Jie A Yu from Union City, CA, age 58

Resumes

Senior Staff Software Engineer

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Location:

4644 Strawberry Park Dr, San Jose, CA 95129

Industry:

Computer Software

Work:

D2Iq
Chief Architect
Mesosphere, Inc.
Principal Software Engineer
Twitter Aug 2013 - Dec 2015
Senior Software Engineer
Twitter May 2012 - Aug 2012
Summer Intern
Nec Laboratories America, Inc. May 2011 - Aug 2011
Summer Research Intern

Education:

University of Michigan 2008 - 2010
Master of Science, Masters, Computer Science, Engineering, Computer Science and Engineering Fudan University 2003 - 2007
Bachelor of Engineering, Bachelors, Software Engineering University of Michigan 1995 - 2001
Doctorates, Doctor of Philosophy, Computer Science, Engineering, Computer Science and Engineering, Philosophy

Skills:

Distributed Systems
Python
Java
Linux Kernel
Parallel Programming
C/C++
Event Driven Programming
Android
Java Virtual Machine

Interests:

Algorithms
Distributed Systems
Computer Science Research

Languages:

Mandarin

Jie Yu

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Jie Yu

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Assistant To The Chairman

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Work:

Assistant To the Chairman

Jie Yu

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Jie Yu

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Jie Yu

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Location:

San Francisco Bay Area

Industry:

Computer Networking

Jie Yu

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Location:

United States

Business Records

Name / Title

Company / Classification

Phones & Addresses

Jie Yu
Owner, Principal

Body Care Therapy Massage
Misc Personal Services

16375 Monterey St, Morgan Hill, CA 95037
(408) 778-8210

Jie Hua Yu
Vice-Chairman

San Francisco Unified School District
High School

2162 24 Ave, San Francisco, CA 94116
(415) 759-2700

Jie Yu
Director

AUSTIN AMTECH, INC

10322 Alpine Dr APT C, Cupertino, CA 95014
11506 Heathrow Dr, Austin, TX 78759

Publications

Us Patents

Rf Impedance Matching Networks For Substrate Processing Platform

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US Patent:

20220359161, Nov 10, 2022

Filed:

May 7, 2021

Appl. No.:

17/314173

Inventors:

- Santa Clara CA, US
Krishna Kumar KUTTANNAIR - San Jose CA, US
Jie YU - Irvine CA, US
Kartik RAMASWAMY - San Jose CA, US
Yang YANG - Cupertino CA, US

International Classification:

H01J 37/32
H03H 7/40

Abstract:

Methods and apparatus using a matching network for processing a substrate are provided herein. For example, a matching network configured for use with a plasma processing chamber comprises a local controller connectable to a system controller of the plasma processing chamber, a first motorized capacitor connected to the local controller, a second motorized capacitor connected to the first motorized capacitor, a first sensor at an input of the matching network and a second sensor at an output of the matching network for obtaining in-line RF voltage, current, phase, harmonics, and impedance data, respectively, and an Ethernet for Control Automation Technology (EtherCAT) communication interface connecting the local controller to the first motorized capacitor, the second motorized capacitor, the first sensor, and the second sensor.

Devices, Systems, And Methods For Generating Multi-Modal Images Of A Synthetic Scene

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US Patent:

20180077376, Mar 15, 2018

Filed:

Apr 21, 2017

Appl. No.:

15/494352

Inventors:

- Tokyo, JP
Sandra Skaff - Mountain View CA, US
Jie Yu - Santa Clara CA, US

International Classification:

H04N 5/445
G06T 7/70

Abstract:

Devices, systems, and methods obtain an object model, add the object model to a synthetic scene, add a texture to the object model, add a background plane to the synthetic scene, add a support plane to the synthetic scene, add a background image to one or both of the background plane and the support plane, and generate a pair of images based on the synthetic scene, wherein a first image in the pair of images is a depth image of the synthetic scene, and wherein a second image in the pair of images is a color image of the synthetic scene.

Devices, Systems, And Methods For Detecting Unknown Objects

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US Patent:

20170270389, Sep 21, 2017

Filed:

Mar 13, 2017

Appl. No.:

15/457067

Inventors:

- Tokyo, JP
Jie Yu - Santa Clara CA, US
Francisco Imai - San Jose CA, US

International Classification:

G06K 9/62

Abstract:

Devices, systems, and methods obtain a region of an image; generate known-object scores for the region using known-object detectors, wherein each known-object detector of the known-object detectors detects objects in a respective object class; determine a likelihood that the region includes a complete object; and determine a likelihood that the region includes an unknown object based on the likelihood that the region includes a complete object and on the known-object scores.

Devices, Systems, And Methods For Knowledge-Based Inference For Material Recognition

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US Patent:

20160321505, Nov 3, 2016

Filed:

Apr 29, 2015

Appl. No.:

14/699344

Inventors:

- Tokyo, JP
Jie Yu - San Jose CA, US

International Classification:

G06K 9/00
G06K 9/62
G06K 9/46

Abstract:

Systems, devices, and methods for material recognition extract one or more features from a patch in an image of a scene; generate an initial prediction of a material category of the patch from candidate material categories based on the one or more features; identify a scene in the image; identify one or more objects in the image; obtain a first relationship model of two or more of the one or more features, the candidate material categories, the one or more objects, and the scene; and make a refined prediction of the material category of the patch based on the initial prediction of the material category and on the first relationship model.

Devices, Systems, And Methods For Pairwise Multi-Task Feature Learning

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US Patent:

20160321522, Nov 3, 2016

Filed:

Sep 4, 2015

Appl. No.:

14/845982

Inventors:

- Tokyo, JP
Jie Yu - Santa Clara CA, US

International Classification:

G06K 9/66
G06N 3/08
G06N 3/04

Abstract:

Systems, method, and devices for pairwise multi-task feature learning are described. The systems obtain a set of digital images, obtain a neural network, and select a pair of digital images, which includes a first image and a second image. Also, the systems forward propagate the first image through a first copy of the neural network, thereby generating a first output, and the systems forward propagate the second image through a second copy of the neural network, thereby generating a second output. Furthermore, the systems calculate a gradient of a joint loss function at a pairwise-constraint layer of the neural network based on the first output, on the second output, and on a target. Additionally, the systems modify the neural network based on the gradient.

Videos & Images