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Veena Basavaraj

from San Francisco, CA
Age ~44
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Related to:
Nataraj Basavaraj
Connected to:
Kavita B Basavaraju, 63Lakshmi Basavaraju, 45Madesha B Basavaraju, 51Pramod Basavaraju, 41Vasanthi B Basavaraju, 57Alberto A Basave, 62Sabrina G Basave, 42Kathleen M Basbagill, 51Mehmet E Basbug, 35Matt Basca, 43

Veena Basavaraj Phones & Addresses

  • San Francisco, CA
  • Germantown, MD
  • Sunnyvale, CA
  • Vienna, VA
  • Arlington, VA
  • Blacksburg, VA
  • 18053 Cottage Garden Dr, Germantown, MD 20874

Resumes

Resumes

Veena Basavaraj Photo 1

Director Of Engineering

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Location:
1262 Dolores St, San Francisco, CA 94110
Industry:
Real Estate
Work:
Linkedin - San Francisco Bay Area since Jun 2010
Staff Software Engineer
Blackboard - Washington D.C. Metro Area Aug 2006 - Apr 2010
Senior Software Engineer
ANZ - Melbourne Area, Australia May 2002 - Jun 2004
Software Engineer
Education:
Virginia Tech 2004 - 2006
M.S., Computer Science and Applications National Institute of Engineering 1998 - 2002
B.E, Computer Science and Engineering
Skills:
Javascript
Java
Web Applications
Distributed Systems
Awesomeness
Scalability
Rest
Design Patterns
Technical Leadership
Node.js
Android
Objective C
Ios Development
Interests:
Science and Technology
Education
Awards:
Hackday Winner: Best of the show
linkedIn
Building a easy to learn and play tool for web-development with dust and JSON
Veena Basavaraj Photo 2

Veena Basavaraj

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Veena Basavaraj Photo 3

Veena Basavaraj

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Publications

Us Patents

Optimization Techniques For Artificial Intelligence

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US Patent:
20200234002, Jul 23, 2020
Filed:
Nov 21, 2018
Appl. No.:
16/198453
Inventors:
- SINGAPORE, SG
Schuyler D. Erle - San Francisco CA, US
Jason Brenier - Oakland CA, US
Paul A. Tepper - San Francisco CA, US
Tripti Saxena - Cupertino CA, US
Gary C. King - Los Altos CA, US
Jessica D. Long - San Francisco CA, US
Brendan D. Callahan - Philadelphia PA, US
Tyler J. Schnoebelen - San Francisco CA, US
Stefan Krawczyk - Menlo Park CA, US
Veena Basavaraj - San Francisco CA, US
International Classification:
G06F 40/169
G06F 40/221
G06F 40/137
G06F 40/42
G06F 40/40
G06F 40/30
G06N 20/00
G06F 3/0482
G06Q 50/00
G06F 16/2453
G06F 16/332
G06F 16/951
G06F 16/36
G06F 16/28
G06F 16/242
G06F 16/93
G06F 16/35
Abstract:
Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: selecting from a pool of documents, a first set of documents to be annotated; receiving annotations of the first set of documents elicited by first human readable prompts; training a natural language model using the annotated first set of documents; determining documents in the pool having uncertain natural language processing results according to the trained natural language model and/or the received annotations; selecting from the pool of documents, a second set of documents to be annotated comprising documents having uncertain natural language processing results; receiving annotations of the second set of documents elicited by second human readable prompts; and retraining a natural language model using the annotated second set of documents.

Architectures For Natural Language Processing

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US Patent:
20200034737, Jan 30, 2020
Filed:
Feb 28, 2019
Appl. No.:
16/289481
Inventors:
- SINGAPORE, SG
Schuyler D. Erle - San Francisco CA, US
Christopher Walker - San Francisco CA, US
Sarah K. Luger - San Francisco CA, US
Jason Brenier - Oakland CA, US
Gary C. King - Los Altos CA, US
Paul A. Tepper - San Francisco CA, US
Ross Mechanic - San Francisco CA, US
Andrew Gilchrist-Scott - Berkeley CA, US
Jessica D. Long - San Francisco CA, US
James B. Robinson - San Francisco CA, US
Brendan D. Callahan - Philadelphia PA, US
Michelle Casbon - San Antonio TX, US
Ujjwal Sarin - San Francisco CA, US
Aneesh Nair - Fremont CA, US
Veena Basavaraj - San Francisco CA, US
Tripti Saxena - Cupertino CA, US
Edgar Nunez - Union City CA, US
Martha G. Hinrichs - San Francisco CA, US
Haley Most - San Francisco CA, US
Tyler J. Schnoebelen - San Francisco CA, US
Assignee:
AIPARC HOLDINGS PTE. LTD. ` - SINGAPORE
International Classification:
G06N 20/00
G06F 17/28
Abstract:
Systems are presented for generating a natural language model. The system may comprise a database module, an application program interface (API) module, a background processing module, and an applications module, each stored on the at least one memory and executable by the at least one processor. The system may be configured to generate the natural language model by: ingesting training data, generating a hierarchical data structure, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document, receiving the annotation based on the annotation prompt, and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Methods For Generating Natural Language Processing Systems

