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Rebecca J Passonneau

from State College, PA
Age ~71
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Also known as:
Rebecca Jane Passonneau, Rebecca K Passonneau, Rebecca J Schiffman, Rebecca J Shenker, Rebecca U
Phone and address:
219 Ronan Dr, State College, PA 16801
(646) 250-5044
Related to:
Stephen Mark Harrell, 51Zachary S Schiffman, 75Stephen H Shenker, 71William L SchiffmanSylvia R Schiffman
Connected to:
Kristine M Passons, 36Alessandra M Passos, 51Carol J Passos, 77Erick Passos, 47Gabriel Passos, 27Lucia H Passos, 61Gerald S Passovoy, 87Gerald C Passow, 64Jon M Passow, 69Vickie L Passow, 64

Rebecca Passonneau Phones & Addresses

  • 219 Ronan Dr, State College, PA 16801 (646) 250-5044
  • 71 Linaria Way, Portola Valley, CA 94028 (650) 561-9676
  • Menlo Park, CA
  • 101 End Ave, New York City, NY 10023 (212) 787-7597 (917) 441-6207
  • 142 End Ave, New York, NY 10023 (212) 787-7597
  • 142 W End Ave APT 5V, New York, NY 10023 (212) 787-7597
  • Philadelphia, PA
  • Central, PA
  • Highland Park, NJ
  • Princeton, NJ

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Rebecca Passonneau

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Publications

Us Patents

Machine Learning For Power Grid

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US Patent:
20130232094, Sep 5, 2013
Filed:
Jan 15, 2013
Appl. No.:
13/742124
Inventors:
Cynthia Rudin - New York NY, US
David Waltz - Princeton NJ, US
Maggie Chow - Hartsdale NY, US
Haimonti Dutta - Trenton NJ, US
Phil Gross - Brooklyn NY, US
Huang Bert - Silver Spring MD, US
Steve Ierome - New York NY, US
Delfina Isaac - New York NY, US
Arthur Kressner - Westfiled NJ, US
Rebecca J. Passonneau - New York NY, US
Axinia Radeva - New York NY, US
Leon L. Wu - New York NY, US
Peter Hofmann - Hasbrouck Heights NJ, US
Frank Dougherty - Yorktown Heights NY, US
Assignee:
Consolidated Edison Company of New York - New York NY
The Trustees of Columbia University in the City of New York - New York NY
International Classification:
G06N 99/00
US Classification:
706 12
Abstract:
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 within the electrical grid; (b) a data processor, operatively coupled to the raw data assembly, to convert the raw data to more uniform data via one or more data processing techniques; (c) a database, operatively coupled to the data processor, to store the more uniform data; (d) a machine learning engine, operatively coupled to the database, to provide a collection of propensity to failure metrics for the like components; (e) an evaluation engine, operatively coupled to the machine learning engine, to detect and remove non-complying metrics from the collection of propensity to failure metrics and to provide the collection of filtered propensity to failure metrics; and (f) a decision support application, operatively coupled to the evaluation engine, configured to display a ranking of the collection of filtered propensity to failure metrics of like components within the electrical grid.
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Rebecca J Passonneau from State College, PA, age ~71 Get Report