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Yu U Zhao, 656063 Southcrest Way, Saint Louis, MO 63129

Yu Zhao Phones & Addresses

6063 Southcrest Way, Saint Louis, MO 63129    314-8453464   

Clayton, MO   

Washington, DC   

1313 6Th St SE, Minneapolis, MN 55414    612-3783158   

Rowland Heights, CA   

Woodside, NY   

Pomona, CA   

Rowland Heights, CA   

Work

Company: English language institute Jul 2013 Position: Esl tutoring-volunteer

Education

School / High School: American University- Washington, DC May 2014 Specialities: M.A. in Teaching English to Speakers of Other Languages

Mentions for Yu U Zhao

Career records & work history

Medicine Doctors

Yu Anita Zhao

Specialties:
Occupational Medicine
Work:
Kaiser Permanente Medical GroupKaiser Permanente Medical Center
10050 N Wolfe Rd STE SW1190, Cupertino, CA 95014
408-2366160 (phone) 408-2366152 (fax)
Site
Education:
Medical School
China Med Univ, Shenyang City, Liaoning, China
Graduated: 1999
Languages:
English, Spanish
Description:
Dr. Zhao graduated from the China Med Univ, Shenyang City, Liaoning, China in 1999. She works in Cupertino, CA and specializes in Occupational Medicine. Dr. Zhao is affiliated with Kaiser Permanente Oakland Medical Center.
Yu Zhao Photo 1

Yu Zhao

Specialties:
Preventive Medicine

Resumes & CV records

Resumes

Yu Zhao Photo 43

Yutao (Tony) Zhao

Position:
Engineer/Analyst at SAIC
Location:
Washington D.C. Metro Area
Industry:
Defense & Space
Work:
SAIC since Jun 2010
Engineer/Analyst
Education:
Georgia Institute of Technology 2008 - 2009
Master of Science, Operations Research
Cornell University 2004 - 2008
Bachelor of Science, Engineering Physics
Skills:
Computer Programming, Computational Optimization, Probability & Statistics, Bayesian Statistics, Computational Statistics, Stochastic Process, Simulation & Modeling, and Data Mining & Statistical Learning
Yu Zhao Photo 44

Yu Zhao

Location:
Washington, DC
Industry:
Higher Education
Education:
American University 2012 - 2014
Masters, Master of Arts, Teaching, English
Skills:
Microsoft Office, Microsoft Excel, Microsoft Word, Powerpoint, Research, Photoshop, Social Media, Teamwork, English
Yu Zhao Photo 45

Software Sevurity Intern

Location:
Washington, DC
Industry:
Telecommunications
Work:
China Mobile Jul 2014 - Oct 2014
Software Sevurity Intern
Education:
The George Washington University - School of Engineering & Applied Science 2015 - 2017
Master of Science, Masters, Electrical Engineering
Beijing University of Technology 2011 - 2015
Bachelors, Bachelor of Science, Engineering
Skills:
Matlab, Ms Office, Java, C, C++
Languages:
Chinese
English
Yu Zhao Photo 46

Examiner

Location:
Washington, DC
Industry:
Computer & Network Security
Work:
Uspto
Examiner
Education:
George Mason University 2002 - 2005
Yu Zhao Photo 47

Yu Zhao

Yu Zhao Photo 48

Yu Zhao

Yu Zhao Photo 49

Yu Zhao - Washington, DC

Work:
English Language Institute Jul 2013 to Dec 2013
ESL Tutoring-Volunteer
ESL program, Carlos Rosario International Public Charter School - Washington, DC Sep 2013 to Nov 2013 ESL program, Chevy Chase Presbyterian Church - Washington, DC Jan 2013 to May 2013 Chinese Student & Scholar Association - Washington, DC Sep 2012 to Dec 2012 Chinese Student & Scholar Association - Washington, DC Sep 2012 to Dec 2012
Volunteer
Baotou Teachers' College - Baotou, CN Aug 2011 to Jan 2012
Teaching Assistant
Education:
American University - Washington, DC May 2014
M.A. in Teaching English to Speakers of Other Languages
Jinan University - Guangzhou, CN Jun 2011
B.A. in Chinese Language and Literature
Yu Zhao Photo 50

