Part-Time Lecturer
Education
- B.S. Chemical Engineering (1989), Pontificia Universidade Catolica do Rio Grande do Sul
- M.S. Industrial Engineering (1994), Universidade Federal do Rio Grande do Sul
- Ph.D. Industrial & Systems Engineering (1997), Rutgers - The State University of New Jersey
Expertise
- Health and Humanitarian Systems
- Analytics and Machine Learning
- Data Science and Statistics
About
Flavio S. Fogliatto is a Lecturer in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology and an Adjunct Professor in the Ph.D. Program in Industrial Engineering at the Federal University of Rio Grande do Sul (UFRGS), Brazil, where he previously served as Full Professor, Chair of the Department of Industrial Engineering, and Director of the Graduate Program. His scholarly work has received more than 12,500 citations (Google Scholar, h-index 52), and he has been recognized among the world's top 2% most-cited researchers by Stanford University. He is the author of more than 300 scientific publications and has received numerous national and international honors, including the Rio Grande do Sul Research Foundation Lifetime Achievement Award in Engineering. He is currently serving a second 3-year term on the Industrial Engineering Advisory Committee of Brazil's National Council for Scientific and Technological Development (CNPq), contributing to national research policy and the advancement of industrial engineering.
Research
Dr. Fogliatto develops data-driven and AI-enabled methods to improve the performance, resilience, and sustainability of complex socio-technical systems. His research integrates industrial engineering, operations management, statistics, machine learning, and human-centered design to support decision-making in healthcare, manufacturing, and service organizations. His current work focuses on resilient healthcare operations, digital transformation, and the design and evaluation of human-AI systems, including the optimization of surgical services and the assessment of individual and team resilience through social network analysis. He also conducts research in quality engineering, advanced statistical methods, and predictive analytics. Earlier in his career, he made influential contributions to the field of mass customization, helping shape research on product variety, customization strategies, and customer-driven manufacturing. His research is conducted through collaborations with leading universities in Australia, Canada, Chile, France, Italy, and the United Kingdom.
Teaching
Dr. Fogliatto's teaching is centered on developing the analytical and problem-solving skills that engineers need to make informed, data-driven decisions. His courses emphasize a rigorous understanding of probability, statistics, and quantitative methods while demonstrating how these tools can be applied to address practical challenges in engineering, healthcare, manufacturing, and service systems. He currently teaches undergraduate statistics at Georgia Tech, where he combines strong conceptual foundations with real-world applications to prepare students for evidence-based decision-making. His broader teaching interests include quality engineering, operations management, multivariate statistical methods, optimization, reliability engineering, and production planning and control. Across all courses, he seeks to help students develop not only technical competence but also the engineering judgment needed to select, interpret, and communicate quantitative analyses for solving complex engineering and organizational problems.
Awards and Honors
- Best Doctoral Paper Award, XI Brazilian Meeting on Research and Graduate Studies in Industrial Engineering (ANPEPRO Conference)
- Stanford University Top 2% Most-Cited Scientists (annual global ranking)
- Research in Engineering Lifetime Achievement Award, Rio Grande do Sul Research Foundation (FAPERGS), Brazil
- Best Conference Paper Award, IEEE International Conference on Industrial Engineering and Engineering Management, Singapore
- IIE Transactions Best Paper Award, Institute of Industrial Engineers
Representative Publications
Silva, P.F., Saurin, T.A., Fogliatto, F.S., Dora, J.M., & Rados, D.R.V. (2026). Identifying informal leaders among medical residents as a basis for educational interventions. BMC Medical Education. https://doi.org/10.1186/S12909-026-08918-0
*Calegari, R., Fogliatto, F. S., Lucini, F. R., Brito, J. B. G., Yamashita, G. H., Anzanello, M. J., Tortorella, G. L., & Schaan, B. D. (2025). Designing a long-term master surgical timetable: A case study in balancing post-operative ward bed demand and minimizing changes in surgical schedules. Journal of Scheduling. https://doi.org/10.1007/s10951-025-00851-2
*Melo, I. E. S., & Fogliatto, F. S. (2025). Integration of decision levels in operating room scheduling problems: Systematic review and proposition of a decision support framework. Computers and Operations Research, 175, 107063. https://doi.org/10.1016/j.cor.2025.107063
*Deina, C., Fogliatto, F. S., Silveira, G. J. C., & Anzanello, M. J. (2024). Decision analysis framework for predicting no-shows to appointments using machine learning algorithms. BMC Health Services Research, 24, 37. https://doi.org/10.1186/s12913-023-10418-6
Fogliatto, F. S., Saurin, T. A., Tortorella, G. L., Dora, J. M., & Tonetto, L. M. (2024). Workspace layout for resilient performance using social network analysis: A case study. HERD: Health Environments Research & Design Journal. https://doi.org/10.1177/19375867241271435
Patriarca, R., Simone, F., Artime, O., Saurin, T. A., & Fogliatto, F. S. (2024). On the conceptualization of a functional random walker for the analysis of socio-technical systems. Reliability Engineering & System Safety, 251, 110341. https://doi.org/10.1016/j.ress.2024.110341
*Medeiros, N. B., Fogliatto, F. S., Rocha, M. K., & Tortorella, G. L. (2023). Predicting the length-of-stay of pediatric patients using machine learning algorithms. International Journal of Production Research, 63(2), 483–496. https://doi.org/10.1080/00207543.2023.2235029
*Santos, B. M., Fogliatto, F. S., Saurin, T. A., & Tortorella, G. L. (2023). Modeling help chains in health services as social networks: Moving from linearity to complexity. International Journal of Production Research. https://doi.org/10.1080/00207543.2023.2298486
Terra, S. X., Saurin, T. A., Fogliatto, F. S., & Magalhaes, A. M. M. (2023). Burnout and network centrality as proxies for assessing the human cost of resilient performance. Applied Ergonomics, 108, 103955. https://doi.org/10.1016/j.apergo.2022.103955
*Tortorella, G. L., Fogliatto, F. S., Mendoza, D. T., Pepper, M., & Capurro, D. (2023). Digital transformation of health services: A value stream-oriented approach. International Journal of Production Research, 61(6), 1814–1828. https://doi.org/10.1080/00207543.2022.2048115
*Tortorella, G. L., Prashar, A., Samson, D., Kurnia, S., Fogliatto, F. S., Capurro, D., & Antony, J. (2023). Resilience development and digitalization of the healthcare supply chain: An exploratory study in emerging economies. International Journal of Logistics Management, 34(1), 130–163. https://doi.org/10.1108/IJLM-09-2021-0438
Bertoni, V. B., Saurin, T. A., & Fogliatto, F. S. (2022). How to identify key players that contribute to resilience performance: A social network analysis perspective. Safety Science, 147, 105648. https://doi.org/10.1016/j.ssci.2021.105648
Yamashita, G. H., Anzanello, M. J., Rocha, M. K., Soares, F., & Fogliatto, F. S. (2022). Selecting relevant wavelength intervals for PLS calibration based on absorbance interquartile ranges. Chemometrics and Intelligent Laboratory Systems, 231, 104689. https://doi.org/10.1016/j.chemolab.2022.104689
Yamashita, G. H., Fogliatto, F. S., Anzanello, M. J., & Tortorella, G. L. (2022). Customized prediction of attendance to soccer matches based on symbolic regression and genetic programming. Expert Systems with Applications, 187, 115912. https://doi.org/10.1016/j.eswa.2021.115912
*Denotes co-author who is a current and/or former undergraduate or graduate research student