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Dr. Kuhnle’s works generally focuses on the design and analysis of algorithms to solve optimization problems arising from machine learning, network science, and bioinformatics. Particular interests currently include scalable optimization of submodular functions, fast evolutionary algorithms with theoretical guarantees, and succinct data structures for representing genomics data.