University of Southern California

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Kristina Lerman
 Social Dynamics and Networks  
  As people interact on the social Web, their activity affects the structure of the Web itself, with complex feedback between individual and collective decisions producing qualitatively new online behaviors. We are developing a mathematical framework that both will model collective behavior of social web users and will use these models to predict trends. For example, the framework may help forecast which news stories on Digg or Twitter will become popular.  
 Social Media Analysis | Modeling Social Dynamics | Network Sturucture & Dynamics 
 Learning from Social Metadata  
  Many social Web sites allow users to create content and annotate it with descriptive metadata, such as tags, and to organize content within personal hierarchies. Structured social metadata offers invaluable evidence for learning how a community organizes knowledge. But such metadata also tends to be sparse, shallow, ambiguous, noisy and inconsistent. We are developing machine learning methods to aggregate social metadata to improve information discovery and learn common taxonomies (folksonomies).  
 Geospatial Social Networks | Harvesting Folksonomies 
 Semantic Modeling of Information Sources  
  My research as a member of the Information Integration group deals with automatically recognizing semantics of data types used by various information sources. We use machine learning methods to represent and learn the structure of data extracted from information sources, and apply the learned representations to recognize new instances of the same data.  
 Source Modeling | Wrapper Maintenance 
 Mathematical Modeling of Multi-Agent Systems  
  Mathematical analysis is a crucial tool for understanding, predicting and controlling emergent behavior of autonomous agent systems, whether human or robot. We have developed a stochastic processes-based framework for mathematical analysis of multi-agent systems, and have successfully compared analytic predictions with empirical results in several robotics applications. These include foraging, collaboration and dynamic task allocation.  
  In addition, we have developed a methodology for designing multi-robot systems that uses machine learning to automatically synthesize robotsŐ behavior rules. In another project with collaborators at USC and Brandeis University, we are modeling collective behavior of museum visitors and validating models experimentally by recording actual museum visitors.  
 Robot Swarms | TASK | Human Crowds 
 Pattern Formation in Spatially Extended Systems  
  My graduate research on pattern formation in spatially extended systems sparked my fascination with emergent behavior in complex adaptive systems, which continues to inspire my work.  
 Binary Fluid Convection