Location: NDSSL Conference Room 2018, RB XV, Corporate Research Center
Speaker 1: Elizabeth Musser
Title: EpiDash 1.0: A Gastrointestinal Illness (GI) Case Study
Abstract: EpiDash 1.0 renders a complementary tool for local syndrome community surveillance to traditional case based microbiological lab reports and conventional syndrome surveillance by providing automated observation of social media within a community, powered by chattergrabber technology EpiDash collects critical social media data unchallenged by low population density or a high degree of linguistic confounders and synthesizes and visualizes in an user friendly web page that displays salient information in a format supported by evidence based practice to demonstrate and display crucial data both comprehensibly and efficiently for the field epidemiologist. EpiDash Dashboard achieves ongoing systematic collection and analysis and interpretation of disease-related twitter social media data essential to the planning, implementation, and evaluation of public health practice with integrated visualization and mapping for the timely dissemination of digital disease data to those responsible for prevention and control.
Speaker 2: Tania Hamid
Title: Modeling of Graph Dynamical Systems on Parallel and Distributed Frameworks
Abstract: A Graph Dynamical System (GDS) is a theoretical construct that can be used to simulate and analyze the dynamics of a wide spectrum of real world processes that can be modeled as networked systems. These networks can consist of billions of vertices and trillions of edges. One of our goals is to compute the phase space of a system, and for this, even 30-vertex graphs present a computational challenge, since the number of state transitions is exponential in the number of graph vertices. These problems thus produce memory and execution speed challenges. To address this, we aim to design scalable, fault-tolerant and efficient algorithms, on parallel and distributed frameworks, to characterize system state transitions, compute phase space, functional equivalence classes, dynamic equivalence classes and cycle equivalence classes of dynamical systems. Through this implementation, we can evaluate the performance optimization of a GDS on various frameworks and also determine which framework is most suitable for implementing GDS-like systems.