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Ph.D. THESIS PROPOSAL
TITLE: From spatio-temporal data to functional weighted networks: methods and
applications in climate science, neuroscience and ecology.
Ilias Fountalis
School of Computer Science
College of Computing
Georgia Institute of Technology
Date: Wednesday, December 3, 2014
Time: 11:00 AM - 1:00 PM
Location: KACB 3100
Committee:
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Prof. Constantine Dovrolis, School of Computer Science, GeorgiaTech
(Advisor)
Prof. Mostafa H. Ammar, School of Computer Science, GeorgiaTech
Prof. Annalisa Bracco, Earth and Atmospheric Sciences Department,
GeorgiaTech
Assistant Prof. Bistra Dilkina, School of Computational Science and
Engineering, GeorgiaTech
Associate Prof. Shella Keilholz, Wallace H. Coulter Department of
Biomedical Engineering, GeorgiaTech and Emory University School of Medicine
Prof. Athanasios Nenes, Earth and Atmospheric Sciences Department,
GeorgiaTech
Abstract:
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There is an abundance of spatio-temporal data today from diverse complex
systems such as the Earth's climate, the human brain, or the mobility
patterns of migratory species. By analyzing such data, scientists are
able to discover the key modules of the corresponding system, and to
investigate their dynamics and inter-dependencies.
Spatio-temporal data are typically embedded in a two- or
three-dimensional grid, and the dynamics of each grid cell are
represented by a time-series. Common computational analysis methods for
such data include standard time series analysis, spatial clustering, and
principal/independent component analysis. These techniques, although
valuable in specific contexts, are not able to directly identify the
latent functional components of the system and how these components
interact with each other. This objective can be met more naturally with
a framework that is based on network analysis.
The emerging field of network analysis incorporates a broad range of
models, metrics and algorithms to study complex nonlinear dynamical
systems; its main premise is that the underlying topology or network
structure of a system has a strong impact on its dynamics and evolution.
We propose a novel network-based analysis framework for the study of
spatio-temporal data. First, we cluster grid-cells into "areas", defined
as spatially coherent regions that are highly homogeneous in terms of
dynamics. The proposed algorithm identifies a parsimonious set of latent
functional components, and it relies on a single parameter that is set
based on a target statistical significance level. In a second step, we
identify edges between areas. The strength of the edge between two areas
is given by the covariance of their cumulative anomaly time series. Each
edge is also characterized by the lag at which the cross-correlation
between the two areas is maximum, in absolute sense.
The proposed framework has been applied successfully in Climate Science
to evaluate state-of-the-art climate models and to assess their
performance. Further, we have investigated future projections of these
models' trajectories under increased greenhouse gas emission scenarios.
We are going to also apply the proposed method on functional MRI data to
construct dynamic functional brain networks. Finally, we will apply the
proposed framework in the context of Ecology, to investigate bird
migration patterns.
