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Hui Q Zhou

from San Francisco, CA
Age ~66
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Also known as:
Hui Qiang Zhou, Pian Pian Zhou, Pian P Zhou, Qiang Zhou Hui, Huiqiang Zhou, Pianpian Zhou, Zhou Pian
Phone and address:
532 Bright St, San Francisco, CA 94132
Related to:
Nancy Chu, 40Jenny Zhou, 30You L ZhouHui Juan Chu, 71Pian Zhou Pian, 61Raymond Chu, 41Ho Chu Yui, 76Diane Zhou, 32
Connected to:
Aihua Zhou, 47Aishi Zhou, 74Angela Zhou, 56Anqi Zhou, 57Anqiang Zhou, 80Bonnie Zhou, 65Boyu Zhou, 34Calvin J Zhou, 40Carolyn L Zhou, 27Cecilia Zhou, 55

Hui Zhou Phones & Addresses

  • 532 Bright St, San Francisco, CA 94132
  • Castro Valley, CA

Resumes

Resumes

Hui Zhou Photo 1

Hui Zhou Las Vegas, NV

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Work:
Casino M8trix
San Jose, CA
2012 to May 2014
table games dealer
California Grand Casino
Pleasant Hill, CA
2005 to Aug 2008
Poker Dealer
SourceCorp

2002 to Sep 2004
Data Entry
Education:
Let's Make A Dealer Poker Training School
Las Vegas, NV
2015 to 2015
Certificate
City College of San Francisco
2001 to 2002
Certificate in Accounting and office clerk
YunNan University
1978 to 1981
Physics Graduate for AS
Hui Zhou Photo 2

Hui Zhou San Ramon, CA

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Work:
Theranos
Palo Alto, CA
Mar 2011 to Nov 2011
Sr. Scientist
Bio-Rad Laboratories
Hercules, CA
2005 to 2011
Scientist
Zyomyx, Inc
Hayward, CA
2001 to 2004
Associate Scientist
Qiagen-Operon Technologies, Inc
Alameda, CA
2000 to 2001
Scientist
AlphaGene Inc
Woburn, MA
1998 to 2000
Senior Research Associate
Genome Therapeutics Inc
Waltham, MA
1997 to 1998
Research Associate
Education:
The University of Mississippi, University, MS
1993
Master of Science in Biochemistry
Fudan University
Jan 1987
Bachelor of Science in Biochemistry
Skills:
process/assay development, molecular biology, microarray
Hui Zhou Photo 3

Hui Zhou Belmont, CA

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Work:
Vanderbilt University

2011 to 2000
Bioinformatics Manager
University of California at Los Angeles
Los Angeles, CA
2006 to 2011
Senior Statistician
University of Texas Southwestern Medical Center
Dallas, TX
2005 to 2006
Senior Research Associate
Molecular Kinetics Inc
Indianapolis, IN
2003 to 2005
Computational Biologist
Celltech R&D Inc., Bioinformatics Group
Seattle, WA
2002 to 2002
Intern
Children's Hospital of Los Angeles
Los Angeles, CA
2001 to 2002
Research Assistant
University of California
San Francisco, CA
2001 to 2001
Research Intern
University of Missouri
Kansas City, MO
1999 to 2000
Research Assistant
Chinese Academy of Medical Sciences, National Heart Institute

1995 to 1999
Resident Cardiologist
Education:
Shandong University School of Medicine
Jinan, Shandong Province, P. R. China
1990 to 1995
MD in Medicine
University of Southern California
Los Angeles, CA
M. S. in Computational Biology and Bioinformatics
Skills:
COMPUTER SKILLS Experienced in the application of: Database and Software: Database (MySQL, SQL server, ORACLE), data analysis (R/Bioconductor, Matlab, SAS), bioinformatics (BLAST, CASAVA, BWA, GATK, SAMtools, Picard, Bowtie, TopHat, Cufflinks, GeneSpring, Partek, Ingenuity, DAVID, Galaxy, GenePattern, CLUSTALW, PSI-Pred, MASCOT, Phrap, MEME suite, Cytoscape) as well as online tools and databases (NCBI, UCSC genome browser) OS and Programming: LINUX, Windows, PERL/DBI, CGI, C++, Python, shell, JAVA Script, Coldfusion Ontology, data exchange standards and semantic web technologies: GO, XML. High performance computing: TORQUE PBS

