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Seminar: How to Explain the Decisions of Machine Learning Models?

Photo of Dr. Sheeraz Ahmad

As machine learning (ML) models continue to find applications in critical domains, there is an increasing need for their accountability. In this talk, I will motivate the need for explainability further, and go over two major frameworks for explaining the decisions of ML models. I will also present some applications as well as anecdotes where explainability techniques helped generate unique insights.
Oct 29, 2021

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Seattle University Computer Science Team Places 1st at the 2021 IASA Student IT Architecture Competition

Photo of the SITAC 2021 Winners

Seattle University computer science students Ana Carolina De Souza Mendes and Carrie Schaden won 1st place for their Nudge Bud app at the 2021 IASA Student IT Architecture Competition (SITAC).
May 26, 2021

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Seminar: A Big Data Story

Photo of John Dietz

A narrative about how Big Data analysis has changed over the last 15 years, with personal anecdotes and lessons learned, including a brief look at how Google views Big Data and how to think about data in the future.
May 3, 2021

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Seminar: The Most Frequent Connected Induced Subgraph

Photo of Dr. Srinibas Swain

The frequency of an unlabeled graph H in a graph G is the number of induced subgraphs of G that are isomorphic to H. The graph H is a most frequent connected induced subgraph (MFCIS) of G if the frequency of H in G is maximum among all unlabeled graphs occurring as induced subgraphs of G. We introduce the MFCIS problem and discuss some results on it including its complexity. We also determine MFCIS of some special classes of graphs.
Apr 1, 2021

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Seminar: Exploiting Geospatial Properties for Efficient Visual Data Management and Learning

Photo of Seon Ho Kim

Due to the availability of smart mobile cameras, we are experiencing unprecedented growth in the amount of visual content that is being collected. Mobile cameras include a plethora of built-in sensors such as global position system (GPS) receiver and digital compass, recording geospatial properties (e.g., location and viewing direction of camera) during capture time. This facilitates the modeling of mobile visual content through its geo-spatial properties at a fine granular level and provides an essential metadata to manage a large amount of geo-tagged visual data.
Feb 4, 2021

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