Manuel D. Rossetti
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
University Professor
Also affiliated: University of Wisconsin–Madison (2020); University of Pittsburgh (2020); Arkansas Department of Education (2020); Wichita State University (2020); Engineering Systems (United States) (1995); University of Virginia Medical Center (1998); University of Virginia (1995–2002); The Ohio State University (1992)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Manuel D. Rossetti's research centers on simulation modeling and its applications, with a particular focus on developing and enhancing tools for discrete-event simulation. He has authored publications introducing and detailing the Kotlin Simulation Library (KSL), including tutorials and advancements in automated input distribution fitting.
His work explores enabling massively parallel exploration of simulation design spaces within serverless environments. Rossetti has also contributed to the creation of interdisciplinary academic programs, specifically a multi-college B.S. Data Science program with various concentrations.
With a career marked by extensive scholarship, Rossetti holds an h-index of 22 and has published 137 works, accumulating over 1,600 citations. He is recognized as a highly cited researcher and collaborates with colleagues at the University of Arkansas at Fayetteville, including Edward Pohl and Ronna Turner.
Metrics
- h-index: 22
- Publications: 137
- Citations: 1,675
Selected Publications
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A Tutorial on Resource Modeling Using the Kotlin Simulation Library (2025)
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Automated input distribution fitting based on multiple criteria for the Kotlin Simulation Library (2025)
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Input Distribution Modeling Using the Kotlin Simulation Library (2024)
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An Introductory Tutorial for the Kotlin Simulation Library (2024)
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A multicriteria model for assessing item importance and risk using operational data from military supply chains (2024)
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Enabling massively parallel, ad hoc exploration of the design space for simulation models within a serverless environment (2024)
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An Overview of the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program (2024)
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Introducing the Kotlin Simulation Library (KSL) (2023)
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Creating a Multi-College Interdisciplinary B.S. Data Science Program with Concentrations (2021)
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Measuring the Impact of Data Standards in an Internal Hospital Supply System (2021)
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Introducing the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program (2021)
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Creating a Successful Pathway to Graduate Studies: The Student Integrated Intern Research Experience (SIIRE) (2020)
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Decision-Making Framework for Evaluating Physicians’ Preference Items Using Multi-Objective Decision Analysis Principles (2020)
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Performance-based inventory classification methods for large-Scale multi-echelon replenishment systems (2020)
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Effectiveness of GRE Workshops to Increase Awareness (2020)
Collaboration Network
Top Collaborators
- Introducing the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- Measuring the Impact of Data Standards in an Internal Hospital Supply System
- An Overview of the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- Introducing the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- An Overview of the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- Introducing the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- An Overview of the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- Introducing the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- An Overview of the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- Introducing the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- An Overview of the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- Measuring the Impact of Data Standards in an Internal Hospital Supply System
- Measuring the Impact of Data Standards in an Internal Hospital Supply System
- Creating a Multi-College Interdisciplinary B.S. Data Science Program with Concentrations
- An Overview of the Multi-Disciplinary Data Science (MDaS) S-STEM Scholarship Program
- Enabling massively parallel, ad hoc exploration of the design space for simulation models within a serverless environment
- A multicriteria model for assessing item importance and risk using operational data from military supply chains
- A multicriteria model for assessing item importance and risk using operational data from military supply chains
- A multicriteria model for assessing item importance and risk using operational data from military supply chains
- A multicriteria model for assessing item importance and risk using operational data from military supply chains
- Input Distribution Modeling Using the Kotlin Simulation Library
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