My research in large-scale data mining and machine learning (ML) focuses on principled, interpretable, and scalable methods for discovering and summarizing the unknown unknowns in the world's data by leveraging the inherent connections within them. These connections are naturally modeled in networks or graphs, which in turn span every facet of our lives: email communication networks, knowledge graphs for web search, social networks, coauthorship graphs, brain networks, artificial neural networks, and more. My work harnesses the massive scale, heterogeneity, and complexity of these data by providing concise and interpretable network summaries as a way to: (a) speed up follow-up analysis and methods that only need to apply on smaller, representative data; (b) gain understanding into the underlying processes, and inform our decisions by removing the burden of manually sifting through mountains of data; and (c) provide insights into scientific data, generate new hypotheses, and lead to novel scientific discoveries.

Research Interests: data science, large-scale graph mining, data mining, graph neural networks, network representation learning, network neuroscience, summarization, network similarity, network alignment, mining time-evolving and streaming data, graph anomaly and event detection, applied machine learning