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<div class="" style="word-wrap:break-word"><span class="" style="font-size:14.666666984558105px">This is an announcement of Bruno Barbarioli's Candidacy Exam.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">===============================================</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Candidate: Bruno Barbarioli</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Date: Wednesday, October 26, 2022</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Time: 3:30 pm CST</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Location: JCL 390</span><br class="" style="font-size:14.666666984558105px">
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<span class="" style="font-size:14.666666984558105px">Title: Hierarchical Residual Encoding for Multiresolution Spatial-Temporal Data Compression</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">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.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">One challenge in lossy compression is that different applications may have different accuracy demands.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">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.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">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.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">There are a number of benefits over current state-of-the-art techniques.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">(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.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">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.</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Advisors: Sanjay Krishnan</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Committee Members: Sanjay Krishnan, Aaron Elmore, Raul Castro Fernandez, and Stavros Sintos</span>
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