David F. Gleich
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Professor Gleich is interested in how we can utilize matrix algebra to express -- and improve -- algorithms in network analysis and data-based simulation analysis. Matrix algebra is a particularly attractive paradigm to study these procedures as it often gives rise to efficient computational procedures in a variety of settings (serial, parallel, streaming). This research straddles a few different areas and often involves working with large datasets on high performance computing architectures (e.g. MPI clusters) and data computing architectures (e.g. MapReduce).


invited talk
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as author at  The 11th Workshop on Mining and Learning with Graphs (MLG) 2013,