[Colloquium] Bruno Barbarioli Candidacy Exam/Oct 26, 2022

[Megan Woodward]

This is an announcement of Bruno Barbarioli's Candidacy Exam.
===============================================
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, and Stavros Sintos

-------------- next part --------------
A non-text attachment was scrubbed...
Name: proposal_bruno_barbarioli.pdf
Type: application/pdf
Size: 1692404 bytes
Desc: proposal_bruno_barbarioli.pdf
URL: <http://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20221018/32483fbd/attachment-0001.pdf>