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• Information Fusion and Complex Event Detection
• Efficient Computation of Social Network Metrics
• Optimal Resource Allocation for Spacial Analysis
• Causal Inference with Observational Data
• Social Network Analysis of Online Smoking Cessation Communities
• Stochastic Modeling of Hospital Readmission Process
• IE 374: Systems Modeling and Optimization: Operations Research II
• IE 575: Stochastic Methods
• IE 411/511: Social Network Behavior Analysis
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Social Network Analysis of Online Smoking Cessation Communities
•Description
•Personnel
| Description |
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The use of analytical social network models in public health, and tobacco control
in particular, has recent support. Their application in designing, refining, and evaluating
behavioral interventions for smoking cessation is less defined, but the opportunities are compelling.
The goal of this project is to apply the combined advances in mathematical modeling, narrative theory,
computational linguistics, artificial intelligence and optimization under uncertainty to identify,
model and assess the effects of targeted interventions on social network actors, with the application
focus on users in online smoking cessation communities. The modeling component of this research develops a
unifying framework for the analysis of social network actor behavior and intervention outcomes
within a comprehensive conceptual approach, venturing into a largely unexplored research area
of mathematical social optimization, and uses actor-oriented analysis to enhance our understanding
of how individual behaviors lead to the emergence of collective behaviors.
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| Personnel |
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Collaborators: Dr. Cecilia Alm (English, Rochester Institute of Technology),
Dr. Laura Shackelford (English, Rochester Institute of Technology),
Dr. Scott McIntosh (Community & Preventive Medicine, University of Rochester)
Students: TBA
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