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Tye Lawrence Rattenbury

from Orinda, CA
Age ~43
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Also known as:
Tye L Rattenbury, Tye Kirby Rattenbury, Lawrence T Rattenbury
Related to:
Eiko K Rattenbury, 76
Connected to:
James C Rattenborg, 61Denise L Rattenni, 54Alexis N Ratterman, 29Bernard Ratterman, 51Erica L Ratterman, 26Jeffrey P Ratterman, 59Justin R Ratterman, 47Stephen E Ratterman, 39Amber L Ratterree, 40Brooke A Ratterree, 27

Tye Rattenbury Phones & Addresses

  • Orinda, CA
  • 199 New Montgomery St, San Francisco, CA 94105
  • New York, NY
  • Wilton, CT
  • Centennial, CO

Work

Company: Salesforce Jul 1, 2016 to Oct 2018 Position: Senior director, data science and machine learning

Education

Degree: Doctorates, Doctor of Philosophy School / High School: University of California, Berkeley 2001 to 2008 Specialities: Computer Science

Skills

Algorithms • Machine Learning • Human Computer Interaction • Apis • Python • Data Mining • Data Analysis • Statistical Modeling • Data Visualization • Mixed Research Methods • Research • Ethnography • Computer Science • Interaction Design • Rapid Prototyping • Qualitative Research • User Centered Design • Computer Vision • User Research • User Experience • Experience Design • Artificial Intelligence • Pattern Recognition • Natural Language Processing • Analytics • Data Science • Software Engineering • Big Data • Information Retrieval • Mathematical Modeling • Usability Testing • Information Visualization • Contextual Inquiry • Research Design • Predictive Analytics • Predictive Modeling • Algorithm Design • Text Mining • Distributed Systems • Software Development

Languages

English

Interests

The Dead Weather • New York City • Flight of the Conchords (Tv Series) • Central Park (New York City) • Kindle • Skiing • Interpol • Nikola Tesla • Jack White • San Francisco • Facebook • Amazon • The Black Keys • The Next Generation • Star Trek • Evolutionary Biology • Nhl • Nike • Patagonia

Industries

Information Technology And Services

Resumes

Resumes

Tye Rattenbury Photo 1

Research Science Manager, Marketplace

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Location:
32 Wilder Rd, Orinda, CA 94563
Industry:
Information Technology And Services
Work:
Salesforce Jul 1, 2016 - Oct 2018
Senior Director, Data Science and Machine Learning
Lyft Jul 1, 2016 - Oct 2018
Research Science Manager, Marketplace
Trifacta Jul 1, 2016 - Oct 2018
Advisor
Trifacta Jun 2014 - Jul 2016
Director, Data Science and Solutions Engineering
Facebook Jun 2012 - May 2014
Data Scientist, Core Data Science
Education:
University of California, Berkeley 2001 - 2008
Doctorates, Doctor of Philosophy, Computer Science University of Colorado Boulder 1999 - 2001
Bachelors, Bachelor of Science, Applied Mathematics Yale University
Arapahoe High School
Skills:
Algorithms
Machine Learning
Human Computer Interaction
Apis
Python
Data Mining
Data Analysis
Statistical Modeling
Data Visualization
Mixed Research Methods
Research
Ethnography
Computer Science
Interaction Design
Rapid Prototyping
Qualitative Research
User Centered Design
Computer Vision
User Research
User Experience
Experience Design
Artificial Intelligence
Pattern Recognition
Natural Language Processing
Analytics
Data Science
Software Engineering
Big Data
Information Retrieval
Mathematical Modeling
Usability Testing
Information Visualization
Contextual Inquiry
Research Design
Predictive Analytics
Predictive Modeling
Algorithm Design
Text Mining
Distributed Systems
Software Development
Interests:
The Dead Weather
New York City
Flight of the Conchords (Tv Series)
Central Park (New York City)
Kindle
Skiing
Interpol
Nikola Tesla
Jack White
San Francisco
Facebook
Amazon
The Black Keys
The Next Generation
Star Trek
Evolutionary Biology
Nhl
Nike
Patagonia
Languages:
English

