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Software Engineer In Large Scale Machine Learning
Qualcomm Jun 2015 - Sep 2016
Software Engineer
The University of Texas at Austin Aug 2013 - May 2015
Teaching Assistant
Qualcomm May 2014 - Aug 2014
Software Engineer Internship
Wireless Networking and Communications Group Aug 2013 - Dec 2013
Graduate Student Researcher
Education:
The University of Texas at Austin 2013 - 2015
Master of Science, Masters, Computer Engineering
Uc San Diego 2012 - 2014
Master of Science, Masters, Computer Engineering
The Hong Kong University of Science and Technology 2009 - 2013
Bachelor of Engineering, Bachelors, Engineering
Tianjin University 2008 - 2012
Bachelors, Bachelor of Science, Electronics Engineering
University of Michigan 2011 - 2011
Dalian No.24 High School
The Hong Kong University of Science and Technology
Skills:
C++ C Python Algorithms Java Machine Learning R Computer Architecture Go Javascript Lte 3Gpp Mongodb Php Mysql Cdma Wcdma Assembly Language Matlab Computer Networking Verilog Embedded Systems Circuit Design Cuda Linux Programming Perl Operating Systems Opencl Opengl Glsl
- Redmond WA, US Brian J. TOLENO - Cupertino CA, US Sahar VILAN - Atlit, IL Han LI - Sammamish WA, US Bo DAN - Redmond WA, US
Assignee:
Microsoft Technology Licensing, LLC - Redmond WA
International Classification:
H01L 23/427 H01L 21/48 F28D 15/02
Abstract:
A vapor chamber device is provided. The vapor chamber device includes a heat-generating component, a first metallic layer deposited directly on the heat-generating component, and a second metallic layer deposited so as to contact the first metallic layer at a perimeter. The first and second metallic layers fully enclose an internal void formed therein.
Machine Learning Models For Evaluating Entities In A High-Volume Computer Network
- Walldorf, DE Fuming Wu - Palo Alto CA, US Julio Navas - Concord CA, US Ajain Kuzhimattathil - Chicago IL, US Hanxiang Chen - Burnaby, CA Nazanin Zaker Habibabadi - Sunnyvale CA, US Omar Rahman - San Jose CA, US Han Li - Santa Clara CA, US
International Classification:
G06N 99/00
Abstract:
In an example, a machine learning algorithm is used to train an entity risk evaluation model to output an entity risk score based on transaction data in a computer network. Entity risk scores for various entities may be stored in a database, and retrieved and displayed upon user interaction with one or more reports involving corresponding entities.