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Vinodkumar Prabhakaran Phones & Addresses

  • San Francisco, CA
  • Menlo Park, CA
  • Sunnyvale, CA
  • Bronx, NY
  • New York, NY
  • Schaumburg, IL
  • Roselle, IL

Work

Company: Center for computational learning systems, columbia university Sep 2009 Position: Graduate research assistant

Education

Degree: Master of Philosophy (MPhil) School / High School: Columbia University in the City of New York 2010 to 2012 Specialities: Computer Science

Skills

Machine Learning • Algorithms • Information Retrieval • Computer Science • Natural Language Processing • Java • C++ • Information Extraction • Python • Artificial Intelligence • Latex • Perl • C • Matlab • Computational Linguistics • Linux • Text Mining • Data Mining • Pattern Recognition • Databases • Software Engineering • Data Structures • Programming • Research • Data Analysis

Languages

English • Malayalam • Hindi

Industries

Research

Resumes

Resumes

Vinodkumar Prabhakaran Photo 1

Research Scientist

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Location:
San Francisco, CA
Industry:
Research
Work:
Center for Computational Learning Systems, Columbia University since Sep 2009
Graduate Research Assistant

Avaya - Basking Ridge, NJ Jun 2012 - Sep 2012
Research Scientist Intern

Siemens Corporate Research - Princeton, NJ Jun 2011 - Aug 2011
Summer Research Intern

Columbia Univ Feb 2009 - Aug 2009
Research Programmer

TATA Consultancy Services Limited (TCSL) Aug 2004 - Jul 2008
Assistant Systems Engineer
Education:
Columbia University in the City of New York 2010 - 2012
Master of Philosophy (MPhil), Computer Science
Columbia University in the City of New York 2008 - 2009
Master of Science (MS), Computer Science
Cochin University of Science And Technology, College of Engineering, Chengannur 2000 - 2004
B-Tech, Computer Science and Engineering
Skills:
Machine Learning
Algorithms
Information Retrieval
Computer Science
Natural Language Processing
Java
C++
Information Extraction
Python
Artificial Intelligence
Latex
Perl
C
Matlab
Computational Linguistics
Linux
Text Mining
Data Mining
Pattern Recognition
Databases
Software Engineering
Data Structures
Programming
Research
Data Analysis
Languages:
English
Malayalam
Hindi

Publications

Us Patents

System For Generating A Medical Knowledge Base

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US Patent:
20130066870, Mar 14, 2013
Filed:
May 24, 2012
Appl. No.:
13/479363
Inventors:
Swapna Somasundaran - Lawrenceville NJ, US
Vinodkumar Prabhakaran - Bronx NY, US
Vinay Damodar Shet - Princeton NJ, US
Kateryna Tymoshenko - Trento, IT
Mathäus Dejori - NY NY, US
International Classification:
G06F 17/30
US Classification:
707737, 707E17091
Abstract:
A system generates medical knowledge base information by searching at least one repository of medical information to identify sentences including a received medical term. A data processor searches the identified sentences to identify sentences including a medical term different to the received term in response to a predetermined repository of medical terms and excludes sentences without a term different to the received term, to provide remaining multiple term sentences. The data processor groups different terms of individual sentences of the multiple term sentences to provide grouped terms, determines whether a medically valid relationship occurs between different terms of an individual group of terms of the grouped terms by using predetermined sentence structure and syntax rules and outputs data representing grouped terms having a medically valid relationship.

System And Method For Analysis Of Power Relationships And Interactional Dominance In A Conversation Based On Speech Patterns

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US Patent:
20140337034, Nov 13, 2014
Filed:
Sep 25, 2013
Appl. No.:
14/036868
Inventors:
- Basking Ridge NJ, US
Vinodkumar PRABHAKARAN - Bronx NY, US
Doree Duncan SELIGMANN - New York NY, US
Assignee:
Avaya Inc. - Basking Ridge NJ
International Classification:
G10L 25/48
US Classification:
704270
Abstract:
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for evaluating dominance of participants in a conversation or interaction. An example system configured to practice the method first receives interaction data involving a plurality of participants, and can identify a type of interaction based on the interaction data. The system can parse the interaction data to identify dialog turns, and extract, from the interaction data and dialog turns, a plurality of participant features, wherein the plurality of participant features is selected based on the type of interaction. Then the system can generate, for each of the plurality of participants, a power index based on the respective participant features.
Vinodkumar Prabhakaran from San Francisco, CA, age ~41 Get Report