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Matthew Otey

Matthew Otey

Matthew Eric Otey received his B.S. degree from the University of Virginia in 2001 and his Ph.D. degree from The Ohio State University in 2006, both in computer science. From 2006 to 2007 he worked for Mission Critical Technologies, Inc. at the NASA Ames Research Center where he worked on anomaly detection for aviation safety applications. He is currently a software engineer at Google, Inc. in Pittsburgh PA, where he is working on using machine learning to improve ads quality by detecting bad and dangerous ads and advertisers.
Authored Publications
Google Publications
Other Publications
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    Detecting Adversarial Advertisements in the Wild
    Michael Pohl
    Bridget Spitznagel
    John Hainsworth
    Yunkai Zhou
    Proceedings of the 17th ACM SIGKDD International Conference on Data Mining and Knowledge Discovery, KDD (2011)
    Preview abstract In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful advertisements. In this paper, we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because both false positives and false negatives have high cost, our deployed system uses a tiered strategy combining automated and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge, and independently assessing the effectiveness of our system. View details
    History Dependent Domain Adaptation
    Allen Lavoie
    Nathan Ratliff
    Domain Adaptation Workshop at NIPS '11 (2011)
    Preview abstract We study a novel variant of the domain adaptation problem, in which the loss function on test data changes due to dependencies on prior predictions. One important instance of this problem area occurs in settings where it is more costly to make a new error than to repeat a previous error. We propose several methods for learning effectively in this setting, and test them empirically on the real-world tasks of malicious URL classification and adversarial advertisement detection. View details
    Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety
    Suratna Budalakoti
    Ashok N. Srivastava
    IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 39 (2009), pp. 101-113