SYST 468 / OR 568: Applied Predictive Analytics
This course introduces the fundamentals of data analysis and some of the most widely used models in applied predictive analytics. The focus includes data understanding, summarization, exploratory analysis and visualization, linear and non-linear predictive models, logistic regression, classification, and clustering among others. Students will be introduced to a powerful open source statistical programming language (R) and work on applied data analysis projects. While no prior knowledge on R is required, students must be well prepared in programming.
OR 531: Analytics and Decision Analysis
This course introduces the fundamentals of prescriptive analytics and some of the most widely used models in predictive analytics. The focus includes mathematical optimization, sensitivity analysis, networks modeling, stochastic modeling, multi-objective modeling, Monte Carlo simulation, and decision analysis using decision trees among others. Students will be introduced to Analytic Solver Platform, Python programming language, and Gurobi and work on various case studies.
OR 645: Stochastic Processes
Many real-world processes are fundamentally stochastic and uncertain. This course introduces an in-depth survey of models that can be used to analyze a wide variety of stochastic processes. The focus includes the Poisson process and exponential distribution, renewal theory, discrete- and continuous-time Markov Chains, Queuing theory, and Markovian Decision Processes. Both theoretical analysis and applications of stochastic processes will be presented. This course assumes some prior knowledge of probability and basic stochastic models (like Markov chains).
OR 635: Discrete System Simulation
Simulation is a powerful tool to analyze complex, dynamic and stochastic systems. This course introduces the fundamentals of discrete-event simulation in theory and practice. The focus includes stochastic modeling of discrete-event systems, input modeling, random number generation, statistical analysis of simulation outputs, techniques to improve the efficiency and accuracy of simulation results, and case studies. Students will be introduced to simulation packages such as Arena and AnyLogic. Students have the opportunity to develop simulation models using a general programming language (Java, Python, etc.). Other types of simulation including Monte Carlo, agent-based, and system dynamics will be briefly introduced.
- Associate Editor, International Journal of Applied Logistics, IGI-Global
- Guest Co-editor, Special Collection on “Integrative Analysis and Modeling of Interdependent Systems“, ASCE Journal of Environmental Engineering.
- Board Director, Modeling and Simulation (M&S) Division, Institute of Industrial and Systems Engineers (IISE)
- Track Co-chair, Environment, Sustainability, and Resilience, 2023 Winter Simulation Conference
- Program Co-chair, Annual ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities
- Track Program Committee, Reliability Modeling and Simulation, 2022 Winter Simulation Conference
- Faculty Mentor, Introduce a Girl to ISE, 2021 IISE Annual Meeting
- Faculty Mentor, WORMS (Women in ORMS) Mentorship Program, 2021 INFORMS Annual Meeting
- Track Program Committee, Data Science for Simulation, 2021 Winter Simulation Conference
- Track Lead, Shelter, Heating/Cooling, Lighting, Converging Approaches to Sustainable Resilience, C-RASC and STAR-TIDES, George Mason University, 2020
- Faculty Mentor for Women in Engineering, University of Oklahoma (designed a hands-on session on how ISE and ORMS can contribute to tackling grand engineering challenges), 2018-2019