Assistant Signal Processing

Dayu (David) Yang

-----------------------------------------------------------------------------

*** ***** ***** ***., *********, CA 94086

Phone: 408-***-**** begin_of_the_skype_highlighting              408-***-****      end_of_the_skype_highlighting

Email: ********@*****.***

QUALIFICATION SUMMARY

=====================

* Excellent knowledge in pattern classification, information retrieval, anomaly detection, data mining, signal processing, image processing and statistical modeling.

* Great experience on support vector machine (SVM), neural networks, k-mean, kNN, regression, decision trees, decision fusion, feature extraction, feature selection, and decision fusion, etc.

* Excellent understanding of data structures and algorithms, such as searching and sorting.

* Highly self-motivated, inquisitive, creative and resourceful.

* Fluent in Matlab, C/C++, OpenCV, LaTeX, Microsoft Office. Good Ubuntu experience.

EDUCATION

=========

Ph.D. Electrical Engineering and Computer Science 01/2006 - 12/2009

University of Tennessee, Knoxville

M.S. Electrical Engineering 08/2001 - 12/2004

University of Tennessee, Knoxville

B.S. Communications Engineering 09/1992 - 07/1996

Chongqing University of Posts and Telecommunications

EXPERIENCE

===================

01/2006 - 12/2009

Graduate Research Assistant

Advanced Imaging and Collaborative Information Processing (AICIP) Lab

University of Tennessee, Knoxville

* Designed and implemented a highly efficient nonparametric quickest detection algorithm which can be effectively applied to detect a wide variety of anomalies or abrupt changes in real-time. Originally defined a distance measure, Q-Q distance, for distributional change based on quantile-quantile plot and provided an asymptotic analysis of it. Designed and implemented a nonparametric decentralized quickest detection procedure which, to the best of our knowledge, is the first implementation of nonparametric decentralized quickest detection.

* Designed and implemented a real-time algorithm for network intrusion detection which includes feature extraction using independent component analysis and decision fusion to aggregate the classification results from kNN and SVM classifiers. The KDD99 data was used and the overall detection rate is 89.4%, which is comparable to the KDD99 winner's 92.7% detection rate but my algorithm is able to heavily reduce the computational burden for classification tasks.

* Designed and implemented an algorithm applying autoassociative kernel regression coupled with the statistical probability ratio test onto a simulated SCADA system to detect a variety of common attacks or anomalies. This work resulted in a patent applied by UTK.

* Uses Scale-invariant feature transform (SIFT) for feature based image comparison. The detected SIFT feature points are used as anchor points. The image’s feature descriptors are used for finding their correspondence in other images. If in two images, there are large enough number of sift points can match we claim these two images contain similar scenes.

Jan 2002 - Dec 2004

Graduate Research Assistant

Applied Visualization Center (AVC)

University of Tennessee, Knoxville

* Implemented a 3D sound system using the Head Related Transfer Functions and successfully simulated a virtual environment with multiple sound sources. This is also my MS thesis work.

* Developed CheckViz, a 3D visual simulation software for airport security training.

PROFESSIONAL EXPERIENCE

=======================

Aug 1996 - Jul 2001

Sr. Field Engineer and Assistant Dept. Manager

China Telecom Chongqing Branch, Chongqing, China

* Implemented wireless communications networks, including national 139 GSM network and 198 FLEX paging network and some other local networks. Tested and optimized existing wireless networks.

* Directed workflow, supervised and trained junior engineers.

SELECTED PUBLICATIONS

=====================

Dayu Yang, Hairong Qi, A Network Intrusion Detection Method using Independent Component Analysis, IEEE 19th International Conference on Pattern Recognition, Tampa, FL, Dec. 2008.

Dayu Yang, Alexander Usynin, and J. Wesley Hines, Anomaly-Based Intrusion Detection for SCADA Systems, 5th International Topical Meeting on Nuclear Plant Instrumentation, Controls, and Human Machine Interface Technology (NPIC&HMIT 2006), Albuquerque, NM, Nov. 2006

Dayu Yang, Alexander Usynin, and J. Wesley Hines, Anomaly-Based Intrusion Detection for SCADA Systems, IAEA Technical Meeting on Cyber Security of NPP I&C and Information systems, Idaho Fall, ID, Oct. 2006

Dayu Yang, Hairong Qi. An Effective Nonparametric Quickest Detection Procedure based on Q-Q Distance, the 35th International Conference on Acoustics, Speech, and Signal Processing, 2010

Dayu Yang, Hairong Qi. An Effective Decentralized Nonparametric Quickest Detection Approach, IEEE Transactions on Signal Processing. Submitted for review.

Contact this candidate