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This session shows how graphs and machine learning (ML) together can enable new insights that could not be attained previously. Graph models and machine learning techniques are becoming popular for discovering relationships, classifying information, identifying patterns and anomalies in data, and improving understanding of information. By combining ML techniques with relationships in graphs, we can answer questions such as “How did other investigators approach similar cases?” “Does this malware have the same characteristics as prior attacks?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” We explain modern techniques for combining graphs and machine learning, so that signals in your irregular and unstructured data sets are properly captured by graph analysis, before feeding them into the machine learning pipeline.
I am interested in topics concerning distributed machine learning, recommender systems, online privacy, and big data related problems. My research work has led to publications in many premier conferences in the above-mentioned fields.