Publications

Us Patents

Automatic Extraction Of Semantics From Text Information

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US Patent:
8060491, Nov 15, 2011
Filed:
Jan 27, 2011
Appl. No.:
13/014874
Inventors:
Mor Naaman - San Francisco CA, US
Tye Rattenbury - San Francisco CA, US
Nathaniel Good - Albany CA, US
Assignee:
Yahoo! Inc. - Sunnyvale CA
International Classification:
G06F 7/00
G06F 17/30
US Classification:
707708, 707736
Abstract:
The present invention provides systems, methods and computer program products for extracting semantic information from text data having metadata associated therewith. A method according to an embodiment of the present invention includes selecting an ordered set of scale values for a plurality of scales and, for each of the scale values, determining one or more subset of metadata information related to the scale value. For each of the scales and associated subsets, a statistic on occurrences of content associated with the metadata in each subset is determined and the statistics are aggregated for each scale and associated subsets to determine a semantic level for the content. The scales and associated subsets having content with a semantic level above a threshold may be determined to extract semantic information across multiple time frames with the ordered set of scale values for the plurality of scales.

Automatic Extraction Of Semantics From Text Information

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US Patent:
20090063536, Mar 5, 2009
Filed:
Aug 30, 2007
Appl. No.:
11/847848
Inventors:
Mor Naaman - San Francisco CA, US
Tye Rattenbury - San Francisco CA, US
Nathaniel Good - Albany CA, US
Assignee:
YAHOO! INC. - Sunnyvale CA
International Classification:
G06F 17/30
US Classification:
707102, 707E17005
Abstract:
The present invention provides systems, methods and computer program products for extracting semantic information from text data having metadata associated therewith. A method according to an embodiment of the present invention includes selecting an ordered set of scale values for a plurality of scales and, for each of the scale values, determining one or more subset of metadata information related to the scale value. For each of the scales and associated subsets, a statistic on occurrences of content associated with the metadata in each subset is determined and the statistics are aggregated for each scale and associated subsets to determine a semantic level for the content. The scales and associated subsets having content with a semantic level above a threshold may be determined to extract semantic information across multiple time frames with the ordered set of scale values for the plurality of scales.

Apparatus, Method And Computer Program Product For Characterizing An Individual Based On Musical Preferences

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US Patent:
20130290348, Oct 31, 2013
Filed:
Apr 30, 2012
Appl. No.:
13/459390
Inventors:
Peter Jung - Darien CT, US
Deborah Serianni - Old Greenwich CT, US
Chris Colborn - Sunnyside NY, US
William Turnage - Brooklyn NY, US
Seth Solomon - Brooklyn NY, US
Michael C. Piccuirro - Cranford NJ, US
Tye Rattenbury - New York NY, US
Assignee:
MasterCard International Incorporated - Purchase NY
International Classification:
G06F 17/30
US Classification:
707748, 707E17102
Abstract:
Each of a plurality of media clips accessed by a user is scored by analyzing metadata associated with the media clips. A representative subset of the media clips is selected based on the scoring. A visual representation of the representative subset of the media clips is displayed.

Action Detection And Activity Classification

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US Patent:
20200171371, Jun 4, 2020
Filed:
Nov 27, 2019
Appl. No.:
16/697376
Inventors:
- Beaverton OR, US
Jordan M. Rice - Portland OR, US
Steven H. Walker - Camas WA, US
Adam Carroll - Bend OR, US
Corey Dow-Hygelund - Sunriver OR, US
Aaron K. Goodwin - Bend OR, US
James M. Mullin - Bend OR, US
Tye L. Rattenbury - New York NY, US
Joshua M. Rooke-Ley - New York NY, US
John M. Schmitt - Bend OR, US
International Classification:
A63B 71/06
A61B 5/00
A41D 1/00
G16H 20/40
G16H 40/67
G16H 20/30
G09B 5/12
G09B 5/00
A63B 24/00
G09B 19/00
G09B 5/02
A61B 5/11
A61B 5/024
A61B 5/01
A43B 3/00
Abstract:
Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Action Detection And Activity Classification

