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Gabriel S Peschiera

from Brooklyn, NY
Age ~37
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Also known as:
Peschiera S Gabriel, Gabriel S Pescheria
Related to:
Evert M Makinen, 85Gabriel S Pescheria, 37Nanci H, 69Patri N Tudela, 76Thomas Jakovich, 66Olivia Jakovich, 34Megan A Jakovich, 31
Connected to:
Gabriela M Peschiera, 83Juan L Peschiera, 71Kenneth Peschier, 48Melanie D Peschio, 60Ellyn R Peschitz, 40Charles Peschka, 40Christine M Peschka, 48Diane P Peschka, 65Steve V Peschka, 61Barbara J Peschke, 62

Gabriel Peschiera Phones & Addresses

  • Brooklyn, NY
  • New York, NY
  • 210 Sea View Dr APT 202, Key Biscayne, FL 33149 (305) 332-4898

Work

Company: Jaros, baum & bolles Jul 2018 Position: Director, smart buildings

Education

Degree: Master of Science, Masters School / High School: Columbia University May 2011

Skills

Energy Efficiency • Energy • Project Management • Energy Conservation • Sustainability • Renewable Energy • Engineering • Energy Management • Sustainable Energy • Analysis • Data Analysis • Alternative Energy • Energy Audits • Hvac • Civil Engineering • Green Building • Management • Product Development • Sales • Python • Feasibility Studies • Retro Commissioning

Languages

Spanish • French

Ranks

Certificate: Licensed Professional Engineer

Industries

Mechanical Or Industrial Engineering

Resumes

Resumes

Gabriel Peschiera Photo 1

Director, Smart Buildings

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Location:
5383 Hollister Ave, Goleta, CA 93111
Industry:
Mechanical Or Industrial Engineering
Work:
Jaros, Baum & Bolles
Director, Smart Buildings
Envairo
Chief Executive Officer
Ecorithm, Inc. Jun 2014 - Apr 2016
Director of Energy Engineering
R3 Energy Management Audit & Review Llc Jan 2014 - Jun 2014
Director of Engineering
R3 Energy Management Audit & Review Llc Jul 2011 - Dec 2013
Energy Systems Engineer
Education:
Columbia University May 2011
Master of Science, Masters Columbia Engineering 2010 - 2011
Masters, Civil Engineering Columbia University 2010
Bachelors, Bachelor of Arts, Bachelor of Science Columbia University In the City of New York 2005 - 2010
Bachelors, Bachelor of Arts, Bachelor of Science, Architecture, Civil Engineering
Skills:
Energy Efficiency
Energy
Project Management
Energy Conservation
Sustainability
Renewable Energy
Engineering
Energy Management
Sustainable Energy
Analysis
Data Analysis
Alternative Energy
Energy Audits
Hvac
Civil Engineering
Green Building
Management
Product Development
Sales
Python
Feasibility Studies
Retro Commissioning
Languages:
Spanish
French
Certifications:
Licensed Professional Engineer
Certified Energy Manager
Existing Building Commissioning Professional
Bpi Multi-Family Building Analyst Professional
State of New York, License 096598
Association of Energy Engineers, License 20826
Association of Energy Engineers, License 95073
Building Performance Institute, License 5033525
Engineer In Training
License 096598
License 20826
License 95073
License 5033525

Publications

Us Patents

Machine Learning-Based Fault Detection System

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US Patent:
20160203036, Jul 14, 2016
Filed:
Jan 7, 2016
Appl. No.:
14/990559
Inventors:
- Goleta CA, US
Michael Vincent Georgescu - Los Angeles CA, US
Gabriel Sebastian Peschiera - Brooklyn NY, US
Donald William Kasper III - Auburn NY, US
Sophie Marie Louise Loire - Oak View CA, US
International Classification:
G06F 11/07
G06N 7/00
G06N 99/00
Abstract:
Various systems and methods are provided that detect faults in data-based systems utilizing techniques that stem from the field of spectral analysis and artificial intelligence. For example, a data-based system can include one or more sensors associated with a subsystem that measure time-series data. A set of indicator functions can be established that define anomalous behavior within a subsystem. The systems and methods disclosed herein can, for each sensor, analyze the time-series data measured by the respective sensor in conjunction with one or more indicator functions to identify anomalous behavior associated with the respective sensor of the subsystem. A spectral analysis can then be performed on the analysis to generate spectral responses. Clustering techniques can be used to bin the spectral response values and the binned values can be compared with fault signatures to identify faults. Identified faults can then be displayed in a user interface.
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Gabriel S Peschiera from Brooklyn, NY, age ~37 Get Report