Jennifer Lavergne, Ph.D.


Contact Information

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Education

Ph.D. - Computer Science
University of Louisiana at Lafayette (2013)

M.S. - Computer Science
University of Louisiana at Lafayette (2010)

M.S. - Mathematics
McNeese State University (2007)

B.S. - Computer Science
McNeese State University

Biography

Jennifer Lavergne, Ph.D. joined the University of Louisiana at Lafayette (UL Lafayette) as a Software Architect for the Center for Business Information Technology (CBIT) under the Informatics Research Institute

(IRI) in June 2013. She received her Ph.D. in Computer Science in 2013 from the University of Louisiana at Lafayette. She also has a Masters in Computer Science (UL Lafayette) and a Masters in Mathematics (McNeese State University). She has a combined 6 years of instruction experience, both online and in class in Business, Computer Science, and Mathematics.

The focus of her research has been in the field of data mining and includes both interdepartmental projects, collaborative research with UL’s Civil Engineering Department, and with CVDI IAB member projects. Her projects have covered such data science areas as datamining, association mining, rare association mining, action rules mining, temporal and streaming analysis, contrast sets, anomaly detection, and distributed mining. She currently has a thesis and dissertation publication, as well as four publications based upon dissertation research and CVDI projects.

Publications

Conference

Abbady, S., Ke, C., Lavergne, J., Chen, J., Raghavan V.V., and Benton, R., 2017. Online Mining for Association Rules and Collective Anomalies in Data Streams, Second Workshop on Real-time and Stream Processing in Big Data 2288-2297, in 2017 IEEE International Conference on Big Data (BIGDATA), Boston, MA.

Lavergne J., Benton R., Raghavan V.: “Min-Max Itemset Trees for Targeted Association Mining.” Foundations of Intelligent Systems: Lecture Notes in Computer Science Volume 7661, 2012, pp 51-60

Lavergne J., Benton R., Raghavan V.: “TRARM-RelSup: Targeted Rare Association

Rule Mining Using Itemset Trees and the Relative Support Measure.” Foundations of Intelligent Systems: Lecture Notes in Computer Science Volume 7661, 2012, pp 61-70

Lavergne J., Benton R., Raghavan V., Hafez A.: “An In-Memory Data Structure for Targeted Strong and Rare Association Rule Mining over Time-Varying Domains.” Web Intelligence (WI) and Intelligent Agent Technologies (IAT) Volume 1, 2013, pp 61-70

Thesis

Lavergne J., [2013] “An In-Memory Data Structure for Targeted Association Mining in Time Varying Domains.” Ph.D. in Computer Science. University of Louisiana at Lafayette, USA.

Stutes, Jennifer [2007] “Water Data Mining Comparisons Using Apriori-Water and PCY-Water.” Masters of Science in Mathematics. McNeese State University, USA.