Teaching

Throughout my teaching activities, I have found the interaction with students to be highly rewarding and stimulating. As I engage in teaching and advising, I aim to convey my own passion for learning and quest for new knowledge. In addition, I aim to motivate students to develop strong practical skills, that will serve them throughout their careers. I believe that learning and research share several common themes. Both are driven by the passion for knowledge discovery, a desire to explore new frontiers, and to master new skills to become an expert. Specifically, my approach to teaching and learning can be related to STEM themes: develop a sound theoretical foundation; promote critical thinking; bridge theory and practice; develop life long learning and communication skills.

All my classes have a common theme, centered around the principles of Computational Social Science (CSS, and my focus on GIScience), in the sense that we explore social science problems through computational methods. In each class, I provide the theory and background into the topic under investigation along with requiring students to synthesize such information and develop computational models or carry out data analysis. 

Below you will find a selection of courses which I have taught or am currently teaching in the at George Mason University.

In addition to the courses below, I am also available for directed research and reading courses (e.g. CSS 796 and CSS 996) in areas related to my research. In the past these have ranged from using social media to understand international relations, exploring urban growth through the use of cellular automata models, the evolution of land-markets from an agent-based modeling perspective, to that of exploring megacities through the lens of CSS. If you are interested in  carrying out a directed research and reading course with me feel free to pass by my office after having a look at my research and recent publications and we can sketch out a course of action.   In addition to directed readings I am often involved with the  MAIS with a concentration in CSS thesis and research projects, or doctoral dissertations (e.g. CSS 998 and CSS 999). For more information about current and past topics please see my students page.


 Agent-based Modeling of Urban Systems


Description: This course will introduce graduate students in the spatial and computational social sciences to the use of agent-based techniques to model various aspects of urban systems. Emphasis will be placed on the notion that urban systems are constantly changing through time and across geographical scales where activities and features change from the split second decision involving local movements such as people walking, the development of land over months and years, to the migration of peoples over decades. We will cover applications ranging across the spectrum of urban systems, from pedestrian modeling, traffic simulation, and residential dynamics to that to urban growth models of cities and regions.

The course will combine taught classes, literature reviews with hands-on modeling. When code is available we will compile and run models as we review articles based on those models. Students will be expected to read assigned course literature and other relevant material they seem as appropriate to understand the subject matter covered in class in greater detail. In addition students will complete a class project where they develop their own agent-based model in their area of interest based on some aspect of urban systems discussed in the class.

Sample Syllabus



Spatial Agent-based Models of Human-Environment Interactions

 
Description: This course will introduce graduate students in the spatial, environmental, and computational social sciences to the use of agent-based techniques as a means of modeling human-environmental interactions. Emphasis will be placed on spatial processes, the use of spatial identifiers to link socioeconomic and biophysical models, and where possible, links to geographic information and associated technologies. We will cover applications in areas such as agriculture, forestry, biodiversity, habitat degradation, interactions between human populations and nonhuman species, urban models, and civil violence.

The course will combine literature review with some hands-on modeling. When demo versions are available, we will compile and run models as we review articles based on those models. In addition, students will complete a class project where they develop their own models in their areas of interest.

Click on the links to see a selection of course projects from Spring 2016 (click here) and Spring 2015 (click here) classes. 


Land-use Modeling Techniques and Applications


Description: The course surveys literature on spatially disaggregated empirical models of Land-Use Change (LUC). The course will begin with a discussion of factors that are hypothesized to drive land-use change across multiple spatial, institutional, and human scales and a discussion of issues related to Land-Use and Land-Cover Change (LUCC) modeling. The majority of the course will be spent reviewing techniques for land-use modeling, including statistical and regression models, cellular automata, mathematical programming and other optimization methods, agent-based models, and integrated models. We will conclude with a discussion of the strengths, weaknesses, and potential complementarities of the models discussed. The role of geographic information systems (GIS) as a tool for data management, analysis and visualization in land-use modeling will be discussed throughout the course.


Geographical Information Systems and Agent-based Modeling


Description: Designed to introduce students in computational social sciences and the social sciences in general to the principles and concepts of geographical information systems (GIS) and science and how spatial data can be used in the creation of spatially explicit agent-based models. Emphasis is placed on spatial processes, and linking agents to spatially explicit environments. Applications covered include: land use and land use change, urban growth, urban change, segregation, conflict, and humanitarian relief.

Key areas of discussion will be on:
  • The principles and concepts of GIS;
  • Sourcing data for the creation of geospatial agent-based models;
  • Creating geospatial agent-based models utilizing either raster or vector data structures;
  • Visualization of geospatial agent-based models;
  • Model communication;
  • Analyzing model results;
  • Verification and validation of geospatial agent-based models.


Introduction to Computational Social Science


Description: This course is a graduate-level survey of computational approaches to social science research, with emphasis on methods, tools, software frameworks, and complexity theory as these apply to the investigation of social phenomena. For our purposes, "the social sciences" include anthropology, communication, economics and finance, geography, history, linguistics, political science, sociology, and social psychology, informed by developments in psychology, cognitive science, neuroscience, and related branches of behavioral science.

Computational social science (CSS) is at the interdisciplinary frontier in the social sciences. As an introduction to the subject, the course has the following objectives:
  1. To understand the motivation for the use of computational models in social science theory and research, including some historical aspects (Why conduct computational research in the social sciences?).
  2. To learn about the variety of CSS research programs across the social science disciplines, through a survey of social simulation models (What has CSS accomplished thus far?).
  3. To understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity (What is the relation between computational social science.
  4. To provide foundations for more advanced work in subsequent courses or projects for those students who already have or will develop a long-term interest in computational social science.
Click on the links to see a selection of course projects from Fall 2015 (click here), Fall 2014 (click here) and Fall 2016 (click here) classes.

