Events Calendar

Special Seminar

Speaker: Natali Ruchansky, Postdoc - Boston University

Talk Title: Learn to query and query to learn.

Series: AI Seminar

Abstract: Working with data has never been easy: Netflix wishes to make movies recommendations to users, but their data is incomplete. Biologists have large interesting protein-protein interaction networks, but they don't know how to extract useful insight from them.
The above examples can be viewed as dealing with two components: the data and the inference process.
In the case of Netflix the latter is known and algorithms are needed to complete the missing data; I will call this Learning to Query. In the biology example the data is readily available and an algorithm is needed to query the graph for discoveries; I will call this Querying to Learn. In my work I addressed these two converse problems and proposed simple, but effective algorithms to solve them.
In this talk I will first present my work on the Learning to Query problem through the lens of matrix completion. I will discuss the new problem of Active Matrix Completion which asks to first analyzes the quality of the available data, such as movie ratings on Netflix, then perform the completion and inference, or movie recommendation. I will then present a new algorithm called Order&Extend that tackles the Active Completion problem. By framing the problem in terms of linear systems, Order&Extend identifies which portions of the data do or do not have enough information, suggests how the data can be augmented, and finally produces a completion.
In the second half of my talk I will present my work on the Querying to Learn through the lens of graph mining. Here there is a data set available, and in particular there are some query-nodes of interest; the biologists have a protein-protein-interaction network and wish to study the interactions between three particular proteins. I will present the new notion of a Wiener-Connector that isolates interesting connections among the query-nodes by utilizing the simple relationship of shortest paths. I will then discuss the algorithm for finding the Wiener-Connector along with its applicability and utility, for example, in identifying possible protein-disease associations and providing outputs that are easy to interpret and visualize, making it useful across different domains.

Biography: I am a PhD student in the Computer Science Department at Boston University.
I am a member of the Data Managment Group, and I work with Professor Evimaria Terzi and Professor Mark Crovella.

While 'passionately curious' about (too) many things, my research focus is algorithmic data mining, mathematics, and networks. In particular I am currently working on problems in graph mining, and in matrix and tensor completion through the lense of linear algebra.

Host: Kristina Lerman

Webcast: http://webcasterms1.isi.edu/mediasite/SilverlightPlayer/Default.aspx?peid=0ac4e800c18744fcbac14781671b6d481d

Location: Information Science Institute (ISI) - 11th floor Large CR

WebCast Link: http://webcasterms1.isi.edu/mediasite/SilverlightPlayer/Default.aspx?peid=0ac4e800c18744fcbac14781671b6d481d

Audiences: Everyone Is Invited

Contact: Alma Nava / Information Sciences Institute