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Sergei S Shinkevich

from Bothell, WA
Age ~51
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Also known as:
Sergei R Shinkevich, Sergey S Shinkevich, Sergel S Shinkevich, Sergei H
Related to:
Andrei S ShinkevichIgor A Braginskiy, 56Andrei S Shinkevich, 47Elena V Braginskiy, 54
Connected to:
Nadia Shinkewicz, 32Douglas C Shinkle, 63Glenn F Shinkle, 77Judith D Shinkle, 79Ronald W Shinkle, 76Shawna Shinkle, 51Scott S Shinkman, 46Dana ShinkoLisa A Shinko, 47Rebecca W Shinkoff, 64

Sergei Shinkevich Phones & Addresses

  • Bothell, WA
  • Elkins Park, PA
  • Wauna, WA
  • 4250 Lake Sammamish Pkwy, Redmond, WA 98052 (425) 558-2099
  • Philadelphia, PA
  • Mc Lean, VA
  • Snohomish, WA
  • 24049 40Th Ln SE APT 42, Bothell, WA 98021 (425) 485-0704

Work

Company: Google Jun 2019 Position: Senior software engineer

Education

Degree: Master of Science, Masters School / High School: University of Washington 2009 to 2014 Specialities: Computer Science

Skills

C++ • Computer Science • Windows • C • Programming • Software Development • Software Engineering • Algorithms • Node.js • C# • Asp.net Ajax • Asp.net Mvc • Javascript • Java • Perforce • Tfs • Source Depot • Github • Digital Image Processing • Sailing • Applied Mathematics • Linux • Sql • Visual Studio • Intellij Idea • Windows Azure • Distributed Systems • Win32 Api • Software Design • Oop • Design Patterns • Rest • Multithreading • Scrum • Object Oriented Design • .Net • Scalability • Web Services • Microsoft Sql Server • .Net Framework • Object Oriented Programming • Representational State Transfer

Languages

Russian • English

Ranks

Certificate: Mining Massive Datasets

Interests

Skiing • Economic Empowerment • Health • Snowboarding • Education • Environment • Sailing • Science and Technology • Human Rights • Skiingsnowboardingsailing • Skiing Snowboarding Sailing

Industries

Internet

Resumes

Resumes

Sergei Shinkevich Photo 1

Senior Software Engineer

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Location:
Bothell, WA
Industry:
Internet
Work:
Google
Senior Software Engineer
Groupon
Senior Software Engineer
Microsoft Jul 2013 - May 2015
Senior Software Development Engineer
Expedia, Inc. Jan 2013 - Jun 2013
Senior Software Development Engineer
Microsoft Microsoft Open Technologies Collabera Nov 2011 - Nov 2012
Software Design Engineer 3
Education:
University of Washington 2009 - 2014
Master of Science, Masters, Computer Science The Wharton School 1996 - 1999
Masters, Master of Arts, Management Moscow Institute of Physics and Technology (State University) (Mipt) 1990 - 1996
Master of Science, Masters, Computer Science, Applied Mathematics
Skills:
C++
Computer Science
Windows
C
Programming
Software Development
Software Engineering
Algorithms
Node.js
C#
Asp.net Ajax
Asp.net Mvc
Javascript
Java
Perforce
Tfs
Source Depot
Github
Digital Image Processing
Sailing
Applied Mathematics
Linux
Sql
Visual Studio
Intellij Idea
Windows Azure
Distributed Systems
Win32 Api
Software Design
Oop
Design Patterns
Rest
Multithreading
Scrum
Object Oriented Design
.Net
Scalability
Web Services
Microsoft Sql Server
.Net Framework
Object Oriented Programming
Representational State Transfer
Interests:
Skiing
Economic Empowerment
Health
Snowboarding
Education
Environment
Sailing
Science and Technology
Human Rights
Skiingsnowboardingsailing
Skiing Snowboarding Sailing
Languages:
Russian
English
Certifications:
Mining Massive Datasets
Algorithms: Design and Analysis, Part 2
Coursera

Publications

Us Patents

Correcting Eye Color In A Digital Image

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US Patent:
7675652, Mar 9, 2010
Filed:
Feb 6, 2006
Appl. No.:
11/348064
Inventors:
Denis C. Demandolx - Redmond WA, US
Douglas A. Ricard - Woodinville WA, US
Karthik G. Anbalagan - Bellevue WA, US
Sergei S. Shinkevich - Redmond WA, US
Steve J. White - Seattle WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
H04N 1/407
H04N 1/409
G06T 5/00
G03F 3/08
G06K 9/00
US Classification:
358 326, 358518, 382163, 382167
Abstract:
A method is provided for correcting undesired eye color in a digital image. Color information from the digital image is used to identify an orthogonal color channel. The orthogonal color channel is a channel corresponding to a color that is orthogonal to the undesired color. Information from the orthogonal color channel is used to perform channel mixing. The channel mixing is selectively applied to the original digital image using a masking effect to retain certain portions of the original image and replace certain portions of the original image with the channel-mixed image. This method achieves natural-looking black pupils and preserves specular reflections to conceal the fact that the digital image has been corrected.

Identifying Selected Pixels In A Digital Image

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US Patent:
20050271272, Dec 8, 2005
Filed:
Jun 4, 2004
Appl. No.:
10/861207
Inventors:
Sergei Shinkevich - Redmond WA, US
Assignee:
Microsoft Corporation - Redmond WA
International Classification:
G06K009/34
G06K009/40
US Classification:
382173000, 382254000
Abstract:
The present application provides for accurately identifying a selection of pixels in a digital image. A pixel edgewalk procedure utilizes pixel reference points situated around a pixel, such as at pixel vertices, to determine where to draw an outline to encompass selected pixels. Each pixel reference point is represented by one or more bits in memory that indicate certain information about each particular pixel reference point and pixels surrounding the pixel reference point. Utilizing this information, a determination is made as to where an outline can be drawn according to certain pixel reference points to encompass only pixels that are selected pixels. Resource overhead for determining bit settings for some pixel reference points is reduced by utilizing a bit-wise shift in memory bits allocated to an adjacent pixel reference point.

Techniques For Efficient Sampling For Image Effects

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US Patent:
20120105465, May 3, 2012
Filed:
Oct 28, 2010
Appl. No.:
12/914268
Inventors:
Minmin Gong - Beijing, CN
Sergei Shinkevich - Bothell WA, US
Assignee:
MICROSOFT CORPORATION - Redmond WA
International Classification:
G09G 5/00
US Classification:
345587
Abstract:
Techniques to sample texels efficiently for an image effects are discussed. A technique may include determining a number of texels (kernel size) needed to compute a weighted average for an image effect on an image. The technique may further include selecting at least one mipmap generated by a graphics processing unit (GPU) according to a function of the determined kernel size. The function may also consider a threshold kernel size. The technique may further sampling texels, with the GPU, from the selected mipmap(s), and calculate the weighted average of the sampled texels to produce the image effect. Other embodiments are described and claimed.
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Sergei S Shinkevich from Bothell, WA, age ~51 Get Report