Business Records

Name / Title
Company / Classification
Phones & Addresses
Hui Zhou
Principal
New Gift Shop
Ret Gifts/Novelties
269 Jefferson St, San Francisco, CA 94133

Publications

Us Patents

Multi-Scale Method For Multi-Phase Flow In Porous Media

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US Patent:
8204726, Jun 19, 2012
Filed:
May 14, 2009
Appl. No.:
12/466171
Inventors:
Seong H. Lee - Berkeley CA, US
Hui Zhou - Stanford CA, US
Hamdi A. Tchelepi - Belmont CA, US
Assignee:
Chevron U.S.A. Inc. - San Ramon CA
Schlumberger Technology Corporation - Houston TX
International Classification:
G06G 7/48
G06F 17/10
US Classification:
703 10, 703 2, 702 12
Abstract:
A multi-scale method to efficiently determine the fine-scale saturation arising from multi-phase flow in a subsurface reservoir is disclosed. The method includes providing a simulation model that includes a fine-scale grid defining a plurality of fine-scale cells, and a coarse-scale grid defining a plurality of coarse-scale cells that are aggregates of the fine-scale cells. The coarse-scale cells are partitioned into saturation regions responsive to velocity and/or saturation changes from the saturation front. A fine-scale saturation is determined for each region and the saturation regions are assembled to obtain a fine-scale saturation distribution. A visual display can be output responsive to the fine-scale saturation distribution.

Indirect-Error-Based, Dynamic Upscaling Of Multi-Phase Flow In Porous Media

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US Patent:
8346523, Jan 1, 2013
Filed:
Sep 1, 2009
Appl. No.:
12/551906
Inventors:
Seong H. Lee - Berkley CA, US
Hui Zhou - Stanford CA, US
Hamdi A. Tchelepi - Belmont CA, US
Assignee:
Chevron U.S.A. Inc. - San Ramon CA
Schlumberger Technology Corporation - Houston TX
International Classification:
G06G 7/48
US Classification:
703 10
Abstract:
Computer-implemented systems and methods are provided for an upscaling approach based on dynamic simulation of a given model. A system and method can be configured such that the accuracy of the upscaled model is continuously monitored via indirect error measures. If the indirect error measures are bigger than a specified tolerance, the upscaled model is dynamically updated with approximate fine-scale information that is reconstructed by a multi-scale finite volume method. Upscaling of multi-phase flow can include flow information in the underlying fine-scale. Adaptive prolongation and restriction operators are applied for flow and transport equations in constructing an approximate fine-scale solution.

Time-Factored Performance Prediction

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US Patent:
20220284350, Sep 8, 2022
Filed:
May 25, 2022
Appl. No.:
17/824312
Inventors:
- Redmond WA, US
Hui ZHOU - San Francisco CA, US
Pushpraj SHUKLA - Dublin CA, US
Emre Hamit KOK - Kirkland WA, US
Sonal Prakash MANE - Redmond WA, US
Dimitrios BRISIMITZIS - Kirkland WA, US
International Classification:
G06N 20/00
G06N 5/02
G06F 16/903
Abstract:
Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.

System And Method For Product Recommendation Based On Multimodal Fashion Knowledge Graph

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US Patent:
20220207587, Jun 30, 2022
Filed:
Dec 30, 2020
Appl. No.:
17/138147
Inventors:
- Beijing, CN
- Mountain View CA, US
Min Li - Mountain View CA, US
Huiman Hou - Beijing, CN
Qin Wang - Beijing, CN
Jixing Wang - Beijing, CN
Yuhao Zhang - Beijing, CN
Zizhen Wang - Beijing, CN
Xin Li - Beijing, CN
Hui Zhou - Sunnyvale CA, US
International Classification:
G06Q 30/06
G06N 3/02
G06F 16/903
Abstract:
A method and a system for recommending a target garment matching an inputted garment. The method includes: extracting attributes from text description and image of the inputted garment to obtain extracted attributes; querying a knowledge graph using the extracted attributes to obtain matched attributes; retrieving candidate products from a garment pool using the matched attributes; extracting features from the inputted garment and the candidate products; determining the target garment from the candidate products based on grading scores between the features of the inputted garment and the features of the candidate products; and recommending the target garment. The knowledge graph includes nodes corresponding to type of clothes, category of clothes, attribute keys, values of attribute keys, context keys, values of context keys, combination of the values of the attribute keys and the type of clothes, and combination of the value of the attribute keys and the category of clothes.

