The University of Illinois at Urbana-Champaign - College of Engineering has awarded $518,434 to Assistant Professor Maxim Raginsky to use to apply Machine Learning techniques to network analysis to try and discover how to make networks more efficient.
From the article
“The overall design objective is to make sure that the network resources are allocated in a smart way, and each user receives only the data they need without significant waste of bandwidth or power,” said Raginsky, a member of Illinois' electrical and computer engineering faculty.
Raginsky uses ecological monitoring as an example. If someone is tracking a rare bird species in a specific habitat and wants to record how many of these birds fly in and out of the area, it would be a waste of resources to continuously stream video if what the person really wants is just the arrivals and departures of the birds. A big part of the problem is learning to detect events of interest and to reliably communicate only the data describing these events.
“So I want to make sure that only the relevant information gets to those who need it, despite the fact that everyone is using the same network and the kinds of information that are relevant to one user are different than the kinds of information that are relevant to somebody else,” Raginsky said.
These problems are messy and complex, and there is no hope to come up with an accurate model for all kinds of data being transmitted and received over networks because of the increasing size and complexity of both the networks and the data, Raginsky said. Machine learning offers a variety of tools for extracting predictively relevant information from observations, but to date most of the research on machine learning has not focused on the network aspect and all the resource constraints that it imposes.
This project will systematically explore what is and is not possible in these types of large networks with multiple learning agents, specifically identifying the effect of bandwidth limitations, losses, delays and lack of central coordination on the performance of statistical learning algorithms, thus helping develop efficient and robust coding/decoding schemes.
The NSF CAREER Award is awarded by the National Science Foundation specifically to “junior faculty members who demonstrate their roles through outstanding research and education,” according to NSF’s website.
Raginsky said that because these awards are for 5-year projects, the proposals take a lot of time and effort. “You propose to research something you’re really passionate about, and presumably you want to work on this topic even if it did not get funded,” Raginsky said. “So, when I heard about my proposal being recommended for funding, of course it was a relief. I will have a good time working on this problem.”
Raginsky is a member of the Decision and Control group at CSL.
I think that this is a wonderful problem domain in which Machine learning can prove useful. Machine learning is a powerful set of technologies, and we have yet to even scratch the surface of what it can do for human kind. This goes to show you that there are other great uses besides targeted advertising systems, though that is where most of the jobs are at the moment.
Do you have ay ideas as to some practical applications of Machine learning that have yet to be tested?
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