Fatemeh Nargesian

About

Fatemeh Nargesian’s research in data management focuses on the discovery and integration of data in very large repositories of heterogeneous and raw data. Her research applies probabilistic and learning techniques to data management problems. Her previous work studies automated machine learning including feature engineering and model selection. Fatemeh received her PhD from the University of Toronto. Research OverviewFatemeh Nargesian's research focuses on efficiently identifying and combining data that accurately represents and models reality. Recently she has worked on optimizing the aquisition costs associated with building representational datasets that span multiple sources. She also works to futher enable climate data research through the development of efficient, scalable search alg

Selected publications

• Data Lake Management: Challenges and Opportunities. (Tutorial) Nargesian, E. Zhu, R. J. Miller, Ken Q. Pu. In Proceedings of the VLDB Endowment (VLDB), 2019.
• Optimizing Organizations for Navigating Data Lakes. F. Nargesian, K. Q. Pu, E. Zhu, B. G. Bashardoost, R. J. Miller. Revise and Resubmit, https://arxiv.org/abs/1812.07024, PVLDB, 2019.
• A Set Overlap Search Algorithm for Finding Joinable Tables in Massive Data Lakes. E. Zhu, D. Deng, F. Nargesian, R. J. Miller. SIGMOD: 847-864, 2019.
• Making Open Data Transparent: Data Discovery on Open Data. R. J. Miller, F. Nargesian, E. Zhu, Christina Christodoulakis, K. Q. Pu, Periklis Andritsos. IEEE Data Engineering Bulletin, 2018.
• Table Union Search on Open Data. F. Nargesian, E. Zhu, K. Q. Pu, R. J. Miller. In Proceedings of the VLDB Endowment, (PVLDB) 11(7): 813-825, 2018.
• Dataset Evolver: An Interactive Feature Engineering Notebook. F. Nargesian, U. Khurana, H. Samulowitz, D. S. Turaga, T. Pedapati. In Proceedings of the Conference on Artificial Intelligence (AAAI), 2018. Demonstration.
• Interactive Navigation of Open Data Linkages. (Best Demo Award) E. Zhu, K. Q. Pu, F. Nargesian, R. J. Miller. In Proceedings of the VLDB Endowment (VLDB), 2017. Demonstration.
• Learning Feature Engineering for Classification. F. Nargesian, H. Samulowitz, U. Khurana, E. B. Khalil, D. S. Turaga. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2017.
• Automating Feature Engineering. U. Khurana, F. Nargesian, H. Samulowitz, D. S. Turaga, E. B. Khalil. Artificial Intelligence for Data Science Workshop, NIPS, 2016.
• LSH Ensemble: Internet-Scale Domain Search. E. Zhu, F. Nargesian, K. Q. Pu, R. J. Miller. In Proceedings of the VLDB Endowment, (PVLDB) 9(12): 1185-1196, 2016.