System And Method For Knowledge Graph Construction Using Capsule Neural Network

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US Patent:
20220180065, Jun 9, 2022
Filed:
Dec 9, 2020
Appl. No.:
17/116906
Inventors:
- Beijing, CN
Xiaochuan Fan - Mountain View CA, US
Guanghan Ning - Sunnyvale CA, US
Hui Zhou - Sunnyvale CA, US
International Classification:
G06F 40/284
G06N 3/04
G06N 3/08
Abstract:
A system for knowledge graph construction. The system includes a computing device. The computing device has a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: define entities and relations of the knowledge graph; provide documents having sentences; convert the sentences into fix length sentence embeddings and regard the sentence embeddings as primary capsule layers; use a set transformer to learn entity capsules and relation capsules from the primary capsule layers; for each triple, project head and tail entities from entity space to the specific relation space, and determine the relation exists when the sum of the projected head entity vector and the relation vector substantially equals to the projected tail entity vector; and construct the knowledge graph using the head entity, the tail entity, and the determined relation.

System And Method For Automatically Generating Articles Of A Product

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US Patent:
20200074013, Mar 5, 2020
Filed:
Aug 28, 2018
Appl. No.:
16/114592
Inventors:
- Beijing, CN
- Mountain View CA, US
Jixing Wang - Beijing, CN
Huiman Hou - Beijing, CN
Yingqiu Tian - Beijing, CN
Hui Zhou - Sunnyvale CA, US
International Classification:
G06F 17/30
G06Q 30/06
G06F 17/27
Abstract:
A method and system for generating an article of a product. The method includes: receiving a request from a user; when the request include an identification of the product, retrieving traits of the product from a trait database, when the request include keywords, retrieving the traits of the product by comparing the similarity between the keywords and the traits or the synonym of the traits; generating candidate sentences corresponding to the traits; selecting sentences from the candidate sentences, and revising and rearranging the sentences to generate the article.

System And Method For Monitoring Online Retail Platform Using Artificial Intelligence

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US Patent:
20200074242, Mar 5, 2020
Filed:
Aug 28, 2018
Appl. No.:
16/114664
Inventors:
- Beijing, CN
- Mountain View CA, US
Kailin Huang - Mountain View CA, US
Shanglin Yang - Sunnyvale CA, US
Hui Zhou - Sunnyvale CA, US
International Classification:
G06K 9/62
G06K 9/32
G06F 17/30
G06Q 30/06
Abstract:
A method and system for monitoring an e-commerce platform. The method includes: receiving, by a computing device, a feedback submitted by a user through the e-commerce platform; generating a vector based on content of the feedback, context of the feedback and profile of the user using AI processors; and classifying the vector to determine function corresponding to the feedback and status of the function using AI classifiers. The content includes text, voice, image and video; the context includes time, location and submission channel of the feedback; the profile includes attributes, history and preference of the user. Dimensions of the vector respectively corresponding to the text, voice, image, video, time, location, submission channel, attributes, history, and preference of the user.

Time-Factored Performance Prediction

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US Patent:
20190378048, Dec 12, 2019
Filed:
Jun 8, 2018
Appl. No.:
16/004096
Inventors:
- Redmond WA, US
Hui ZHOU - San Francisco CA, US
Pushpraj SHUKLA - Dublin CA, US
Emre Hamit KOK - Kirkland WA, US
Sonal Prakash MANE - Redmond WA, US
Dimitrios BRISIMITZIS - Kirkland WA, US
International Classification:
G06N 99/00
G06N 5/02
G06F 17/30
Abstract:
Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.
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Hui Q Zhou from San Francisco, CA, age ~66 Get Report