Management Science and Systems Department
PhD, Computer Science and Electrical Engineering, University of
Maryland, Baltimore County
MS, Computer and Information Science, Temple University
BCSE, Computer Science and Engineering, Jadavpur University, India
Applications: health care, smart environments (smart electrical grid, vehicles, buildings, wearable sensors) and digital humanities
Haimonti Dutta, Alex Kamil, Manoj Pooleery, Simha Sethumahadevan, and John Demme,”Distributed Storage of Large Scale Multidimensional Electroencephalogram Data using Hadoop and HBase”, Grid and Cloud Database Management, Springer, 2011.
Xianshu Zhu, Tushar Mahule, Haimonti Dutta, Sugandha Arora, Hillol Kargupta, Kirk D. Borne."Peer-to-peer distributed text classifier learning in PADMINI." Statistical Analysis and Data Mining 5 (5): 446-462, 2012.
Cynthia Rudin, David Waltz, Roger N. Anderson, Albert Boulanger, Ansaf Salleb-Aouissi, Maggie Chow, Haimonti Dutta, Philip Gross, Bert Huang, Steve Ierome, DelÞna Isaac, Arthur Kressner, Rebecca J. Passonneau, Axinia Radeva and Leon Wu, “Machine Learning for the New York City Power Grid", IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol 34(2), Pages 328-345, 2012.
Cynthia Rudin, Rebecca J. Passonneau, Axinia Radeva, Haimonti Dutta, Steve Ierome, Delfina Isaac, "A process for predicting manhole events in Manhattan.", Machine Learning, Vol 80(1), Pages 1-31, 2010.
Hillol Kargupta, Byung Hoon Park, Haimonti Dutta. “Orthogonal Decision Trees”, IEEE Transactions on Knowledge and Data Engineering, Vol 18(7), July 2006.
"An Early Warning Device to Allow Epilepsy Patients to Lead a More Normal Life"
"EEGMine: A Distributed Framework for Learning on EEG Data from Epilepsy Patients"
"Intracranial EEG Acquisition System with Online Fast Ripple Detection"
"Development of Distributed Algorithms for Incremental Sensing and Communication"
"A Distributed Framework for Learning on EEG Data Obtained from Epilepsy Patients"
"Leveraging 'The Wisdom of the Crowd' for Efficient Tagging and Retrieval of Documents from the Historic Newspaper Archive of the New York Public Library"
“Using Machine Learning to Understand the Scaling Behavior of the GFDL FMS High Resolution Atmosphere Model on the Argonne BG/Q System”
Management Science and Systems
School of Management
University at Buffalo
325P Jacobs Management Center
Buffalo, NY 14260-4000