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Welcome to our Faculty and Research Section

College of Business Faculty and Business
Hamid BeladiResearch and teaching are complimentary and equally important.  Research contributes to the production of knowledge, while teaching is concerned with the distribution of knowledge in society.  Teaching without research quickly becomes stereotyped, unexciting --removed from the ever growing frontiers of knowledge, while research without teaching becomes unintelligible and uncommunicative.  Continuous interaction with fresh minds through teaching makes research more proactive and highly productive.

At the UTSA College of Business, we share a commitment to research and publications, which positions us well to absorb quality faculty and students. Research is part of a learning cycle, significantly contributing to the absorption of quality students and retention of quality faculty yielding an institution of higher learning that continuously strives for excellence.

UTSA College of Business faculty members are conducting research throughout a variety of disciplines and have been recognized by their peers for their accomplishments. The Department of Management was ranked 65th nationally and the Department of Marketing was ranked 69th nationally for their research publications according to UT Dallas research rankings for 2012-2013.

UTSA College of Business Receives $1 Million for Digital Forensics Research

UTSA researchers were awarded $797,000 in funding from the Naval Postgraduate School, the U.S. Navy’s national security research university, as part of a three-year $1.4 million contract with the U.S. Department of Homeland Security Science and Technology Directorate Cyber Security Division.

UTSA researchers will be responsible for developing an algorithm that detects hostile insiders using digital forensics—the science of discovering, recovering and investigating digital information. The algorithm will help companies detect data exfiltration, employee misconduct and other unauthorized activity that jeopardizes the organization.

“We are pioneering a new approach in insider threat detection using digital forensics and data mining,” said Nicole Beebe, assistant professor of digital forensics and principal investigator of the project. “Previous approaches relied primarily on behavioral analysis from past breaches, but this failed to detect new methods for attacks because no two threats were exactly the same.”

The end result will be a computer program that will scan an organization’s computer systems, analyze the data and present a report on system usage anomalies.

“The benefit of our system is that it is economical to employ and uses only a small amount of memory, processing power and disk space,” said Beebe. “We have found that a common denominator in corporate data theft is digital hoarding. Our system detects hostile insiders by comparing their storage profiles with the storage profile of others in their organization and by detecting deviations in an individual’s storage pattern over time.”

Statistics Faculty Members Receives $150,000 National Science Foundation Grant

Victor De OliveiraVictor De Oliveira, associate professor of management science and statistics, received a three-year $150,000 grant from the National Science Foundation. De Oliveira's research interests include Bayesian methods, environmental statistics and geostatistics.

The project is "Geostatistical Modeling of Spatial Discrete Data." The research consists of three parts. First, a class of hierarchical spatial models will be developed that seeks to ameliorate some limitations of the currently used models identified by the PI. The properties of these models and likelihood based methods to fit them will be studied. Second, a class of nonhierarchical spatial models will be developed that seeks to represent a wide range of spatial discrete data, not just counts, having association structures that are complementary to those in the class of hierarchical spatial models. The properties of these models and likelihood based methods to fit them will be studied. Third, a Bayesian method to perform goodness-of-fit for the aforementioned two classes of spatial models will be developed.

The statistical methodology developed in the course of this project would have immediate methodological and practical impacts on the earth and social sciences, where spatial data are routinely collected but models and methods for their analysis are scarce. The proposed classes of models will substantially increase the arsenal of tools available to spatial data analysts and the possibility of representing a wide range of behaviors for spatial discrete data.



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