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US Patent:
20190303428, Oct 3, 2019
Filed:
Nov 5, 2018
Appl. No.:
16/181102
Inventors:
- SINGAPORE, SG
Schuyler D. Erle - San Francisco CA, US
Christopher Walker - San Francisco CA, US
Sarah K. Luger - San Francisco CA, US
Jason Brenier - Oakland CA, US
Gary C. King - Los Altos CA, US
Paul A. Tepper - San Francisco CA, US
Ross Mechanic - San Francisco CA, US
Andrew Gilchrist-Scott - Berkeley CA, US
Jessica D. Long - San Francisco CA, US
James B. Robinson - San Francisco CA, US
Brendan D. Callahan - Philadelphia PA, US
Michelle Casbon - San Antonio TX, US
Ujjwal Sarin - San Francisco CA, US
Aneesh Nair - Fremont CA, US
Veena Basavaraj - San Francisco CA, US
Tripti Saxena - Cupertino CA, US
Edgar Nunez - Union City CA, US
Martha G. Hinrichs - San Francisco CA, US
Haley Most - San Francisco CA, US
Tyler Schnoebelen - San Francisco CA, US
International Classification:
G06F 17/24
G06F 16/332
G06F 17/28
G06F 16/28
G06F 16/93
G06F 16/35
G06F 16/2453
G06F 16/951
G06F 16/242
G06F 17/22
G06Q 50/00
G06F 17/27
G06F 3/0482
G06F 16/36
Abstract:
Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Methods For Generating Natural Language Processing Systems

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US Patent:
20160162456, Jun 9, 2016
Filed:
Dec 9, 2015
Appl. No.:
14/964517
Inventors:
Robert J. Munro - San Franciso CA, US
Schuyler D. Erle - San Francisco CA, US
Christopher Walker - San Francisco CA, US
Sarah K. Luger - San Francisco CA, US
Jason Brenier - Oakland CA, US
Gary C. King - Los Altos CA, US
Paul A. Tepper - San Francisco CA, US
Ross Mechanic - San Francisco CA, US
Andrew Gilchrist-Scott - Berkeley CA, US
Jessica D. Long - San Francisco CA, US
James B. Robinson - San Francisco CA, US
Brendan D. Callahan - Philadelphia PA, US
Michelle Casbon - San Antonio TX, US
Ujjwal Sarin - San Francisco CA, US
Aneesh Nair - Fremont CA, US
Veena Basavaraj - San Francisco CA, US
Tripti Saxena - Cupertino CA, US
Edgar Nunez - Union City CA, US
Martha G. Hinrichs - San Francisco CA, US
Haley Most - San Francisco CA, US
Tyler J. Schnoebelen - San Francisco CA, US
Assignee:
Idibon, Inc. - San Francisco CA
International Classification:
G06F 17/24
G06F 17/22
G06F 17/28
Abstract:
Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Optimization Techniques For Artificial Intelligence

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US Patent:
20160162457, Jun 9, 2016
Filed:
Dec 9, 2015
Appl. No.:
14/964520
Inventors:
Robert J. Munro - San Franciso CA, US
Schuyler D. Erle - San Francisco CA, US
Jason Brenier - Oakland CA, US
Paul A. Tepper - San Francisco CA, US
Tripti Saxena - Cupertino CA, US
Gary C. King - Los Altos CA, US
Jessica D. Long - San Francisco CA, US
Brendan D. Callahan - Philadelphia PA, US
Tyler J. Schnoebelen - San Francisco CA, US
Stefan Krawczyk - Menlo Park CA, US
Veena Basavaraj - San Francisco CA, US
Assignee:
Idibon, Inc. - San Francisco CA
International Classification:
G06F 17/24
G06F 17/28
Abstract:
Methods, apparatuses and computer readable medium are presented for generating a natural language model. A method for generating a natural language model comprises: selecting from a pool of documents, a first set of documents to be annotated; receiving annotations of the first set of documents elicited by first human readable prompts; training a natural language model using the annotated first set of documents; determining documents in the pool having uncertain natural language processing results according to the trained natural language model and/or the received annotations; selecting from the pool of documents, a second set of documents to be annotated comprising documents having uncertain natural language processing results; receiving annotations of the second set of documents elicited by second human readable prompts; and retraining a natural language model using the annotated second set of documents.

Front-End Tool For Displaying Diagnostic Information To Facilitate Web Page Development

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US Patent:
20140325399, Oct 30, 2014
Filed:
Apr 30, 2013
Appl. No.:
13/874297
Inventors:
- Mountain View CA, US
Veena Basavaraj - Mountain View CA, US
Johnathan Leppert - Mountain View CA, US
Assignee:
LinkedIn Corporation - Mountain View CA
International Classification:
G06F 17/30
US Classification:
715760
Abstract:
The disclosed embodiments relate to a system that displays diagnostic information to facilitate web page development. While a web page is being assembled at a front-end system using data gathered from one or more back-end systems, the system accumulates metadata associated with the assembly process. Next, the system renders the web page using the gathered data. Finally, the system sends the rendered web page along with the metadata to a browser to be displayed to a user, wherein the browser is configured to selectively display the metadata when the web page is displayed.
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Veena Basavaraj from San Francisco, CA, age ~44 Get Report