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Marshall Minds Ep. 5: “Distance Profiles: A Model-Free Approach to Complex Data Analysis.”

Marshall Minds Ep. 5: “Distance Profiles: A Model-Free Approach to Complex Data Analysis.”

Assistant Professor Paromita Dubey expands the arsenal of methodology for analyzing complex object data by developing an easy-to-compute, theory-backed practical toolkit that goes beyond traditional data analysis techniques.

01.29.25
Pathbreaking Research

Marshall Minds Ep. 5: “Distance Profiles: A Model-Free Approach to Complex Data Analysis.”

As big data analytics strive to tackle the most pressing modern data challenges, Paromita Dubey, assistant professor of data sciences and operations, has introduced “distance profiles” — the collection of distance distributions around data points — to analyze non-Euclidean data such as images, networks, and curves in a model-free, geometry-aware way. By focusing on the distances between data points in abstract spaces rather than the data itself, Dubey’s innovation addresses the difficulty of analyzing non-Euclidean data that often defy standard mathematical operations. From ranking the data points and detecting changes in complex data generation processes to comparing populations and identifying relationships, distance profiles enable a wide range of statistical tasks where traditional methods fall short.

Published in The Annals of Statistics (Vol. 52, No. 2, 2024), Dubey’s research, “Metric Statistics: Exploration and Inference for Random Objects with Distance Profiles,” is supported by the National Science Foundation (NSF) grant DMS-2311034.

Marshall Minds is a faculty research series offering audiences a glimpse into complex issues made simple.