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US Patent:
20190329118, Oct 31, 2019
Filed:
Jul 11, 2019
Appl. No.:
16/508553
Inventors:
- Beaverton OR, US
Jordan M. Rice - Portland OR, US
Steven H. Walker - Camas WA, US
Adam S. Carroll - Bend OR, US
Corey C. Dow-Hygelund - Sunriver OR, US
Aaron K. Goodwin - Bend OR, US
James M. Mullin - Bend OR, US
Tye L. Rattenbury - New York NY, US
Joshua M. Rooke-Ley - New York NY, US
John M. Schmitt - Bend OR, US
International Classification:
A63B 71/06
A61B 5/00
G09B 19/00
A41D 1/00
G16H 40/67
G16H 20/30
G09B 5/12
A63B 24/00
A43B 3/00
A61B 5/01
A61B 5/024
G09B 5/00
A61B 5/11
G09B 5/02
G16H 20/40
Abstract:
Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Computing Dimensional Influence And Health Scores In Non-Linear Statistical Models

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US Patent:
20190164171, May 30, 2019
Filed:
Nov 30, 2017
Appl. No.:
15/828140
Inventors:
- San Francisco CA, US
Tye L. RATTENBURY - San Francisco CA, US
Sree Krishna KUMARASWAMY - Mountain View CA, US
Chaitanya Deepak KONDAPATURI - San Francisco CA, US
International Classification:
G06Q 30/00
G06Q 10/04
G06F 17/18
Abstract:
A method for determining the influence of attributes on a predicted outcome for a customer is described. The method includes computing for each dimension a score based on multiple applications of a non-linear statistical model on different combinations of other pieces of data with attribute values for the customer in that dimension, wherein each attribute was categorized into one of the plurality of dimensions based on a common characteristic for that dimension, wherein each dimension in the plurality of dimensions includes two or more of the attributes and attributes in each dimension share the common characteristic for that dimension, wherein each of the other pieces of data for each of the plurality of dimensions are attribute values independent of the dimension and the customer, wherein the first score for the customer for each of the plurality of dimensions indicates the influence of that dimension on the predicted outcome.

Action Detection And Activity Classification

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US Patent:
20180333611, Nov 22, 2018
Filed:
May 21, 2018
Appl. No.:
15/984732
Inventors:
- Beaverton OR, US
Jordan M. Rice - Portland OR, US
Steven H. Walker - Camas WA, US
Adam S. Carroll - Bend OR, US
Corey C. Dow-Hygelund - Sunriver OR, US
Aaron K. Goodwin - Bend OR, US
James M. Mullin - Bend OR, US
Tye L. Rattenbury - New York NY, US
Joshua M. Rooke-Ley - New York NY, US
John M. Schmitt - Bend OR, US
International Classification:
A63B 24/00
G09B 19/00
G16H 20/40
G09B 5/12
G09B 5/02
G09B 5/00
G06F 19/00
G16H 20/30
G16H 40/67
G16H 80/00
A41D 1/00
A43B 3/00
A63B 71/06
A61B 5/00
A61B 5/11
A61B 5/024
A61B 5/01
Abstract:
Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.

Action Detection And Activity Classification

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US Patent:
20170266493, Sep 21, 2017
Filed:
May 31, 2017
Appl. No.:
15/610176
Inventors:
- Beaverten OR, US
Jordan M. Rice - Portland OR, US
Steven H. Walker - Camas WA, US
Adam S. Carroll - Bend OR, US
Corey C. Dow-Hygelund - Sunriver OR, US
Aaron K. Goodwin - Bend OR, US
James M. Mullin - Bend OR, US
Tye L. Rattenbury - New York NY, US
Joshua M. Rooke-Ley - New York NY, US
John M. Schmitt - Bend OR, US
International Classification:
A63B 24/00
G09B 5/12
A61B 5/11
G06F 19/00
G09B 19/00
A43B 3/00
A61B 5/00
G09B 5/02
G09B 5/00
A61B 5/024
A61B 5/01
A63B 71/06
Abstract:
Activities, actions and events during user performance of physical activity may be detected using various algorithms and templates. Templates may include an arrangement of one or more states that may identify particular event types and timing between events. Templates may be specific to a particular type of activity (e.g., types of sports, drills, events, etc.), user, terrain, time of day and the like.
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Tye Lawrence Rattenbury from Orinda, CA, age ~43 Get Report