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Collaborative systems engineering effort analyzing patient flow and operational bottlenecks to reduce Pediatric Emergency Department wait times.
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Multi-company partnership with Catalyst Connection linking Pitt IE capstone teams to regional manufacturers for operational optimization.
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Award-winning capstone project developing production scheduling and cost-optimization models for pharmacy distribution expansion.
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Recognized with the 2019 Outstanding Community Partner Volunteer Award for volunteer retention modeling and visualization.
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Pitt Industrial Engineering capstone team designs mechanical production tooling for a local Hill District catering business.
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Technical talk on solving two-population SIR compartmental models to estimate infection peaks and manage regional healthcare capacity.
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A collaboration with Carnegie Mellon University Heinz College students to develop a rapid earthquake shelter demand forecasting model.
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Feature profile highlighting over 20 years of disaster planning service and how volunteer fieldwork informs enterprise data science.
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Video interview on operations research, healthcare simulation, the technical translation gap, and Red Cross disaster planning.
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Selected as one of 15 Pittsburgh innovators advancing science and technology in 2025.
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Webinar demonstrating how to integrate Generative AI into data science workflows while navigating technical fault lines and common failure modes.
Published in Interfaces, 2012
This article outlines the operational design and implementation of regional disease prevention and screening facilities in Abu Dhabi
Recommended citation: Luangkesorn, K. L., Norman, B. A., Zhuang, Y., Falbo, M., & Sysko, J. (2012). 'Practice Summaries: Designing Disease Prevention and Screening Centers in Abu Dhabi.' Interfaces, 42(4), 406–409.
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Published in Socio-Economic Planning Sciences, 2012
In this work, we built an agent based model of a given urban area to simulate the emergency medical response to a mass casualty incident (MCI) in that area.
Recommended citation: Wang, Y., Luangkesorn, L., & Shuman, L. (2012). 'Modeling Emergency Medical Response to a Mass Casualty Incident using Agent Based Simulation.' Socio-Economic Planning Sciences, 46(4), pp. 281–290.
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Published in Journal of Public Health Management and Practice, 2013
We used an open-source agent-based modeling system, FRED (A Framework for Reconstructing Epidemiological Dynamic), to simulate the spread of an H1N1 epidemic in Alleghany County, Pennsylvania applying best subset selection procedures.
Recommended citation: Luangkesorn, K. L., Ghiasabadi, F., & Chhatwal, J. (2013). 'A Sequential Experimental Design Method to Evaluate a Combination of School Closure and Vaccination Policies to Control an H1N1-Like Pandemic.' Journal of Public Health Management and Practice, 19(Suppl 5), pp. S37–S41.
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Published in Journal of Statistical Computation and Simulation, 2015
We combine expert judgement, expert classification of procedures by complexity category and historical data in a Markov Chain Monte Carlo model and test it against one year of actual surgery cases at a multi-speciality surgical suite.
Recommended citation: Luangkesorn, K. L., & Eren-Doğu, Z. (2015). 'Markov Chain Monte Carlo methods for estimating surgery duration.' Journal of Statistical Computation and Simulation, 86(2).
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Published in International Journal of Production Economics, 2016
The produce-to-stock with production disruptions model is applicable to systems where the decision on production rate is coupled with the setting of the base stock level when production disruptions are possible.
Recommended citation: 'Systems with potential for severe disruptions.' (2016). International Journal of Production Economics, 171, pp. 478–486.
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Published in Reliability Engineering & System Safety, 2019
This paper details the application of machine learning algorithms to evaluate fire hazard model simulations, streamlining probabilistic safety assessment protocols at nuclear power generation facilities.
Recommended citation: Worrell, C., Luangkesorn, K. L., Haight, J., & Congedo, T. (2019). 'Machine Learning of Fire Hazard Model Simulations for use in Probabilistic Safety Assessments at Nuclear Power Plants.' Reliability Engineering & System Safety, 183, 128-142.
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Published in DAAAM International Scientific Book 2021, 2021
In this study, associative digital technologies, machine learning, and simulation are used to process both operational and related economic data from an actual poultry farm.
Recommended citation: Gaku, R., Luangkesorn, L., & Takakuwa, S. (2021). 'Life Cycle Assessment for Long-Term Production Operation.' DAAAM International Scientific Book 2021, Chapter 11, pp. 131–138.
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Published in Health Informatics: An Interprofessional Approach, 3rd ed., 2024
This book chapter provides an overview of foundational data science applications, methodologies, and analytical approaches utilized within modern healthcare systems.
