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Ariel Te Faigon

from Los Altos, CA
Age ~68
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Also known as:
Anat Faigon
Phone and address:
1800 Alford Ave, Los Altos, CA 94024
(650) 964-1232
Related to:
Annette SaigonGaia Faigon, 29Anat Faigon, 62
Connected to:
Melvin S Faigus, 89Hawa Faikai, 55Anthony J Faikel, 77Lorraine R Faiken, 65John T Faikowski, 65Amanda E Fail, 37Jaclyn Fail, 46June H Fail, 81Melanie J Fail, 34Never M Fail, 90

Ariel Faigon Phones & Addresses

  • 1800 Alford Ave, Los Altos Hills, CA 94024 (650) 964-1232
  • Los Altos, CA
  • Sunnyvale, CA
  • Mountain View, CA
  • 1800 Alford Ave, Los Altos, CA 94024 (408) 410-0192

Work

Position: Administration/Managerial

Education

Degree: Associate degree or higher

Resumes

Resumes

Ariel Faigon Photo 1

Ml Architect

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Location:
San Francisco, CA
Industry:
Internet
Work:
Netskope
Ml Architect
Coupons.com Oct 2010 - 2015
Generalist: Data Science, Software, Performance and Analytics Architect
Yahoo 1999 - Oct 2010
Principal Engineer
Sgi 1995 - 1999
Mts
National Semiconductor 1987 - 1995
Senior Software Engineer
Education:
The Hebrew University of Jerusalem 1984 - 1987
Master of Science, Masters, Bachelors, Bachelor of Science, Mathematics, Computer Science The Hebrew University of Jerusalem 1980 - 1983
Bachelors, Bachelor of Science
Skills:
Linux
Scalability
Unix
Software Engineering
Perl
Machine Learning
Data Mining
Distributed Systems
Big Data
Analytics
C
Shell Scripting
Agile Methodologies
Hadoop
Rest
Python
Object Oriented Design
Subversion
R
Free Software
Open Source
Mapreduce
Vowpal Wabbit
Optimizations
Algorithms
Git
Bash
Amazon Web Services
Apache Pig
Mercurial
Varnish
Apache
C++
Architecture
Ruby
Nosql
Amazon Ec2
Ubuntu
Debian
Business Intelligence
Financial Engineering
Multithreading
Mongodb
Embedded Systems
Mysql
Ggplot
Security
Nginx
Julia
Functional Programming
Interests:
Politics
Education
Environment
Poverty Alleviation
Science and Technology
Human Rights
Animal Welfare
Health
Languages:
Spanish
Hebrew
English
Arabic
Ariel Faigon Photo 2

Ariel Faigon

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Publications

Us Patents

Identifying Exceptional Web Documents

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US Patent:
8583778, Nov 12, 2013
Filed:
Apr 26, 2006
Appl. No.:
11/412360
Inventors:
Ariel Faigon - Los Altos CA, US
Timothy M. Converse - Sunnyvale CA, US
Priyank S. Garg - San Jose CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 17/30
US Classification:
709224, 709223, 709225, 709226, 705 1423, 705 1426, 705 1447
Abstract:
Techniques are provided through which “suspicious” websites may be identified automatically. A suspicious website is one that is associated with many changes or an inconsistent number of changes in web registry information over time. Registry information is received when changes to the registry information occur. The registry information is referred to as a transaction. A transaction is comprised of a plurality of values that each correspond to a characteristic. A characteristic is a property of a website, such as the website's contact information. A count associated with a particular characteristic-value pair is updated each time the particular value is identified in a transaction. A high count indicates that the website associated with the particular value is associated with a lot of changes. Therefore, a website associated with a high count is suspicious. Other factors that may be used for identifying a “suspicious” website include how often and how much the count changes.

Machine Learning Based Anomaly Detection And Response

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US Patent:
20210288983, Sep 16, 2021
Filed:
May 27, 2021
Appl. No.:
17/332879
Inventors:
- Santa Clara CA, US
Ravi ITHAL - Los Altos CA, US
Steve MALMSKOG - San Jose CA, US
Abhay KULKARNI - Cupertino CA, US
Ariel FAIGON - Santa Clara CA, US
Krishna NARAYANASWAMY - Saratoga CA, US
Assignee:
Netskope, Inc. - Santa Clara CA
International Classification:
H04L 29/06
G06N 20/00
G06F 21/55
G06F 21/62
G06N 5/02
G06N 7/00
Abstract:
The technology relates to machine responses to anomalies detected using machine learning based anomaly detection. In particular, to receiving evaluations of production events, prepared using activity models constructed on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, to responding to detected anomalies in near real-time streams of security-related events of tenants, the anomalies detected by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant. An anomaly score received for a production event is determined based on calculated likelihood coefficients of categorized feature-value pairs and a prevalencist probability value of the production event comprising the coded features-value pairs.
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Ariel Te Faigon from Los Altos, CA, age ~68 Get Report