Postdoctoral Research Associate
Semesters assisted: Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009
Course Description: Basic concepts of discrete-event simulation modeling/analysis. Event-scheduling versus Process-interaction approach. Random number and random variate generation; inverse transformation and other selected techniques. Input data analysis and goodness of fit tests. Specific computer simulation languages. Analysis of simulation output and model validation.
Responsibilities: Conducting lectures on ARENA simulation software, problem sessions, homework grading
Semesters assisted: Fall 2008, Fall 2009
Course Description: The objective is to teach the problems, principles and practice of decision experimentation as a simulation based method to analyze the factors affecting human performance in dynamic decision making (DDM) environments. Concepts of dynamic complexity and influence factors in DDM studies are covered. Tools for systems thinking and simulation, causal loop diagramming, stocks, flows and their dynamics are introduced. Students learn how to develop singe and multiuser (network) games of dynamic simulation models. They also learn to identify appropriate game characteristics (task structures), create experimental design, run controlled experiments and analyze and interpret experimental results. Throughout the course, in the computer lab, students become participants of several experiments with simulation games related to the topics of climate change, renewable resource management, environmental pollution, wildlife management, and alike. Applications focus on environmental problems but potentially comprise many other topics related to business management and economics. Students are expected to develop skills in systems thinking, simulation, gaming and decision experimentation.
Responsibilities: Conducting lab experiments, analyzing lab results, preparing and grading homeworks
Semesters assisted: Spring 2012Course Description: This course is designed to provide students with the tools and knowledge necessary to conduct a simulation supported analysis of socio-‐technical problems using agent-‐ based models (ABMs). Students will gain understanding and awareness of the fundamental differences of agent-‐based modeling from other simulation modeling approaches, and nature of problems/objectives that ABMs fit the best. Besides, students will develop competency in building ABMs, analyzing and interpreting results from these models, and communicating a complete simulation supported analysis cycle to peers/clients. Example models used during the semester will be drawn from social, economic, environmental, industrial, energy and logistic/transportation problems. For the term project, students will go through a model supported analysis process as they develop an ABM in order to analyze a problem from their own areas of interest.
Responsibilities: Assisting instructor and projects.
Semester assisted: Fall 2006
Course Description: Random variables and stochastic processes: Generating functions, Bernouilli and Branching processes, Poisson processes and applications in traffic models. Renewal and regenerative processes and applications in inventory control and reliability models. Markov chains and Markov processes with applications in queueing models. Introduction to Brownian motion with financial applications.
Responsibilities: Preparing and grading quizzes
Semesters assisted: Fall 2004, Fall 2005
Course Description: Simulation methodology, model formulation, systems dynamics, overview of simulation languages, generating random varieties, output data analysis, model validation, variance reduction techniques, experimental design and optimization.
Responsibilities: Conducting lectures on ARENA simulation software, problem sessions
Semester assisted: Spring 2011
Course Description: Conceptual foundations of systems theory. Analysis of linear continuous systems; stability, controllability, and observability; applications to physical, ecological, and socio-economic systems; feedback control systems; introduction to optimal control.
Responsibilities: Homework grading
Semesters assisted: Spring 2010, Fall 2010, Fall 2011
Course Description: Use of systems thinking and system dynamics modeling methodology in the analysis of complex, dynamic socio-economic and managerial problems. Lab experiments with simulation models of real case studies ranging from ecological to business issues, from social to agricultural problems. Basic methods and tools of dynamic feedback modeling: stock-flow and causal loop diagrams, linear and non-linear equation formulation and generic structures. Use of a modern modeling/simulation software such as STELLA, VENSIM, POWERSIM. Student term projects involving applied dynamic modeling.
Responsibilities: Grading homeworks, asisting lab sessions
Responsibilities: Assisting lab administration
Responsibilities: Administering active-directory-based Windows domain, DNS, WINS, department wireless network