H
Haimonti Dutta
Researcher at University at Buffalo
Publications - 46
Citations - 1408
Haimonti Dutta is an academic researcher from University at Buffalo. The author has contributed to research in topics: Random forest & Scalability. The author has an hindex of 16, co-authored 46 publications receiving 1202 citations. Previous affiliations of Haimonti Dutta include Columbia University & University of Baltimore.
Papers
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Proceedings ArticleDOI
NILMTK: an open source toolkit for non-intrusive load monitoring
Nipun Batra,John Kelly,Oliver Parson,Haimonti Dutta,William J. Knottenbelt,Alex Rogers,Amarjeet Singh,Mani Srivastava +7 more
TL;DR: This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets, and demonstrates the range of reproducible analyses made possible by the toolkit.
Journal ArticleDOI
Machine Learning for the New York City Power Grid
Cynthia Rudin,David L. Waltz,Roger N. Anderson,Albert Boulanger,Ansaf Salleb-Aouissi,M. Chow,Haimonti Dutta,Philip Gross,Bert Huang,Steve Ierome,Delfina Isaac,Arthur Kressner,Rebecca J. Passonneau,Axinia Radeva,Leon Wu +14 more
TL;DR: A general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems is introduced, and these models are sufficiently accurate to assist in maintaining New York City's electrical grid.
Proceedings Article
Distributed Top-K Outlier Detection from Astronomy Catalogs using the DEMAC System.
Journal ArticleDOI
A process for predicting manhole events in Manhattan
TL;DR: A knowledge discovery and data mining process developed as part of the Columbia/Con Edison project on manhole event prediction can assist with real-world prioritization problems that involve raw data in the form of noisy documents requiring significant amounts of pre-processing.
Patent
Machine learning for power grids
Roger N. Anderson,Albert Boulanger,Cynthia Rudin,David L. Waltz,Ansaf Salleb-Aouissi,Maggie Chow,Haimonti Dutta,Phil Gross,Bert Huang,Steve Ierome,Delfina Isaac,Arthur Kressner,Rebecca J. Passonneau,Axinia Radeva,Leon Wu,Peter Hofmann,Frank Dougherty +16 more
TL;DR: In this article, a machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components in the electrical grid is presented.