Rodica Neamtu
Associate Teaching Professor, Co-Director Bucharest Project Center
Affiliated with:
Education:
PhD Computer Science Worcester Politechnic Institute 2017
Rodica Neamtu is a data mining researcher who investigates how to develop and leverage ground-breaking techniques to explore time series data-sets at the confluence of theoretical computer science and application domains like medicine, economics and education. She has more than fifteen years of teaching experience in various academic institutions. She is committed to use her love and talent for teaching and research to empower others through education to make a difference in the world.
Office Location
Fuller Labs FL-145
Contact
Research Interests
Research Interests:
Data Mining, Big Data, Time Series Analytics, Machine Learning
Interactive Exploration of time series
Scholarly Work
Rodica Neamtu, Ramoza Ahsan, Elke Rundensteiner, Gabor Sarkozy, Eamonn Keogh, Hoang Anh Dau, Cuong Nguyen, Charles Lovering "Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances". In Proceedings of International Conference in Data Engineering ICDE 2018, IEEE 2018. - 2018
Neamtu, Rodica, Ramoza Ahsan, Elke Rundensteiner, Gabor Sarkozy "Interactive time series exploration powered by the marriage of similarity distances." Proceedings of the VLDB Endowment 10.3 (2016): 169-180. - 2017
Neamtu, Rodica, Ramoza Ahsan, Charles Lovering, Cuong Nguyen, Elke Rundensteiner, Gabor Sarkozy "Interactive Time Series Analytics Powered by ONEX." Proceedings of the 2017 ACM International Conference on Management of Data. ACM, 2017. - 2017
C Nguyen, Ch Lovering, R Neamtu. Ranked Time Series Matching by Interleaving Similarity Distances. In Proceedings of 4th Workshop on Advances in Software and Hardware for Big Data Science (ASH2017), Big Data Boston, 2017. - 2017
Rodica Neamtu, Caitlin Kuhlman, Ramoza Ahsan, Elke Rundensteiner. The impact of Big Data on making evidence-based decisions. Book chapter in Frontiers in Data Science. September 30, 2017 CRC Press
Reference - 450 Pages - 50 B/W Illustrations
ISBN 9781498799324 - CAT K30579
Series: Chapman and Hall CRC Big Data Series. - 2017
Ramoza Ahsan, Rodica Neamtu, Elke Rundensteiner. Towards spreadsheet integration using entity identification driven by a spatial-temporal model. In Proceedings of the 31st Annual ACM Symposium on Applied Computing, pages 1083–1085. ACM, 2016. - 2016
Professional Affiliations
Professional Affiliations: