NIMROD HOOFIEN
Pilots at Cowper St, Palo Alto, CA
License number
California A5282343
Issued Date
Dec 2015
Expiration Date
Dec 2017
Category
Airmen
Type
Authorized Aircraft Instructor
Address
Address
2872 Cowper St, Palo Alto, CA 94306
Professional information
Director Of Engineering At Facebook
Position:
Director of Engineering at Facebook
Location:
San Francisco Bay Area
Industry:
Computer Software
Work:
Facebook
- Menlo Park, CA since Feb 2013
-
Director of Engineering
Tango.me
- Palo Alto, CA Apr 2011 - Jan 2013
-
Advisor
Ooyala
- Mountain View, CA May 2010 - Jan 2013
-
SVP of Engineering
Ning
Sep 2009 - Apr 2010
-
Director of Engineering, Monetization
Outcome Concept Systems
Jun 2009 - Sep 2009
-
VP of Development (contract)
Six Slice Studios
Jul 2008 - Jun 2009
-
Founder & CEO
Amazon.com
Aug 2003 - Jul 2008
-
Senior Manager, Software Development
AppStream
Apr 2000 - Jul 2003
-
Streaming Server Team Leader
The Academic College of Tel Aviv Yaffo
1998 - 2001
-
Adjunct Faculty Member, Computer Science
Tecnomatix
1997 - 2000
-
Senior Software Engineer
Mapat Amal
1995 - 1997
-
Software Engineer
Education:
University of Washington, Michael G. Foster School of Business 2005 - 2007
MBA, Executive MBA Tel Aviv University 1998 - 2001
M.Sc., Computer Science The Academic College of Tel-Aviv, Yaffo 1995 - 1998
B.A., Computer Science
MBA, Executive MBA Tel Aviv University 1998 - 2001
M.Sc., Computer Science The Academic College of Tel-Aviv, Yaffo 1995 - 1998
B.A., Computer Science
Skills:
People Management, Software Engineering, Executive Management, Distributed Systems, Software Development, Scalability, SaaS, Start-ups, Agile, Analytics, Cloud Computing, Web Services, Product Strategy, Agile Methodologies, Product Management, Management, Scrum, Hadoop, Strategy, Enterprise Software, Web Applications, Program Management, REST, Product Development, Architecture, Integration, Entrepreneurship, Leadership, Mentoring, Ruby, Java, Technical Leadership, E-commerce, Software Design, Linux, Object Oriented Design, Git, JavaScript
Automatically Recommending Content
US Patent:
8260117, Sep 4, 2012
Filed:
Feb 21, 2012
Appl. No.:
13/401098
Inventors:
Zhichen Xu - San Jose CA, US
Sami Abu-El-Haija - Mountain View CA, US
Lei Huang - Cupertino CA, US
Nimrod Hoofien - Palo Alto CA, US
Sami Abu-El-Haija - Mountain View CA, US
Lei Huang - Cupertino CA, US
Nimrod Hoofien - Palo Alto CA, US
Assignee:
Ooyala, Inc. - Mountain View CA
International Classification:
H04N 9/80
US Classification:
386262, 386239, 386240, 386343
Abstract:
Techniques are provided for selecting which videos to recommend to users by predicting the degree to which recommending each video will satisfy certain goals. To make the predictions, a trained machine learning engine is fed both collaborative filtering parameter values and content-based filtering parameter values. In the case of video-to-video recommendations, the collaborative filtering parameter values may be based on a video pair that includes a video in which a user has already demonstrated an interest. The machine learning engine generates a machine-learning score for each video. The machine learning scores are used as the basis for selecting which videos to recommend to a particular user.
Goal-Based Video Delivery System
US Patent:
2013002, Jan 31, 2013
Filed:
Jul 26, 2011
Appl. No.:
13/190714
Inventors:
Nimrod Hoofien - Palo Alto CA, US
Harry J.M. Robertson - Mountain View CA, US
Caleb E. Spare - Sunnyvale CA, US
Sean M. Knapp - Mountain View CA, US
Harry J.M. Robertson - Mountain View CA, US
Caleb E. Spare - Sunnyvale CA, US
Sean M. Knapp - Mountain View CA, US
International Classification:
H04N 9/80
US Classification:
386248, 386E05003
Abstract:
A system is provided that facilitates achieving a goal associated with a particular video asset. The system may provide an interface through which a user may specify control parameters that are to be the targets of testing, and a goal or combination of goals. The system may control a controller that performs experiments in an attempt to identify optimal values, relative to the specified goals, for the control parameters. The optimal values may be determined and tested on a per-individual-video asset basis. Further, the controller may generate multiple sets of optimal values for a given video, where each set is associated with a different combination of request attributes. To estimate the optimal parameter values for one video, the controller may use usage information collected for that video, as well as usage information collected for similar videos.