Prof Yang chairs Nano and Micro Systems Track at the 2009 ASME International Mechanical Engineering Congress and Exposition

Professor Yang is a co-chair of Technical Track 13: Nano and Micro Systems at the 2009 ASME International Mechanical Engineering Congress and Exposition to be held in Lake Buena Vista, Florida from November 13-19. Track 13 consists of approximately 50 sessions and over 200 papers related to advances in Nano and Micro Systems.

For more information, please contact:

Dr. Eui-Hyeok Yang
Associate Professor
Carnegie
Room 203
Phone: 201.216.5574
Fax: 201.216.8315

eyang@stevens.edu

Speaker:

Dr. Zhihua Qu, Professor

School of Electrical Engineering and Computer Science

University of Central Florida

Orlando, FL 32816

Time:  2pm-3pm Wednesday June 17, 2009

Location: Babbio 203

Bio Sketch:

Zhihua Qu received his Ph.D. degree in Electrical Engineering from Georgia Institute of Technology in 1990. Afterwards, he joined the Department of Electrical Engineering at the University of Central Florida (UCF). From 1995 to 1997, he served as the assistant chair of the department. From 1999 to 2004, he was the Director/Chair of the EE department at UCF. He is currently the SAIC Distinguished Professor at UCF and a Professor in School of EECS.

Dr. Qu’s research interests include system theory, advanced controls, and their applications to autonomous vehicles and intelligent systems. He has received a number of awards, authored three books, and published a number of the papers in his areas of expertise. Currently, he is an Associate Editor for Automatica, IEEE Transactions on Automatic Control, and International Journal of Robotics and Automation.

For more information, please contact:

Yingying Chen
Assistant Professor
Burchard
Room 210
Phone: 201.216.8066
Fax: 201.216.8246

yingying.chen@stevens.edu

Speaker:

Dr. Yihua Wu

Google, Inc.

Time: Wednesday 05/13/2009 3-4PM

Location: Babbio 110

Biography:

Dr. Yihua Wu received her PhD in Computer Science from Rutgers, the State University of New Jersey in 2007 and has been working in Google Inc. New York since then.  Her research interests are streaming techniques for statistical modeling of massive data with applications to databases and networking areas.  During her PhD, she extensively studied i) parametric modeling of skewed data sets; ii) graph modeling of individual's communication patterns; iii) sequential change detection on data streams.  Dr. Yihua Wu spent years of her PhD collaborating with researchers from AT&T Shannon Labs, Telcordia Applied Research, Narus Inc. to develop space- and time-efficient streaming algorithms on real world data sets and is holding two patents on that.  While working at Google, she designs and develops features and models to improve search quality.

Abstract:
Streaming is an important paradigm for handling high-speed data sets that are too large to fit in main memory. Prior work in data streams has shown how to estimate simple statistical parameters, such as histograms, heavy hitters, frequent moments, etc., on data streams. This talk focuses on a number of more sophisticated statistical analyses that are performed in near real-time, using limited resources.

I will first present how to model stream data parametrically; in particular, we fit hierarchical (binomial multifractal) and non-hierarchical (Pareto) power-law models on a data stream. It yields algorithms that are fast, space-efficient, and provide accuracy guarantees. I also designed fast methods to perform online model validation at streaming speeds. Then I studied the detection of changes in models on data with unknown distributions. I adapt the sound statistical method of sequential probability ratio test to the online streaming case, without independence assumption. The resulting algorithm works seamlessly without window limitations inherent in prior work, and is highly effective at detecting changes quickly. Furthermore, I formulated and extended our streaming solution to the local change detection problem that has not been addressed earlier.

As concrete applications of our techniques, we complement our analytic and algorithmic results with experiments on network traffic data to demonstrate the practicality of our methods at line speeds, and the potential power of streaming techniques for statistical modeling in data mining.

Contact:
Yingying Chen <yingying.chen@stevens.edu>

For more information, please contact:

Yingying Chen
Assistant Professor
Burchard
Room 210
Phone: 201.216.8066
Fax: 201.216.8246

yingying.chen@stevens.edu

Speaker:

Dr. Joan Bachenko

Linguistech Consortium

Deception Detection Technologies

Time: Wednesday 05/06/2009 3-4PM

Location: Babbio 110

Abstract:

This talk describes a NLP approach to the identification of deceptive language in transcribed interviews. Although deception detection has long been an interest among research psychologists and law enforcement professionals, language-based analyses of deception is a new area of investigation for computational linguistics. The talk will have three parts: a brief review of previous work on language and deception detection that has motivated our research, a description of the model we have developed for identifying deceptive language by native speakers and a review of an experiment that tested the ability of the model to identify deceptive and non-deceptive passages of transcribed speech. The data used in our work comes exclusively from "real world" sources--police interrogations, criminal statements and legal depositions. Using Classification and Regression Tree techniques, we found that the model correctly identifies 74.9% of the deceptive and non-deceptive propositions rated in the experiment. Current applications of the model focus on automatic recognition of deceptive narratives in large corpora and aids to real-time interviewing.

Biography:

Dr. Bachenko received her Ph.D. in Linguistics from New York University. Her work in computational linguistics has focused on research and technology development in natural language processing, speech synthesis and speech recognition. She has spent roughly half her career at research laboratories—the Naval Research Laboratory in Washington, D.C. and Bell Laboratories in Murray Hill, NJ. She left Bell Laboratories to co-found Linguistic Technologies, Inc. (LTI), a Minnesota-based startup that developed speech recognition technology for the transcription of medical dictation over the phone. After LTI was acquired, Dr. Bachenko began her ongoing collaboration with Montclair State University in New Jersey. Her research with Montclair focuses on the analysis of deceptive language in native and non-native speakers of English. She is currently working on the implementation of a NLP model of deceptive language and on the development of training methods that will enable interviewers to detect significant language changes in real time. Dr. Bachenko has also served as adjunct faculty at the University of Minnesota and Montclair State University.

Contact:
Yingying Chen <yingying.chen@stevens.edu>

For more information, please contact:

Yingying Chen
Assistant Professor
Burchard
Room 210
Phone: 201.216.8066
Fax: 201.216.8246

yingying.chen@stevens.edu

Speaker:

Dr. Yoo Jung An

Time: Wednesday 3/25/2009 12:30 – 1:30PM

Location: Babbio 221

Abstract:

The Semantic Web is considered to be the third generation of the World-Wide Web.  Its goal is to automate some of the activities that humans perform on the Web.  For this purpose, the WWW is augmented with agent programs and ontologies.  Ontologies are represented with standard languages such as RDF/OWL. In this talk we will review the basics of ontologies and the Semantic Web.  A fruitful area of research is the interplay of the Semantic Web with what has become known as the Deep Web. The Deep Web consists of information in backend databases with Web frontends.  We will present past research on automatically extracting ontologies for the Semantic Web from the Deep Web and on how to implement a semantic search engine. We have coined the name "Semantic Deep Web" for the combination of the Deep Web with the Semantic Web. Research plans for the future include building an ontology-enabled search engine on top of Google.