[Apologies for multiple postings]

Dr. Ho will visit us tomorrow (Tue, 11/30) and talk
at GRAND seminar at noon. Please join us.

*    GRAND Seminar


Querying Similar (Tropical Cyclone) Events via Metric Learning
on Multivariate Spatial-Temporal Data Sequences


Nov/30/2010, Tuesday noon-1pm. ENGR 4201


Shen-Shyang Ho
Assistant research scientist
Center for Automated Research (CfAR)
Institute of Advanced Computer Studies
University of Maryland


Prof. Jessica Lin


In this talk, I will first provide an overview of my projects that
utilize computer science research advances for technology development
to support hurricane research. In particular, I will briefly discuss
two projects, namely (1) hurricane tracking using heterogeneous
satellite data sources, and (2) moving objects database technology to
support ad-hoc spatio-temporal query and hurricane data analysis.

Then, I will describe our solution for ad-hoc similarity query based
on user-defined instance-level constraints for tropical cyclone
events, represented by arbitrary length multivariate trajectory data
sequences. A critical component for the solution of such a problem is
the similarity/metric function to compare the data sequences. Our
solution is a novel Longest Common Subsequence (LCSS) parameter
learning approach driven by nonlinear dimensionality reduction and
distance metric learning. Intuitively, arbitrary length multivariate
data sequences are projected into a fixed dimensional manifold for
LCSS parameter learning. Similarity search is achieved through
consensus among the (similar) instance-level constraints based on
ranking orders computed using the LCSS-based similarity measure.

Experimental results using a combination of synthetic and real
tropical cyclone event data sequences are presented to demonstrate the
feasibility of our parameter learning approach and its robustness to
variability in the instance constraints. I will use a similarity query
example on real tropical cyclone events from 2000 to 2008 to discuss
(i) a problem of scientific interest, and (ii) challenges and issues
related to the weather event similarity search and query problem.


Dr. Shen-Shyang Ho received his PhD in Computer Science from George
Mason University in 2007 and his Bachelor (Honors) in Science
(Mathematics and Computational Science) from the National University
of Singapore in 1999. From 2007 to 2010, he was a NASA postdoctoral
fellow and a Caltech Postdoctoral Scholar working at the Jet
Propulsion Laboratory (JPL) at the California Institute of Technology.
His research interests include artificial intelligence, machine
learning, pattern recognition, and data mining for streaming data and
on mobile devices. Currently, he is a researcher in the Center for
Automated Research (CfAR) of the Institute for Advanced Computer
Studies (UMIACS) at the University of Maryland. His current research
is a collaboration with JPL and University of Florida, Gainesville,
and is funded by NASA.

*Jyh-Ming Lien*
Assistant Professor, George Mason University