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Currently, I am a member of the Information Visualization team within the Big Data Organization at AT&T Labs Research. Before, I was a Research Associate (~senior postdoc) in Leonidas Guibas Laboratory at Stanford University. I received my PhD in Applied and Computational Mathematics from Princeton University in 2005, after which I spent seven years at Purdue and Drew universities.

My research interests lie in developing effective and efficient algorithms for processing of, on, and between geometric data. Geometry -- understood broadly as shape, proximity, or connectivity -- is a fundamental aspect of much of the data being captured today. For example, shape is central to 3D medical images, computer graphics models, laser scanner/LiDAR point clouds, and GPS trajectories. Proximity naturally emerges in the setting of high-dimensional data clouds such as a collection of handwritten digit images, or a set of customers embedded based on their product ratings. Connectivity is the main feature when studying all kinds of networks from social networks to human brain connectome.

Analyzing data while ignoring geometry is like trying to understand an organ's function without ever looking at its anatomy. Big data and small data -- all come with complicated anatomies. Versatility of application driven requirements, intricate topology, noise, and irregular sampling pose significant challenges, as well as research opportunities in the area of geometric data analysis. Thus, my research addresses a set of core problems -- each epitomizing universal needs across a variety of applications: a) processing and analysis of geometry itself; b) processing of signals/functions defined on geometry; and  c) understanding relationships between geometric datasets in collections, including precise quantification of differences and commonalities between datasets.



E-mail: raifrustamov~at~gmail.com