Recommended citation: Luangkesorn, L. (2024). 'Data Science and Analytics in Healthcare.' In L. R. Hardy (Ed.), Health Informatics: An Interprofessional Approach (3rd ed.). Elsevier. ISBN: 978-0-323-71196-8.
Published in OR/MS Today, 2026
In this OR/MS Today article, Louis Luangkesorn discusses the American Red Cross’s ongoing development of doctrine, procedures, and predictive tools for Advance Operational Planning (AOP), which helps anticipate emergency shelter and material resources before complete disaster data is available.
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This talk on the Natural Language Toolkit and Association Rules uses the Mine Safety and Health Administration Accident Injuries Data Set as example data was given at the Pittsburgh Python Meetup on October 22, 2014.
Recommended citation: Luangkesorn, L. (2014). 'Natural Language Toolkit and Assocition Rules', Pittsburgh, PA.
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This talk on Bayesian Methods in Python given at the Pittsburgh Python Meetup on May 27, 2015. It gives examples of Bayesian Markov Chain Monte Carlo (MCMC) methods using the emcee, PyMC, and PyStan.
Recommended citation: Luangkesorn, L. (2015). 'Bayesian Methods in Python', Pittsburgh Python, Pittsburgh, PA.
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‘Our Volunteer Services staff has very little analytical knowledge, and the community partners [Pro Bono Analytics team] were able to break down the information in a way for us to understand,’ said Cheronda Rutherford, senior volunteer coordinator at Houston Methodist Hospital.
Recommended citation: Chung, Rachel, K. L. Luangkesorn, A. Nguyen, (2018), Human Resources Analytics Dashboard for Healthcare Volunteer Retention, Pre Association for Information Systems (AIS) International Conference on Information Systems (Pre-ICIS) Special Interest Group on Decision Support and Analytics (SIGDSA) Symposium, San Francisco, CA, December 12, 2018.
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Panel on leveraging analytics expertise for social good and supporting nonprofit organizations.
Recommended citation: Luangkesorn, L. (2019). 'Pro Bono Analytics – Providing Analytics Support to Nonprofit Organizations.' INFORMS 2019 Annual Conference.
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Operations research modeling of telestroke staffing levels to support emergency department consults.
Recommended citation: Luangkesorn, K. L., & Hackett, C. (2021). 'Evaluating Specialist Staffing for Telestroke Consult Support for Regional Hospital Emergency Departments.' INFORMS Annual Meeting.
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Internal talk on using compartmental models to provide early identification of new variant case surges.
Recommended citation: Luangkesorn, L. (2022). 'Solving the Two Population Sir Model to Provide Early Estimates of Peak and Duration of a Covid-19 Wave.' Highmark Health COVID-19 Journal Club, Pittsburgh, PA.
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Conference presentation at INFORMS 2022 on epidemic trajectory forecasting using two-population SIR modeling.
Recommended citation: Luangkesorn, L. (2022). 'Solving the Two Population Sir Model to Provide Early Estimates of Peak and Duration of a Covid-19 Wave.' INFORMS Annual Meeting, Indianapolis, IN.
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Invited technical seminar on epidemic modeling using two-population SIR formulations for ChristianaCare iREACH.
Recommended citation: Luangkesorn, L. (2023). 'Tech Talk: Solving the two population SIR model to project COVID-19 wave.' ChristianaCare iREACH / Delaware-CTR.
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Guest lecture on data for public good and Red Cross operational modeling at the University of Pittsburgh.
Recommended citation: Luangkesorn, L. (2023). 'The development of Advanced Operational Planning for national disaster response at the American Red Cross.' Guest lecture in IE 1171 Data for the Public Good (Prof. Rahimian), University of Pittsburgh.
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Panel session discussing strategies and pathways for entering the data science and analytics profession.
Recommended citation: Luangkesorn, L. (2023). 'Successfully entering the data profession.' INFORMS 2023 Annual Conference.
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Guest lecture on disaster analytics and decision support tools at Carnegie Mellon University Heinz College.
Recommended citation: Luangkesorn, L. (2024). 'Information during a disaster: The development of advanced operational planning tools and models.' Guest lecture in 94-465 Data Analytics for Decision Making (Prof. Barrios), Carnegie Mellon University.
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Career development panel at the INFORMS Analytics Conference.
Recommended citation: Luangkesorn, L. (2024). 'Rising Analytics Career Panel.' INFORMS Analytics 2024, Los Angeles, CA.
