[Colloquium] Bruno Barbarioli Candidacy Exam/Oct 26, 2022
Megan Woodward
meganwoodward at uchicago.edu
Tue Oct 25 11:21:53 CDT 2022
This is an announcement of Bruno Barbarioli's Candidacy Exam.
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Candidate: Bruno Barbarioli
Date: Wednesday, October 26, 2022
Time: 3:30 pm CST
Location: JCL 390
Title: Hierarchical Residual Encoding for Multiresolution Spatial-Temporal Data Compression
Abstract: Data compression is a key technique for reducing the cost of data transfer from storage to compute nodes. Increasingly, modern data scales necessitate lossy compression techniques, where exactness is sacrificed for a smaller compressed representation.
One challenge in lossy compression is that different applications may have different accuracy demands.
Today's compression techniques struggle in this setting either forcing the user to compress to the strictest accuracy demand, or to re-encode the data at multiple resolutions.
This work proposes a simple, but effective multi-resolution compression algorithm for time series data, where a single encoding can be effectively decompressed at multiple output resolutions.
There are a number of benefits over current state-of-the-art techniques.
(1) The storage footprint of this encoding is smaller than re-encoding the data at multiple resolutions. (2) Similarly, the compression time is generally smaller than re-encoding at multiple resolutions. (3) Finally, the decompression latency with our encoding is significantly faster than single encodings at the strictest accuracy demand.
We further propose an extension to the time series case, where we also take into account the spatial dependence of certain data sets. We generalize our model to a high dimensional setting with applications ranging from image segmentation to overlapping satellite images.
Advisors: Sanjay Krishnan
Committee Members: Sanjay Krishnan, Aaron Elmore, Raul Castro Fernandez, and Stavros Sintos
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