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Peter C Lonjers

age ~39

from West Hollywood, CA

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Peter Lonjers Phones & Addresses

  • West Hollywood, CA
  • North Hills, CA
  • Riverside, CA

Work

  • Company:
    Zefr
    Oct 2012
  • Position:
    Developer

Education

  • Degree:
    Graduate course work in PHD program
  • School / High School:
    University of California, Riverside
    2010 to 2012
  • Specialities:
    Neuroscience

Skills

Python • C • C++ • Linux • Javascript • Java • Html • Openmp • Data Analysis • Scheme • Social Media • Computer Science • Research • Algorithms • Openmpi • Matlab • Machine Learning • Testing • Programming • Elasticsearch

Languages

English

Interests

Long Boarding • News • Ultimate

Industries

Computer Software

Resumes

Peter Lonjers Photo 1

Software Developer

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Location:
7705 Hampton Ave, West Hollywood, CA 90046
Industry:
Computer Software
Work:
zefr since Oct 2012
developer
University of California, Riverside Jun 2010 - Jul 2012
Graduate Student Researcher
University of California, Riverside 2008 - 2010
Graduate Student Researcher
University of California, Riverside - Riverside, CA Sep 2007 - Jun 2009
Teaching Assistant
Elastic Image Jun 2006 - Aug 2006
Software Developer
Education:
University of California, Riverside 2010 - 2012
Graduate course work in PHD program, Neuroscience University of California, Riverside 2007 - 2010
Masters, Computer Science Rose-Hulman Institute of Technology 2004 - 2007
BS, Computer Science with a minor in Economics
Skills:
Python
C
C++
Linux
Javascript
Java
Html
Openmp
Data Analysis
Scheme
Social Media
Computer Science
Research
Algorithms
Openmpi
Matlab
Machine Learning
Testing
Programming
Elasticsearch
Interests:
Long Boarding
News
Ultimate
Languages:
English

Us Patents

  • Problem Solving By Plastic Neuronal Networks

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  • US Patent:
    20140012789, Jan 9, 2014
  • Filed:
    Mar 15, 2013
  • Appl. No.:
    13/840539
  • Inventors:
    The Regents of the University of California - , US
    Peter Lonjers - Riverside CA, US
    Steven Skorheim - Riverside CA, US
  • International Classification:
    G06N 3/08
  • US Classification:
    706 25
  • Abstract:
    More realistic neural networks are disclosed that are able to learn to solve complex problems though a decision making network, modeled as a virtual entity foraging in a digital environment. Specifically, the neural networks overcome many of the limitations in prior neural networks by using rewarded STDP bounded with rules to solve a complex problem.

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Peter Lonjers Photo 2

Peter Lonjers

Work:
Elastic Image - Developer
Education:
University of California, Riverside - Neuroscience PHD, Rose-Hulman Institute of Technology - Computer Science/Economics BS, University of California, Riverside - Computer Science MS
About:
Just trying to make things a little happier.
Peter Lonjers Photo 3

Peter Lonjers

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