Sample Syllabus

Recently this course has also been adapted for undergraduate teaching (CDS 201).

CDS 201 Sample Syllabus


Building  Virtual  Worlds


Description: This course is a graduate level survey of building virtual worlds for social science research, with emphasis on tools, software frameworks, and applications. building virtual worlds has the following objectives:
  1. To understand the motivation for the use of virtual worlds in social science theory and research, including some historical aspects (Why use virtual worlds in the social sciences?). 
  2. To learn about the variety of research programs utilizing virtual worlds across the social science disciplines, through a survey of virtual world literature (What has been accomplished thus far?).  
  3. To understand the distinct contribution that  virtual worlds can  make to the social sciences (What  is  their research potential?);  
  4. To provide foundations for more advanced work in virtual worlds.
Click here to see some outputs from one of the class projects.

Sample Syllabus

Urban Analytics


Description: This course is a graduate-level introduction to Urban Analytics that focuses on the use of data to study cities. The emphasis of the class is to provide students with a understanding of what methods, tools and theory can be used to monitor, analyze and model cities. As an introduction to urban analytics, the course has the following objectives:
  1. to understand the motivation for the use of data to study cities, including some historical aspects; 
  2. to learn about the variety of Urban Analytics research programs across the several disciplines (urban planning, regional science, public policy, geography, computational social science etc.), through a survey of the literature and case studies. 
  3. to understand the distinct contribution that Urban Analytics can make by providing specific insights about cities at multiple scales. 
  4. to provide the foundations for more advanced work in the area of Urban Analytics.
Click here to view a selection of end of semester projects.

Sample Syllabus


Introduction to NetLogo (1 credit hour)


Description: This one credit hour offering will focus on NetLogo programming for beginners with applications to the social sciences. It  consists of 1 hour/week meetings. It is intended to provide a working knowledge of NetLogo coding techniques for model construction, output visualization, data analysis, and computational experimentation. It will cover material not dealt with in other computational social science (CSS) courses. Advanced students are welcome to take this class in order to gain a working knowledge of NetLogo.

Assisted by CSS Ph.D. students Matt Koehler and Steve Scott.

Sample Syllabus 
 

Introduction to MASON (1 credit hour)


Description: This one credit hour offering will focus on MASON programming for beginners with applications to the social sciences. It will consist of 1 hour/week meetings. It is intended to provide a working knowledge of MASON coding techniques for model construction, output visualization, data analysis, and computational experimentation. It will cover material not dealt with in those courses. Advanced students are welcome to this class in order to gain a working knowledge of MASON.


Assisted by CSS Ph.D. student Steve Scott.

Sample Syllabus

Research Colloquium in CSS (1 credit hour)



Description: This one credit hour offering provides students with the opportunity to listen to presentations in specific research areas in computational social science by Center for Social Complexity associated faculty and professional visitors (link to seminars).


 

Specialized Short Course: GeoSocial Analysis


Description: GeoSocial analysis is a new interdisciplinary frontier in the geographical and social sciences. As an introduction to the subject, this 1 week short course has the following objectives:
  1. to understand the motivation for the use of GeoSocial Analysis in for gaining a greater understanding of the human environment, including some historical aspects (Why conduct GeoSocial Analysis research in the social sciences?); 
  2. to learn about the variety of GeoSocial research applications across the spectrum of human geography, through a survey of the filed (What has been accomplished thus far?); 
  3. to understand the distinct contribution that GeoSocial Analysis can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.
  4. to provide foundations for more advanced work in GeoSocial Analysis
Co-Taught with Arie Croitoru and Anthony Stefanidis.


Specialized Short Course: GIS and ABM for Exploring Human-Environment Interactions 


Description: These 5 lectures will introduce researchers to the use of agent-based techniques to model various aspects of Human-Environment Interactions. Emphasis will be placed on the notion that our ''world is constantly changing through time and across geographical scales where activities and features change from the split second decision involving local movements such as people walking, the development of land over months and years, to the migration of peoples over decades.

We will cover applications ranging across the spectrum of urban systems, from pedestrian modeling, traffic simulation, and residential dynamics to that to urban growth models of cities and regions.

As an introduction to the subject, the course has the following objectives:
  1. to understand the motivation for the use of agent-based models to study Human-Environment interactions;
  2. to learn about the variety of research areas across the social science disciplines that integrate ABM and GIS;
  3. to understand the distinct contribution that ABM can make to understanding Human-Environment interactions;
  4. to provide foundations for more advanced research upon the exploration of ABM and GIS for the study of Human-Environment interactions.
  5.  
     

Specialized Short Course: GIS and Agent-based Modeling 


Description: Understanding and modeling human behavior is not as simple as it sounds. This is because humans do not just make random decisions, but base actions upon their knowledge and abilities. Moreover, one might think that human behavior is rational, but this is not always the case: decisions can also be based on emotions (e.g. happiness, anger, fear). Emotions can influence decision-making by altering our perceptions about the environment and future evaluations. The question therefore is how we model human behavior? Over the last decade, one of the dominant ways of modeling human behavior in its many shapes and forms has been through agent-based modeling (ABM). In this workshop I will provide a general overview of what agents are, why there is a need for agent-based models for studying cities, and how it links to how we believe societies operate through ideas of complexity theory. I will sketch out how geographical information systems (GIS) can be used to create spatially explicit agent-based models, before reviewing a range of applications where agent-based models have been developed using geographical data. The workshop concludes with an overview of challenges modelers face when using agent-based models to study geographical problems with a special emphasis on cities, and identify future avenues of research relating to big data and social network analysis.


Course syllabi and access to supporting material are available upon request to George Mason students. 



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