Dr. Christopher J. Lynch is a Research Assistant Professor and Certified Cloud Practitioner at the Virginia Modeling, Analysis and Simulation Center (VMASC). He holds a Ph.D. in Modeling and Simulation (M&S) from Old Dominion University. He leads the Data Analytics Working Group at VMASC and works on developing new methods for analyzing simulation results. He serves as Associate Editor of the ACM Special Interest Group on Simulation and Modeling – M&S Knowledge Repository (ACM SIGSIM MSKR) and is a member of the Society for Modeling and Simulation International (SCS).

VIRGINIA COUNTY COVID-19 DAILY CASE TOTAL FORECASTER PLATFORM

(March 2020 – Present) This project creates daily forecasts of COVID-19 case counts at the county level within the state of Virginia. Additionally, the platform provides links to additional modeling and simulation resources developed to analyze and explore the spread of the virus.

AUTOMATION TOOLS AND ANALYTICS COURSES FOR THE NAVAL SHIPYARD PROJECT EXTENSION

(September 2019 – September 2020) This project extends the objectives of the “Automation Tools and Analytics Courses for The Naval Shipyard” to (i) increase the number of individual sessions conducted per training course and (ii) adds on new train-the-trainer sessions. The purpose of the train-the-trainer sessions is to inform and train personnel from the naval shipyards to be able to teach these courses into the future in order to facilitate longevity in the developed coursework across the shipyards. This work is funded by the Naval Sea Systems Command.

AUTOMATION TOOLS AND ANALYTICS COURSES FOR THE NAVAL SHIPYARDS

(September 2018 – September 2020) In this project, we are automated a series of budgeting, risk assessment, and program adjustment tools for the naval shipyards to assist them in report generation. Additionally, we are developing and conducting a series of six training courses on topics pertaining to the analytics, modeling, and management of data. These training courses aim to educate naval shipyards’ workforce on new methods for analyzing their large volumes of data. This project is funded by the Naval Sea Systems Command.

MODELING RELIGION PROJECT

(July 2015 – June 2018) The Modeling Religion Project (MRP), a subproject under the umbrella of IBCSR’s Simulation Religion Project, attempts to connect the sciences of M&S with the scientific study of religion (SSR). The first goal of MRP is to produce a simulation development platform that allows SSR scholars and students to create complex simulations without programming. The second goal is to produce a series of simulations of the role of religion in key transformations of human civilization. The third goal is to explain the importance of M&S to the academic study of religion. This involves web blogs, outreach efforts, and even a documentary film.

JOURNAL ARTICLES (PEER-REVIEWED)

Lynch, C. J., & Gore, R. J. (2021). Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Georgraphic Levels Using Seven Methods: Comparitive Forecasting Study. J Med Internet Res, 23(3), e24925. DOI: 10.2196/24925.

Lynch C. J., & Gore R.J. (2021). Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods. Data in Brief, 35, 106759. DOI: 10.1016/j.dib.2021.106759.

Lynch, C. J., Diallo, S., Kavak, H., and Padilla, J. (2020). A content analysis-based approach to explore simulation verification and identify its current challenges . PLoS One, 15(5), Z-ZZ. DOI: 10.1371/journal.pone.0232929.

Padilla J. J., Kavak H., Lynch C. J., Gore R.J., & Diallo S. Y. (2018). Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter. PLOS ONE 13(6): e0198857. https://doi.org/10.1371/journal.pone.0198857.

Padilla, J., Diallo, S., Lynch, C. J., and Gore, R. (2018). Observations on the practice and profession of Modeling and Simulation: A survey approach. SIMULATION Transactions for the Society for Modeling and Simulation International, 94(6), 493-506, DOI: 10.1177/0037549717737159.

Gore, R. J., Lynch, C. J., & Kavak, H. (2016). Applying statistical debugging for enhanced trace validation of agent-based models. Simulation: Transactions of the Society for Modeling and Simulation International, 93(4), 273-284, doi: 10.1177/0037549716659707. Special Issue of SIMULATION: Modeling and Simulation in the Era of Big Data and Cloud Computing: Theory, Framework, and Tools.

Diallo, S. Y., Gore, R., Lynch, C. J., & Padilla, J. J. (2016). Formal methods, statistical debugging and exploratory analysis in support of system development: Towards a verification and validation calculator tool. International Journal of Modeling, Simulation, and Scientific Computing, 7(1), 1-22, DOI: 10.1142/S1793962316410014.

CONFERENCE (PEER-REVIEWED)

Leathrum Jr., J. F., Collins, A. J., Cotter, T. S., Lynch, C. J., & Gore, R. (2020). “Education in analytics needed for the Modeling & Simulation process”. In Proceedings of the 2020 Winter Simulation Conference, edited by K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing. Piscataway, NJ: IEEE Press, 3236-3247. DOI: 10.1109/WSC48552.2020.9384122.

Kavak, H., Padilla, J. J., Lynch, C. J., and Diallo, S. Y. “Big Data, Agents, and Machine Learning: Towards a Data-Driven Agent-based Modeling Approach”. In Proceedings of the 2018 Spring Simulation Multi-Conference – Annual Simulation Symposium (ANSS), Baltimore, MD, 15-18 April 2018, 1-12. Vista, CA: SCS. ISBN: 978-1-5108-6014-8.

EDITED BOOK CHAPTER

Lynch, C. J., Kavak, H., Gore, R., and Vernon-Bido, D. (2019). Identifying Unexpected Behaviors in Agent-Based Models through Spatial Plots and Heat Maps. In T. Carmichael, A. Collins, & M. Hadžikadić. Complex Adaptive Systems: Views from the Physical, Natural, and Social Sciences. Series in Understanding Complex Systems (UCS), pp.129-142. Cham, Switzerland AG: Springer. DOI: 10.1007/978-3-030-20309-2. ISBN: 978-3-030-20307-8.

DISSERTATION

Lynch, C. J. (2019). A Lightweight, Feedback-Driven Runtime Verification Methodology (Doctoral Dissertation). Old Dominion University Theses: Modeling and Simulation. College of Engineering and Technology: Norfolk, VA, United States of America, 1-323. ProQuest Number: 22619686.