impact and interesting theoretical challenges.

My research often draws on methodologies from stochastic optimization, dynamic programming, machine learning, stochastic modeling, and simulation. My current projects include applications in a broad range of disease and health areas including concussion, chronic disease management, the opioid crisis, and maternal health.

Select publications

  1. Y. Yang , C.-Y. Liao , E. Keyvanshokooh, H. Shao, M. B. Weber, F. Pasquel, and G.-G. P. Garcia (2025). A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study. JMIR Medical Informatics 13, e66200
  2. H. S. Pandey , B. Lahijanian , J. D. Schmidt, R. C. Lynall, S. P. Broglio, T. W. McAllister, M. A. McCrea, P. F. Pasquina, and G.-G. P. Garcia (2025). Quantifying the Diagnostic Utility of Baseline Testing: An Analysis of the NCAA-DoD CARE Consortium Dataset. American Journal of Sports Medicine 53(1), 181–191
  3. S. J. Lee , X. Gong , and G.-G. P. Garcia (2024). Modified Monotone Policy Iteration for Interpretable Policies in Markov Decision Processes and the Impact of State Ordering Rules. Annals of Operations Research 347 (2), 783-841
  4. S. Ma, A. Dehghanian, G.-G. P. Garcia, and N. Serban (2024). Learning Hidden Markov Models with Structured Dynamics. INFORMS Journal on Computing 37(3), 531-556
  5. G.-G. P. Garcia, L. N. Steimle, W. J. Marrero, and J. B. Sussman (2024). Interpretable Policies and the Price of Interpretability in Hypertension Treatment Planning. Manufacturing & Service Operations Management 26(1), 80–94
  6. L. B. Lempke, A. J. Boltz, G.-G. P. Garcia, R. A. Syrydiuk, H. S. Pandey1 , M. A. McCrea, T. W. McAllister, and S. P. Broglio (2023). Optimizing Baseline and Post-Concussion Assessments Through Factor Structure Analysis: Findings from the NCAA-DoD CARE Consortium. The Clinical Neuropsychologist 38(5), 1156–1174
  7. G.-G. P. Garcia, L. L. Czerniak, M. S. Lavieri, S. W. Liebel, K. Van Pelt, P. F. Pasquina, T. W. McAllister, M. A. McCrea, S. P. Broglio, and CARE Consortium Investigators (2023). Estimating the Relationship Between the Symptom-Free Waiting Period and Injury Rates after Return-to-Play from Concussion: A Simulation Analysis Using CARE Consortium Data. Sports Medicine 53(10), 2513–2528
  8. S. J. Lee , G.-G. P. Garcia, K. K. Stanhope, M. Platner, and S. L. Boulet (2023). Interpretable machine learning to predict adverse perinatal outcomes: Examining marginal predictive value of risk factors throughout pregnancy. American Journal of Obstetrics and Gynecology MFM 5(10), 101096
  9. G.-G. P. Garcia, M. S. Lavieri, T. W. McAllister, M. A. McCrea, S. P. Broglio, and the CARE Consortium Investigators (2023). Reducing the price of Naivete in return-to-play from sports-related concussion. Production and Operations Management 32, 3081–3099
  10. L. L. Czerniak, S. W. Liebel, H. Zhou, G.-G. P. Garcia, M. S. Lavieri, M. A. McCrea, T. W. McAllister, S. P. Broglio, and the CARE Consortium Investigators (2023). Sensitivity and Specificity of the ImPACT Neurocognitive Test in Collegiate Athletes and United States Military Cadets with ADHD and/or LD: Findings from the NCAA-DoD CARE Consortium. Sports Medicine 53, 747–759
  11. G.-G. P. Garcia, E. J. Stringfellow, C. DiGennaro, N. Poellinger, J. Wood, S. Wakeman, and M. S. Jalali (2022). Opioid overdose decedent characteristics during COVID-19. Annals of Medicine 54(1), 1081–1088
  12. C.-Y. Liao , G.-G. P. Garcia, C. DiGennaro, and M. S. Jalali (2022). Racial Disparities in Opioid Overdose Deaths in Massachusetts. JAMA Network Open 5(4), e229081
  13. G.-G. P. Garcia, C. M. Schumb, M. S. Lavieri, H. Koffijberg, T. W. McAllister, M. A. McCrea, and S. P. Broglio (2022). Developing Insights for Possible and Probable Acute Concussions Using Cluster Analysis. Journal of Neurotrauma 39(1-2), 102–113
  14. C. DiGennaro, G.-G. P. Garcia, E. J. Stringfellow, S. Wakeman, and M. S. Jalali (2021). Changes in characteristics of drug overdose death trends during the COVID-19 pandemic. International Journal of Drug Policy 98(Accepted), 103392
  15. G.-G. P. Garcia, J. Yang, M. S. Lavieri, T. W. McAllister, M. A. McCrea, and S. P. Broglio (2020). Optimizing Components of the Sport Concussion Assessment Tool for Acute Concussion Assessment. Neurosurgery 87(5), 971–981
  16. G.-G. P. Garcia, M. S. Lavieri, R. Jiang, M. A. McCrea, T. W. McAllister, and S. P. Broglio (2020). Data-driven stochastic optimization approaches to determine decision thresholds for risk estimation models. IISE Transactions 52(10), 1098–1121
  17. G.-G. P. Garcia, M. S. Lavieri, C. Andrews, X. Liu, M. P. Van Oyen, M. A. Kass, M. O. Gordon, and J. D. Stein (2019). Accuracy of Kalman Filtering in Forecasting Visual Field and Intraocular Pressure Trajectory in Patients With Ocular Hypertension. JAMA Ophthalmology 137(12), 1416–1423
  18. G.-G. P. Garcia, M. S. Lavieri, R. Jiang, T. W. McAllister, M. A. McCrea, and S. P. Broglio (2019). A Data-Driven Approach to Unlikely, Possible, Probable, and Definite Acute Concussion Assessment. Journal of Neurotrauma 36(10), 1571–1583
  19. G. J. Schell, G.-G. P. Garcia, M. S. Lavieri, J. B. Sussman, and R. A. Hayward (2019). Optimal Coinsurance Rates for a Heterogeneous Population under Inequality and Resource Constraints. IISE Transactions 51(1), 74–91
  20. G.-G. P. Garcia, S. P. Broglio, M. S. Lavieri, M. McCrea, and T. McAllister (2018). Quantifying the Value of Multidimensional Assessment Models for Acute Concussion: An Analysis of Data from the NCAA-DoD Care Consortium. Sports Medicine 48(7), 1739–1749

Honors & awards

  • First Prize, INFORMS Minority Issues Forum Paper Competition, 2024
  • Best Paper, IISE Transactions Focus Issue on Operations Engineering & Analytics, 2021
  • First Prize, SMDM Lee B. Lusted Prize in Quantitative Methods & Theoretical Developments, 2020
  • First Prize, INFORMS Minority Issues Forum Paper Competition, 2020
  • First Prize, SMDM Lee B. Lusted Prize in Quantitative Methods & Theoretical Developments, 2018
  • INFORMS Bonder Scholarship for Applied OR in Health Services, 2018
  • NSF Graduate Research Fellowship, 2017-2020