Tutorials

Dynamic network analysis enables the analyst to assess change in groups and organizations as they move through time and space on multiple dimensions. By focusing on the network of relations that connect who, what, how, why, where and when we are better able to assess and predict change and identify emergent leaders. This tutorial provides an overview of dynamic network analysis, it's value for identifying emergent leaders, and the capabilities for spatio-temporal reasoning about groups and organizations.

Kathleen M. Carley, Harvard Ph.D., is a faculty member at Carnegie Mellon University, in the School of Computer Science, the department Institute for Software Research. She is also the founder and director of the center for Computational Analysis of Social and Organizational Systems (CASOS). Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and members of her center have developed novel tools and technologies for analyzing largescale geo-centric dynamic-networks and various multi-agent simulation systems. These tools include: ORA, a statistical and graphical toolkit for analyzing and visualizing multi-dimensional networks; AutoMap, a text-mining system for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks; CEMAP, a system for extracting networks from email and blogs; and SORASCS, a service oriented plus architecture for designing and sharing workflows in the human socio-cultural space. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills and weaponized biological attacks; Construct an agent-based dynamic-network based nmodel for assessing network evolution and the diffusion of information and beliefs under diverse socio-demograohic and media environments; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation and international as changes occur within and among red, blue and green forces.

"Crowdsourcing," "human computation," and "collective intelligence" refer to various ways that information and communications technologies are bringing people and computing together to achieve outcomes that were previously beyond our individual capabilities or expectations. Google's search algorithms, Wikipedia's millions of articles, Amazon's recommendations, and open source software's multiple successes are prominent examples of the many ways in which technology and people are being brought together to exhibit new behaviors and outcomes that exceed those previously possible by people or machines in isolation. This tutorial will survey the state of the art and emerging topics in this area. Attendees will acquire knowledge of a wide range of examples in this area, a conceptual framework for relating them to each other, and an appreciation of our growing experience with how such systems can be used in unintended and undesirable ways.

Haym Hirsh is Professor of Computer Science at Rutgers University. His research focuses on crowdsourcing, data mining, human computation, and machine learning, especially targeting question that integrally involve both people and computing. From 2006-2010 he served as Director of the Division of Information and Intelligent Systems at the Nation al Science Foundation, and most recently was a visiting scholar at MIT's Center for Collective Intelligence. Haym received his BS from the Mathematics and Computer Science Departments at UCLA and his MS and PhD from the Computer Science Department at Stanford University.

This tutorial is designed to introduce people with backgrounds in computer science, mathematics, engineering, physics, and related disciplines to fundamental concepts in health-related research, and provide resources and strategies for applying for National Institutes of Health (NIH) grant funding. The tutorial is aimed at health at the behavioral and social levels, rather than at biomedical and biological levels. The tutorial is aimed at systems scientists who have little or no formal background or training in the health-related disciplines, with the goal of helping prepare them to apply their methodological skills in the health domain. The tutorial will lay out the concepts necessary to facilitate the building of health related models, improve their quality, and help build cross-disciplinary relationships (i.e., bridge to health researchers). The tutorial will include: an explanation of the rationale for modeling in health; a description of classification of issues in the public health space (e.g., environmental health, communicable diseases); an appreciation for the diversity of disciplines within the health arena; and a brief discussion of specific topics of interest to the NIH. Also covered in this tutorial: important terminology issues to be aware of when working across domains; an exploration of the time dimension and its impact on health issues; a brief review of types of models and the health problems they are well suited to addressing; and major public health data types and sources that modelers should be familiar with. Presenters will also give an overview of funding agencies that support public health modeling with an emphasis on the component organizations within NIH along with specific funding opportunities at NIH and strategies for preparing grant applications. Some attention will also be paid to issues surrounding tenure and interdisciplinary work, training challenges and opportunities, and resources for learning more.

Learning Objectives:

• Understand why investigators in public health are motivated to use systems science methodologies
• Understand some basic terminology used in the public health field
• Appreciate the diversity of disciplines within health; be able to name the broad domains of health and understand their differences
• Be aware of the pitfalls of working across disciplines and how to address them proactively
• Be aware of the types of data and a few of the large data sets frequently used in health
• Name several major funders of health research and understand how their missions differ
• Be aware of a variety of journals and conferences that welcome systems science health projects
• Obtain some ideas for identifying collaborators in the health field and for developing partnerships
• Be exposed to some existing work featuring systems science methodologies applied to health problems
• Obtain resources and some explanation of the NIH grants process and specific funding opportunities
• Obtain resources on where to learn more about the above topics

Nathaniel Osgood is an Associate Professor in the Department of Computer Science and Associate Faculty in the Department of Community Health & Epidemiology and Division of Bioengineering at the University of Saskatchewan. His research is focused on providing tools to inform understanding of population health trends and health policy tradeoffs. This work includes both application and methodological components. On the application side, Dr. Osgood works closely with cross-disciplinary teams applying simulation modeling, smartphone-based epidemiological sensing platforms, Bayesian inference and mathematical analysis to address urgent public health challenges in both the chronic and infectious disease areas. Dr. Osgood's methodological work seek to advance the science and art of model building and epidemiological data collection for models through improved formalisms, algorithms, ubiquitous sensing frameworks, analysis techniques, and software tools. Dr. Osgood received his PhD in 1999 from MIT's Department of Electrical Engineering and Computer Science. Prior to joining the U of S faculty, he worked for many years in a number of academic and industry positions, including on industry & academic projects applying modeling to tobacco and environmental epidemiology, health informatics, and multi-framework modeling for natural resource policy-making.