GMU Software Engineering Seminar Series

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Date: Wed, 01/28/2009

Time: 11:30 – 12:30pm

Location: 430A ST2

Pizza will be served at 12:30pm with a chance to meet with Dr. Ding

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Title: Discriminating Patterns for Empirical Discovery in Geospatial Data

Speaker: Wei Ding

Assistant Professor, University of Massachusetts Boston

Abstract

Numerous properties of an increasing number of sites on Earth are now thoroughly measured due to routinization of remote sensing and the progress in the GIS techniques. This new found richness of geospatial data in conjuncture with intelligent data processing transforms our understanding of geospatial phenomena. This project seeks to contribute to this trend by developing methods for auto-modeling causal relationships that are present between geographically collocated geo-variables but have been difficult or impossible to identify using traditional methods. Specifically, the project aims at providing the scientific community with an efficient tool for auto-analyzing the root causes behind observed patterns in geospatial data. We propose to integrate techniques from association analysis, reinforcement learning, and similarity measurement to design a tool that provides a domain expert with a complete yet comprehensible empirical model of an observed phenomenon. Novel solutions are required at all stages of the method development: (i) integration of geospatial data into the format suitable to association analysis, (ii) determining a scope and footprint of the studied phenomenon, (iii) selecting patterns of controlling factors that are indicative of the phenomenon, and (iv) summarizing the patterns into the products which are of immediate use to a domain experts.

Bio

Wei Ding is an Assistant Professor of Computer Science at the University of Massachusetts Boston. She received her M.Sc. Degree in Software Engineering from the George Mason University in 2000 and her Ph.D. degree in Computer Science from the University of Houston in 2008. Her main research interests include Data Mining, Machine Learning, Artificial Intelligence and with applications to astronomy, geosciences, and environmental sciences.

Host: Jeff Offutt and Paul Ammann