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Manikandan K Kesavan

from Campbell, CA
Age ~50
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Also known as:
N Kesavan
Phone and address:
548 Cherry Blossom Ln, Campbell, CA 95008
(408) 340-5563
Connected to:
Aparna Kesavan, 39Jana S Kesavan, 58Nagaraj Kesavan, 49Pavithra Kesavan, 36Santhosh Kesavan, 38Venkitasamy Kesavan, 54Barbara L Kesckes, 50Donald G Kesden, 80Daniel K Kese, 34Bobbie J Kesecker, 55

Manikandan Kesavan Phones & Addresses

  • 548 Cherry Blossom Ln, Campbell, CA 95008 (408) 340-5563
  • 80 Descanso Dr, San Jose, CA 95134
  • 1265 Capitol Ave, San Jose, CA 95132
  • Plainsboro, NJ

Work

Company: Cisco systems Jun 2008 Address: San Jose, CA Position: It engineer

Education

Degree: Bachelor of Engineering School / High School: University of Madras 1992 to 1996 Specialities: Electronics and Communications

Skills

HTML5 • JavaScript • jQuery • jQuery Mobile • jQuery UI • Dojo • Ext JS • AJAX • Web 2.0 Development • PHP • Flash • Flex • Silverlight • Android • iOS • Objective-C • Java • J2EE Application Development • Struts • Velocity • JMS • XML • XSLT • Websphere • IIS • Apache • Tomcat • ActiveX • UML • Web Services • Data Modeling • SOA • BPMN • LDAP • Oracle SQL • Hibernate • Microsoft SQL Server • MySQL • ASP.NET • Streaming Media • Windows Media • Real Media • Quicktime • MPEG-4 • H.264 • AVC • HTTP Live Streaming • Smooth Streaming • CSS3 • Jabber

Industries

Information Technology and Services

Resumes

Resumes

Manikandan Kesavan Photo 1

It Engineer At Cisco Systems

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Position:
IT Engineer at Cisco Systems
Location:
San Francisco Bay Area
Industry:
Information Technology and Services
Work:
Cisco Systems - San Jose, CA since Jun 2008
IT Engineer
e4e Aug 2001 - Jun 2008
Senior Software Engineer
Rapidigm Mar 2000 - Apr 2001
Senior Software Engineer
Ramco Systems Jan 1999 - Mar 2000
Programmer Analyst
T3 Media 1999 - 1999
Software Consultant
Education:
University of Madras 1992 - 1996
Bachelor of Engineering, Electronics and Communications
Skills:
HTML5
JavaScript
jQuery
jQuery Mobile
jQuery UI
Dojo
Ext JS
AJAX
Web 2.0 Development
PHP
Flash
Flex
Silverlight
Android
iOS
Objective-C
Java
J2EE Application Development
Struts
Velocity
JMS
XML
XSLT
Websphere
IIS
Apache
Tomcat
ActiveX
UML
Web Services
Data Modeling
SOA
BPMN
LDAP
Oracle SQL
Hibernate
Microsoft SQL Server
MySQL
ASP.NET
Streaming Media
Windows Media
Real Media
Quicktime
MPEG-4
H.264
AVC
HTTP Live Streaming
Smooth Streaming
CSS3
Jabber

Publications

Us Patents

Contextual Services In A Network Using A Deep Learning Agent

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US Patent:
20200068041, Feb 27, 2020
Filed:
Oct 29, 2019
Appl. No.:
16/666518
Inventors:
- San Jose CA, US
Enzo Fenoglio - Issy-Les-Moulineaux, FR
Plamen Nedeltchev - San Jose CA, US
Manikandan Kesavan - Campbell CA, US
Joseph Friel - Ardmore PA, US
International Classification:
H04L 29/08
H04L 12/24
H04L 12/26
Abstract:
In one embodiment, a device in a network monitors a plurality of traffic flows in the network. The device extracts a plurality of features from the monitored plurality of traffic flows. The device generates a context model by using deep learning and reinforcement learning on the plurality of features extracted from the monitored traffic flows. The device applies the context model to a particular traffic flow associated with a client, to determine a context for the particular traffic flow. The device personalizes data sent to the client from a remote source based on the determined context.

