The explosion of high-dimensional data brings exciting opportunities alongside serious analytical and computational challenges. At the heart of these challenges lies a fundamental question: how can we extract meaningful structure from complex data? Real-world datasets are rarely clean - they are shaped by noise, outliers, and uneven sampling - and each new application introduces fresh scientific questions.

The projects below span diverse domains, yet they are united by a shared goal: developing efficient ways to compare and understand high-dimensional data. Together, they push the boundaries of data analysis and open new directions for discovery. Dive into the details and explore the ideas driving this work.