The FIND Seminar is a bi-weekly seminar series that hosts cutting-edge research talks on topics related to the broad themes of Foundations of Information, Networks and Decision Systems. Talks are about 50 minutes long with time for questions and discussion.
Location: Phillips Hall 233 and Zoom
Time: 4:10PM ET, bi-weekly on (alternating) Thursdays
Delivery format: All talks will have a live audience in Phillips Hall 233. Until circumstances allow otherwise, external speakers will give the talk remotely via Zoom (broadcasted in PH233). Remote audience is also welcome, but in-person participation is encouraged.
Mailing list: To subscribe to the FIND seminar mailing list, email firstname.lastname@example.org, with “join” in the subject line and a blank email body. All talks info and reminders will be sent via the mailing list.
Title: A User Guide to Low-Pass Graph Signal Processing and its Applications
Speaker: Anna Scaglione
Date and Time: 10/21/2021 4:10PM ET
Location: Phillips 233 and Zoom (link)
Abstract: The notion of graph filters can be used to define generative models for graph data. In fact, the data obtained from many examples of network dynamics may be viewed as the output of a graph filter. With this interpretation, classical signal processing tools such as frequency analysis have been successfully applied with analogous interpretation to graph data, generating new insights for data science. What follows is a user guide on a specific class of graph data, where the generating graph filters are low-pass, i.e., the filter attenuates contents in the higher graph frequencies while retaining contents in the lower frequencies. Our choice is motivated by the prevalence of low-pass models in application domains such as social networks, financial markets, and power systems. We illustrate how to leverage properties of low-pass graph filters to learn the graph topology or identify its community structure; efficiently represent graph data through sampling, recover missing measurements, and de-noise graph data; the low-pass property is also used as the baseline to detect anomalies.
Schedule for Fall 2021:
|9/9/2021||Alon Orlisky||Robust Learning from Batches: The Best Things in life are (Almost) Free|
|9/23/2021||Qing Zhao||Random Walk on a Tree for Stochastic Search and Optimization|
|10/7/2021||Mary Wootters||Low-bandwidth recovery of linear functions of Reed-Solomon-encoded data|
|10/21/2021||Anna Scaglione||A User Guide to Low-Pass Graph Signal Processing and its Applications|
|11/04/2021||Steven H. Low||TBD|