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Milan M Gada

from Redmond, WA
Age ~49
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Also known as:
Milan N Gada, Gada Milan, Milan M Gaba, Milan M Gidwani, Michael Gada
Phone and address:
16110 NE 51St St, Redmond, WA 98052
(425) 372-6661
Related to:
Nisha Gidwani, 48Nisha GadaKalpana Gada, 45Nalin Gada, 47
Connected to:
Alexandra E Gada, 56Ashish D Gada, 35Deepa Gada, 50Kimberly A Gada, 35Paresh G Gada, 54Pooja S Gada, 37Mahantesh Gadad, 41Srikanth Gadadasu, 38Michael Gadaev, 46Rita S Gadag, 59

Milan Gada Phones & Addresses

  • 16110 NE 51St St, Redmond, WA 98052 (425) 372-6661
  • 4850 156Th Ave, Redmond, WA 98052 (425) 882-4778
  • Seattle, WA
  • Kirkland, WA
  • Lawrence, KS
  • Sarasota, FL
  • Kiona, WA
  • 16110 NE 51St St, Redmond, WA 98052

Resumes

Resumes

Milan Gada Photo 1

Milan Gada

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Location:
Seattle, WA
Industry:
Computer Software
Work:
Microsoft Sep 2013 - Dec 2016
Principal Program Manager
Tata Infotech 1997 - 1998
Software Engineer
Education:
The University of Kansas 1998 - 1999
Master of Science, Masters University of Mumbai 1993 - 1997
Bachelor of Engineering, Bachelors, Computer Engineering
Skills:
Software Project Management
Agile Project Management
Software Development
Product Management
Cloud Computing
Windows Azure
Agile Methodologies
Outsourcing
Mobile Devices
Drm
Program Management
Software Design
Silverlight
Software Engineering
Distributed Systems
Testing
Enterprise Software
Management
Saas
Scrum
Microsoft Azure
Software As A Service
Ai
Cloud Applications
Video
Machine Learning
Deep Learning
Neural Networks
Cognitive Neuroscience
Speech Recognition
Face Recognition
Object Detection
Digital Copyright
E Commerce
Technical Product Management
Scalability
Analytics
Project Management
C#
Solution Architecture
Strategic Partnerships
Strategy
Languages:
English
Hindi
Gujarati
Marathi
Milan Gada Photo 2

Milan Gada

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Publications

Us Patents

Managing Digital Rights In A Member-Based Domain Architecture

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US Patent:
20080256646, Oct 16, 2008
Filed:
Apr 12, 2007
Appl. No.:
11/734704
Inventors:
Clifford P. Strom - Sammamish WA, US
Patrik Schnell - Issaquah WA, US
Benjamin Brooks Cutter - Woodinville WA, US
Quintin S. Burns - Fort Mill SC, US
Milan M. Gada - Redmond WA, US
Kevin Lau - Sammamish WA, US
Adrian J. Oney - Woodinville WA, US
Kedarnath A. Dubhashi - Redmond WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
H04L 9/32
US Classification:
726 29
Abstract:
Techniques enable seamless movement and consumption of licensed digital content amongst multiple devices. In some embodiments, these techniques allow establishment of a domain capable of having multiple member devices. Each member device of the domain typically comprises a content-consuming device such as a personal computer, a portable media player, or the like. These techniques enable a license associated with digital content to bind to a domain rather than an individual device. As such, each member device of the domain may contain a domain identity and, with the identity, may consume the content with use of the license and in accordance with policy described in the license. These tools may also enable a member device to join multiple domains and to contain an identification of each of these multiple domains.

Training Set Sufficiency For Image Analysis

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US Patent:
20190347522, Nov 14, 2019
Filed:
May 11, 2018
Appl. No.:
15/977517
Inventors:
- Redmond WA, US
Royi RONEN - Tel Aviv, IL
Ohad JASSIN - Tel Mond, IL
Milan M. GADA - Redmond WA, US
Mor Geva PIPEK - Giv'atayim, IL
International Classification:
G06K 9/62
G06K 9/00
Abstract:
Aspects of the technology described herein improve an object recognition system by specifying a type of picture that would improve the accuracy of the object recognition system if used to retrain the object recognition system. The technology described herein can take the form of an improvement model that improves an object recognition model by suggesting the types of training images that would improve the object recognition model's performance. For example, the improvement model could suggest that a picture of a person smiling be used to retrain the object recognition system. Once trained, the improvement model can be used to estimate a performance score for an image recognition model given the set characteristics of a set of training of images. The improvement model can then select a feature of an image, which if added to the training set, would cause a meaningful increase in the recognition system's performance.

Multi-Tenant License Enforcement Across Job Requests

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US Patent:
20140201752, Jul 17, 2014
Filed:
Jan 14, 2013
Appl. No.:
13/741182
Inventors:
- Redmond WA, US
Samuel Ng - Bellevue WA, US
Jake Carver Swenson - Seattle WA, US
Milan Gada - Redmond WA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
G06F 9/52
G06F 9/50
US Classification:
718104
Abstract:
Scheduling job request submitted by multiple tenants in a manner that honors multiple software license agreements for the multiple tenants. A queue persistently stores job requests that await scheduling. A job state tracking component persistently tracks a state of the job requests, and perhaps provides job requests into the queue. A software license agreement enforcer reviews the job requests in the queue, selects one or more job requests should be scheduled next based on the license agreements, and provide the selected job requests to a resource manager. A subscriber/publisher pool may be used to the various components to communicate. This decouples the communication from being a simple one-to-one correspondence, but instead allows communication from a component of one type to a component of the other type, whichever instance of those components happens to be operating.
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Milan M Gada from Redmond, WA, age ~49 Get Report