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Applications: Networks Methodologies: Graph Theory and Graph-based Methods, Heterogeneous Data Integration, High-Dimensional Data Analysis, Machine Learning, Network Analysis, Pattern Analysis and Classification, Predictive Modeling, Sparse Data Analysis, Statistical Inference, Statistical Modeling, Statistics Relevant Projects:


Elizaveta Levina

Vijay Nair Collegiate Professor, Statistics

Elizaveta (Liza) Levina and her group work on various questions arising in the statistical analysis of large and complex data, especially networks and graphs. Our current focus is on developing rigorous and computationally efficient statistical inference on realistic models for networks. Current directions include community detection problems in networks (overlapping communities, networks with additional information about the nodes and edges, estimating the number of communities), link prediction (networks with missing or noisy links, networks evolving over time), prediction with data connected by a network (e.g., the role of friendship networks in the spread of risky behaviors among teenagers), and statistical analysis of samples of networks with applications to brain imaging, especially fMRI data from studies of mental health).