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Songxiang Gu

from Boston, MA
Age ~46
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Related to:
Songxian Gu
Connected to:
Alexander Gu, 33An Gu, 30Chaoning Gu, 62Crystal Gu, 33Fred Y Gu, 58Grace Y Gu, 64Hanna Gu, 44Helena Y Gu, 32Hong C Gu, 57Howard H Gu, 66

Songxiang Gu Phones & Addresses

  • 65 E India Row APT 25H, Boston, MA 02110
  • Sunnyvale, CA
  • San Bruno, CA
  • Milpitas, CA
  • Mount Rainier, MD
  • Worcester, MA
  • Seattle, WA

Work

Company: Amazon.com Oct 2012 Address: Seattle, WA Position: Software engineer

Education

Degree: Ph.D School / High School: Worcester Polytechnic Institute 2004 to 2009 Specialities: Computer Science

Skills

Computer Vision • C++/Java Programming • Digital Image Processing • Camera Calibration • Computational Model Observer • Mathematics and Statistics • Matlab Programming • Use VTK/ITK for Image Analysis • CUDA GPGPU Programming • CT Dose Estimation

Interests

Heart Models, Segmentation, Radiation Do...

Industries

Computer Software

Resumes

Resumes

Songxiang Gu Photo 1

Software Engineer At Amazon.com

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Position:
Software Engineer at Amazon.com
Location:
Seattle, Washington
Industry:
Computer Software
Work:
Amazon.com - Seattle, WA since Oct 2012
Software Engineer
Food and Drug Administration - Silver Spring, MD Aug 2009 - Sep 2012
Visiting Scientist
EMC May 2008 - Aug 2008
Internship
Worcester Polytechnic Institute Jul 2004 - May 2008
Research Assistant
VIA-tech Apr 2003 - Jun 2004
Software Engineer
Education:
Worcester Polytechnic Institute 2004 - 2009
Ph.D, Computer Science Zhejiang University 2000 - 2003
Master of Science, Computer Science Zhejiang University 1996 - 2000
Bachelor of Science, Computer Science
Skills:
Computer Vision
C++/Java Programming
Digital Image Processing
Camera Calibration
Computational Model Observer
Mathematics and Statistics
Matlab Programming
Use VTK/ITK for Image Analysis
CUDA GPGPU Programming
CT Dose Estimation
Interests:
Heart Models, Segmentation, Radiation Dose Estimation, Image Reconstruction, Camera Calibration, CT Reconstruction, Image Quality Assessment, Image Analysis, Monte Carlo Simulation

Publications

Us Patents

Early Feedback Of Schematic Correctness In Feature Management Frameworks

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US Patent:
20190325258, Oct 24, 2019
Filed:
Apr 20, 2018
Appl. No.:
15/958997
Inventors:
- Redmond WA, US
Ke Wu - Sunnyvale CA, US
Priyanka Gariba - San Mateo CA, US
Grace W. Tang - Los Altos CA, US
Yangchun Luo - Sunnyvale CA, US
Songxiang Gu - Sunnyvale CA, US
Bee-Chung Chen - San Jose CA, US
Assignee:
Microsoft Technology Licensing, LLC - Redmond WA
International Classification:
G06K 9/62
G06F 15/18
Abstract:
The disclosed embodiments provide a system for processing data. During operation, the system obtains feature configurations for a set of features and a command for inspecting a data set that is produced using the feature configurations. Next, the system obtains, from the feature configurations, one or more anchors containing metadata for accessing the set of features in an environment and a join configuration for joining a feature with one or more additional features. The system then uses the anchors to retrieve feature values of the features and zips the feature values according to the join configuration without matching entity keys associated with the feature values. Finally, the system outputs the zipped feature values in response to the command.

Common Feature Protocol For Collaborative Machine Learning

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US Patent:
20170109652, Apr 20, 2017
Filed:
Feb 17, 2016
Appl. No.:
15/046199
Inventors:
- Mountain View CA, US
Xu Miao - Sunnyvale CA, US
Lance M. Wall - San Francisco CA, US
Joel D. Young - Milpitas CA, US
Eric Huang - San Francisco CA, US
Songxiang Gu - Sunnyvale CA, US
Da Teng - Sunnyvale CA, US
Chang-Ming Tsai - Fremont CA, US
Sumit Rangwala - Fremont CA, US
Assignee:
LinkedIn Corporation - Mountain View CA
International Classification:
G06N 99/00
G06F 17/30
Abstract:
The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.
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Songxiang Gu from Boston, MA, age ~46 Get Report