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Working with the American Red Cross and using the FEMA Individuals and Households Program (IHP) valid registrations data set, we explore the relationship between socially vulnerable populations and the damage that occurred to their homes from Hurricane Michael.
Recommended citation: Arnette, A., Zobel, C. W., Whitehead, M., & Luangkesorn, L. (2024). 'Analyzing Social Vulnerability as a Proxy for Damage in a Natural Disaster.' Production and Operations Management Society (POMS) Annual Conference, Minneapolis, MN.
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Panel exploring practical applications and challenges of generative AI in enterprise analytics practice.
Recommended citation: Luangkesorn, L. (2024). 'Panel on Artificial Intelligence and Generative AI in Analytics Practice.' INFORMS 2024 Annual Conference, Seattle, WA.
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This workshop will discuss current findings on the capabilities and limitations of Generative AI will guide participants in assessing domains where participants have knowledge. Finally, participants will use Generative AI to provide summaries of mathematical papers for a diverse set of audiences.
Recommended citation: Luangkesorn, L. (2024). 'Uses and Limitations of Generative AI in the Mathematical Sciences.' Chiang Mai University, Faculty of Science, Department of Mathematics.
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In the past two years Generative AI has had broad impacts in the applications of analytics. In public health, Generative AI can help perform our duties. This includes planning activities, statistical analysis, and public health communications.
Recommended citation: Luangkesorn, L. (2025). 'Uses and Limitations of Generative AI in Public Health', Chulalongkorn University, College of Public Health Sciences
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This talk focuses on common management structures—centralized, decentralized, and matrix organizations—and how they impact the success of analytics projects.
Recommended citation: Luangkesorn, L. (2025). 'Where should analysts live: Organizing analytics within the enterprise.' INFORMS-Pittsburgh & INFORMS Analytics+.
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This talk discusses some experiences using Generative AI as an aid in applied analytics and walks through an example that illustrates working around its weaknesses and taking advantage of its capabilities.
Recommended citation: Luangkesorn, L. (2025). 'Does Generative AI know statistics?' PyCon USA, Pittsburgh, PA.
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Panel on the integration of artificial intelligence and mathematical optimization in modern healthcare systems.
Recommended citation: Luangkesorn, L. (2025). 'Panel on AI and Optimization for Smarter Healthcare Systems.' INFORMS 2025 Annual Conference, Atlanta, GA.
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Panel discussion on mentoring strategies and professional development in analytics.
Recommended citation: Luangkesorn, L. (2025). 'Mentoring in Analytics.' INFORMS-Pittsburgh, Pittsburgh, PA.
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Fireside chat with Heinz College at CMU Healthcare Analytics and AI Management students.
Recommended citation: Luangkesorn, L. (2026). 'Exploring Generative AI in Healthcare and Emergency Response.' Fireside chat at Heinz College of Information Systems and International Affairs, Carnegie Mellon University.
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Demonstration and discussion of generative AI workflows, common pitfalls, and best practices presented for INFORMS.
Recommended citation: Luangkesorn, L. (2026). 'Using Generative AI in analytics: demonstration, pitfalls, and practices.' INFORMS Webinar & INFORMS Analytics+ 2026.
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Guest lecture on disaster response operational planning in Operations Research for Public Policy at Columbia University IEOR.
Recommended citation: Luangkesorn, L. (2026). 'Development and deployment of advance operational planning for disaster response for the American Red Cross.' Guest lecture in Operations Research for Public Policy (Dr. Eric Stratman), Columbia University IEOR.
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This talk focuses on common management structures—centralized, decentralized, and matrix organizations—and how they impact the success of analytics projects.
Recommended citation: Luangkesorn, L. (2026). 'Where should the analysts live: Organizing analytics within the enterprise.' Guest Lecture in Healthcare Analytics (Dr. Saied Samiedaluie), Alberta School of Business, University of Alberta.
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Doctoral Student Colloquium Industry Career Paths Panel at the INFORMS 2026 Annual Conference.
Recommended citation: Luangkesorn, L. (2026). 'Industry Career Paths Panel.' INFORMS 2026 Annual Conference Doctoral Student Colloquium, San Francisco, CA.
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Panel discussion on the role of AI in the OR/MS workforce at the INFORMS 2026 Annual Conference.
Recommended citation: Luangkesorn, L. (2026). 'Panel: AI in the ORMS Workforce: Threat, Tool, or Career Accelerator?' INFORMS 2026 Annual Conference, San Francisco, CA.