Method And Apparatus For Synthesizing Adaptive Data Visualizations

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US Patent:
20190378506, Dec 12, 2019
Filed:
Nov 9, 2018
Appl. No.:
16/186019
Inventors:
- San Jose CA, US
Eric Loyd Kroll - San Jose CA, US
Manikandan Kesavan - Campbell CA, US
International Classification:
G10L 15/22
G06F 3/16
G06F 3/0484
G10L 15/18
G10L 15/16
Abstract:
A system to dynamically update presentations based on context classification of voice inputs, comprising: a storage device and a processor communicatively coupled to the storage device, wherein the processor executes application code instructions that are stored in the storage device to cause the system to: display a first graphical user interface associated with a first context via a user interface, obtain a first voice input, determine one or more first terms from the first voice input, determine that the first voice input is related to a first context based on the one or more first terms, and in response to determining that the first voice input is related to the first context: modify the first graphical user interface associated with the first context and display the modified first graphical user interface associated with the first context via the user interface.

Speaker Anticipation

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US Patent:
20180376108, Dec 27, 2018
Filed:
Jul 11, 2017
Appl. No.:
15/646470
Inventors:
- San Jose CA, US
Nathan Buckles - McKinney TX, US
Keith Griffin - Galway, IE
Eric Chen - Palo Alto CA, US
Manikandan Kesavan - Campbell CA, US
Plamen Nedeltchev - San Jose CA, US
Hugo Mike Latapie - Long Beach CA, US
Enzo Fenoglio - Issy-les-Moulineaux, FR
International Classification:
H04N 7/15
G06K 9/66
G06K 9/00
G10L 15/18
G10L 25/57
Abstract:
Systems and methods are disclosed for anticipating a video switch to accommodate a new speaker in a video conference comprising a real time video stream captured by a camera local to a first videoconference endpoint is analyzed according to at least one speaker anticipation model. The speaker anticipation model predicts that a new speaker is about to speak. Video of the anticipated new speaker is sent to the conferencing server in response to a request for the video on the anticipated new speaker from the conferencing server. Video of the anticipated new speaker is distributed to at least a second videoconference endpoint.

Deep Learning Bias Detection In Text

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US Patent:
20180246873, Aug 30, 2018
Filed:
Feb 28, 2017
Appl. No.:
15/445059
Inventors:
- San Jose CA, US
Enzo Fenoglio - Issy-les-Moulineaux, FR
Guillaume Sauvage De Saint Marc - Sèvres, FR
Monique Jeanne Morrow - Zurich, CH
Manikandan Kesavan - Campbell CA, US
International Classification:
G06F 17/27
G06N 3/08
Abstract:
In one embodiment, a method includes obtaining text from a user, applying the text to a deep learning neural network to generate a plurality of bias coordinates defining a point in an embedded space, and, in response to determining that at least one of the plurality of bias coordinates exceeds a threshold, providing an indication of bias to the user.

Entity Identification For Enclave Segmentation In A Network

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US Patent:
20180212996, Jul 26, 2018
Filed:
Jan 23, 2017
Appl. No.:
15/412386
Inventors:
- San Jose CA, US
Hugo Latapie - Long Beach CA, US
Enzo Fenoglio - Issy-Les-Moulineaux, FR
Manikandan Kesavan - Campbell CA, US
Deon J. Chatterton - Livermore CA, US
International Classification:
H04L 29/06
G06N 99/00
H04L 29/08
G06F 17/16
Abstract:
In one embodiment, a device in a network identifies a set of network entities. The device determines characteristics of the network entities. The device assigns each of the set of network entities to one or more hyperedges of a hypergraph based on the characteristics. The device applies a security policy to a particular one of the network entities based on the one or more hyperedges of the hypergraph to which the particular network entity is assigned.

Contextual Services In A Network Using A Deep Learning Agent

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US Patent:
20170366425, Dec 21, 2017
Filed:
Jun 17, 2016
Appl. No.:
15/185157
Inventors:
- San Jose CA, US
Enzo Fenoglio - Issy-Les-Moulineaux, FR
Plamen Nedeltchev - San Jose CA, US
Manikandan Kesavan - Campbell CA, US
Joseph Friel - Ardmore PA, US
International Classification:
H04L 12/26
H04L 12/24
H04L 29/08
Abstract:
In one embodiment, a device in a network monitors a plurality of traffic flows in the network. The device extracts a plurality of features from the monitored plurality of traffic flows. The device generates a context model by using deep learning and reinforcement learning on the plurality of features extracted from the monitored traffic flows. The device applies the context model to a particular traffic flow associated with a client, to determine a context for the particular traffic flow. The device personalizes data sent to the client from a remote source based on the determined context.
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Manikandan K Kesavan from Campbell, CA, age ~50 Get Report