Yu Zhao

Location:
United States
Education:
Saint John's University

Publications & IP owners

Us Patents

Object Detection Machine Learning

US Patent:
2020028, Sep 10, 2020
Filed:
Sep 24, 2019
Appl. No.:
16/581621
Inventors:
- Costa Mesa CA, US
Peter Nguyen - Costa Mesa CA, US
David Kettler - Bellevue WA, US
Yu Zhao - Costa Mesa CA, US
International Classification:
G06K 9/62
G06T 7/136
G06K 9/00
G06N 3/08
G06T 11/20
Abstract:
Provided herein are embodiments of systems and methods for classifying one or more objects in an image. One of the methods includes: receiving object classification results of the image from one or more classification engines, the object classification results comprise classification of one or more objects and confidence scores associated with the one or more objects; aggregating the object classification results from the one or more classification engines to generate a list of confidence scores associated with each of the one or more objects, the list of confidence scores comprises one or more confidence scores from one or more classification engines; calculating an overall certainty score for each of the one or more objects based at least on the list of confidence scores; and generating a first orchestrated classification result based the overall certainty score for each of the one or more object.

System And Method For Neural Network Orchestration

US Patent:
2020007, Mar 5, 2020
Filed:
Mar 6, 2019
Appl. No.:
16/294781
Inventors:
- Costa Mesa CA, US
Peter Nguyen - Costa Mesa CA, US
David Kettler - Bellevue WA, US
Karl Schwamb - Mission Viejo CA, US
Yu Zhao - Irvine CA, US
International Classification:
G10L 15/32
G06F 17/27
G10L 15/02
G10L 15/18
G06N 3/08
G10L 15/16
G10L 15/30
G06N 5/00
G06N 20/00
G06N 3/04
Abstract:
Methods and systems for training an engine prediction neural network is disclosed. One of the methods can include: extracting image features of a first ground truth image using outputs of one or more layers of an image classification neural network; classifying the first ground truth image using a plurality of candidate neural networks; determining a classification accuracy score of a classification result of the first ground truth image for each candidate neural network of the plurality of candidate neural networks; and training the engine prediction neural network to predict the best candidate engine by associating the image features of the first ground truth image with the classification accuracy score of each candidate neural network.

System And Method For Neural Network Orchestration

US Patent:
2020006, Feb 27, 2020
Filed:
Feb 27, 2019
Appl. No.:
16/287892
Inventors:
- Costa Mesa CA, US
Peter Nguyen - Costa Mesa CA, US
David Kettler - Bellevue WA, US
Karl Schwamb - Mission Viejo CA, US
Yu Zhao - Irvine CA, US
International Classification:
G10L 15/32
G06F 17/27
G10L 15/02
G10L 15/18
G06N 3/08
G10L 15/16
G10L 15/30
G06N 5/00
G06N 20/00
G06N 3/04
Abstract:
Methods and systems for classifying a multimedia file using interclass data is disclosed. One of the methods can use classification results from one or more engines of different classes to select a different engine for the original classification task. For example, given an audio segment with associated metadata and image data, the disclosed interclass method can use the classification results from a topic classification of metadata and/or an image classification result of the image data as inputs for selecting a new transcription engine to transcribe the audio segment.

System And Method For Neural Network Orchestration

US Patent:
2020005, Feb 20, 2020
Filed:
Feb 22, 2019
Appl. No.:
16/283222
Inventors:
- Costa Mesa CA, US
Peter Nguyen - Costa Mesa CA, US
David Kettler - Bellevue WA, US
Karl Schwamb - Mission Viejo CA, US
Yu Zhao - Irvine CA, US
International Classification:
G10L 15/32
G10L 15/30
G10L 15/16
G06N 3/08
G10L 15/18
G10L 15/02
G06F 17/27
G06N 3/04
G06N 20/00
G06N 5/00
Abstract:
Methods and systems for classifying a multimedia file using interclass data is disclosed. One of the methods includes receiving, from a first transcription engine, one or more transcription results of one or more audio segments of the multimedia file; identifying a first transcription result for a first audio segment having a low confidence of accuracy; identifying a first image data of the multimedia file corresponding to the first segment; receiving, from an image classification engine trained to classify image data, an image classification result of one or more portions of the first image data in response to requesting the image classification engine to classify the first image data; and selecting, based at least on the image classification result of the one or more portions of the first image data, a second transcription engine to re-classify the first audio segment.

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