|
Yanna Shen |
|
I am a Ph. D. student in the Intelligent Systems Program at the University of Pittsburgh, advised by Dr. Gregory Cooper. I am working as a Graduate Student Researcher at RODS Laboratory in the Department of Biomedical Informatics. My research interests include machine learning, graphical models, Bayesian statistics, and the investigation of these methods in addressing real-world problem. I am currently involved in an NSF-funded project that investigates Bayesian biosurveillance. |
|
Suite M-183 VALE, 200 Meyran Ave University of Pittsburgh Pittsburgh, PA 15260
Phone: (412)648-6710 E-mail: shenyn@cs.pitt.edu |
|
Detecting anomalous events in data has important applications in domains such as disease outbreak detection, fraud detection, and intrusion detection. In a typical scenario, a monitoring system examines a sequence of data to determine if any recent activity can be considered as deviation from baseline behavior. Many anomaly-detection algorithms, such as cumulative sum method, use frequentist statistical techniques. I am currently doing Bayesian modeling for anomaly detection. ►The goals of automated disease-outbreak detection systems are to detect disease outbreaks early, while exhibiting few false positives. ☆ I developed a general Bayesian univariate anomaly-detection algorithm that runs in linear time. I intend to develop a multivariate version of this algorithm for monitoring multiple features of some event. ☆ I developed an efficient spatial Bayesian outbreak-detection algorithm that performs complete Bayesian model averaging over all possible spatial hypotheses, which we call SBMA. I intend to develop a multivariate version of SBMA and apply SBMA to a wide variety of disease-outbreak scenarios. ►The objective when evaluating a disease-outbreak detection algorithm is to measure its accuracy (sensitivity and false alarm rate) and time to detection. However, little research has been done on estimating how well automated disease-outbreak detection systems augment traditional outbreak detection that is carried out by clinicians. It would be best to develop algorithms that are augmentative of, rather than redundant with, clinician detection. ☆ I developed a general mathematical framework for evaluating joint clinician-machine detection of anomalies. |
|
Shen, Y. and G.F. Cooper, Estimating disease outbreak detection time when a detection algorithm is augmenting traditional clinician surveillance. To appear in the Journal of Biomedical Informatics. [PDF] Shen, Y., W.-K. Wong, J. Levander and G.F. Cooper, An outbreak detection algorithm that efficiently performs complete Bayesian model averaging over all possible spatial distributions of disease. Advances in Disease Surveillance 2007; 4:113. [PDF] Shen, Y. and G.F. Cooper, A Bayesian biosurveillance method that models unknown outbreak diseases. In: Proceedings of Intelligence and Security Informatics: BioSurveillance 2007: 209-215. [PDF] Shen, Y., W.-K. Wong, and G.F. Cooper, Estimating the expected warning time of outbreak-detection algorithms. Advances in Disease Surveillance 2006; 1:65. [PDF] Lu, X., Q. Li, Z. Huang, Y. Shen and T. Yao, Towards Chinese-English sentence alignment based on statistical method. Journal of MINI-MICRO Systems. 2004, Vol. 25, No. 6, 990-992. Zhang, L., X. Lu, Y. Shen and T. Yao, A statistical approach to extract Chinese chunk candidates from large corpora. In: Proceedings of International Conference on Computer Processing on Oriental Languages, 2003. Papers Submitted or In Preparation Shen, Y. and G.F. Cooper, A Bayesian univariate anomaly-detection algorithm. Submitted to the Machine Learning Journal. Shen, Y. and G.F. Cooper, Bayesian anomaly detection that includes a model of anomalies due to unknown causes. Shen, Y. and G.F. Cooper, An efficient Bayesian model averaging algorithm for disease-outbreak detection.
|
|
An outbreak detection algorithm that efficiently performs complete Bayesian model averaging over all possible spatial distributions of disease. 2007 International Society for Disease Surveillance Annual Conference, Indianapolis, Indiana USA, October 2007. [PPT] A Bayesian biosurveillance method that models unknown outbreak diseases. NSF Workshop on BioSurveillance Systems and Case Studies (BioSurveillance 2007), New Brunswick, New Jersey USA, May 2007. [PPT] Estimating the expected warning time of outbreak-detection algorithms. 2005 Syndromic Surveillance Conference, Seattle, Washington USA, September 2005. [PPT] |
|
2003 Fall 2004 Spring 2004 Fall 2005 Spring 2005 Fall 2006 Spring 2006 Fall |
|
Last update: Nov 2007
|