Chao Li Phones & Addresses
-
San Francisco, CA
-
Daly City, CA
Resumes
Resumes
Chao Li San Jose, CA
View pageWork:
AFFILIATION
Oct 2012 to 2000
Member, Accounting Association, UCSB
ALLEN ASSOCIATES
Santa Barbara, CA
Jul 2013 to Aug 2013
Temporary Accounting Assistant
CIB SECURITY
Sunnyvale, CA
Jun 2011 to Sep 2011
Technician
American Marketing Association
Sep 2009 to May 2010
Member
Oct 2012 to 2000
Member, Accounting Association, UCSB
ALLEN ASSOCIATES
Santa Barbara, CA
Jul 2013 to Aug 2013
Temporary Accounting Assistant
CIB SECURITY
Sunnyvale, CA
Jun 2011 to Sep 2011
Technician
American Marketing Association
Sep 2009 to May 2010
Member
Education:
University of California-Santa Barbara
Santa Barbara, CA
Mar 2013
Bachelor of Art in Economic and Accounting
Ohlone College
Fremont, CA
Sep 2010 to May 2011
Business Administration
Central Michigan University
Mount Pleasant, MI
Jan 2007 to May 2010
Bachelor of Science in Business Administration
Santa Barbara, CA
Mar 2013
Bachelor of Art in Economic and Accounting
Ohlone College
Fremont, CA
Sep 2010 to May 2011
Business Administration
Central Michigan University
Mount Pleasant, MI
Jan 2007 to May 2010
Bachelor of Science in Business Administration
Chao Li Santa Barbara, CA
View pageWork:
AFFILIATION
Oct 2012 to 2000
Member, Accounting Association, UCSB
CIB SECURITY
Sunnyvale, CA
Jun 2011 to Sep 2011
Technician
American Marketing Association
Sep 2009 to May 2010
Member
Real Food on Campus
Mount Pleasant, MI
Jun 2007 to Sep 2007
Server
Oct 2012 to 2000
Member, Accounting Association, UCSB
CIB SECURITY
Sunnyvale, CA
Jun 2011 to Sep 2011
Technician
American Marketing Association
Sep 2009 to May 2010
Member
Real Food on Campus
Mount Pleasant, MI
Jun 2007 to Sep 2007
Server
Education:
University of California-Santa Barbara
Santa Barbara, CA
Mar 2013
Bachelor of Art in Economic and Accounting
Ohlone College
Fremont, CA
Sep 2010 to May 2011
Business Administration
Central Michigan University
Mount Pleasant, MI
Jan 2007 to May 2010
Bachelor of Science in Business Administration
Santa Barbara, CA
Mar 2013
Bachelor of Art in Economic and Accounting
Ohlone College
Fremont, CA
Sep 2010 to May 2011
Business Administration
Central Michigan University
Mount Pleasant, MI
Jan 2007 to May 2010
Bachelor of Science in Business Administration
Skills:
MS Word, Excel, Powerpoint, Chinese&English
Business Records
Name / Title
Company / Classification
Phones & Addresses
President
CHAO LI ELECTRICAL, INC
Electrical Contractor
Electrical Contractor
996 Randolph St, San Francisco, CA 94132
(415) 239-9688
(415) 239-9688
MEBOOKS, LLC
Principal
Chao's Family Service
Individual and Family Services, Nsk · Individual/Family Services
Individual and Family Services, Nsk · Individual/Family Services
1474 42 Ave, San Francisco, CA 94122
Publications
Us Patents
Anomaly Detection For An E-Commerce Pricing System
View pageUS Patent:
20220237670, Jul 28, 2022
Filed:
Apr 15, 2022
Appl. No.:
17/721594
Inventors:
- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US
International Classification:
G06Q 30/02
G06K 9/62
G06Q 20/20
G06N 20/00
G06Q 10/06
G06K 9/62
G06Q 20/20
G06N 20/00
G06Q 10/06
Abstract:
This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.
Methods And Apparatus For Anomaly Detections
View pageUS Patent:
20200380570, Dec 3, 2020
Filed:
May 30, 2019
Appl. No.:
16/427238
Inventors:
- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US
International Classification:
G06Q 30/02
G06K 9/62
G06Q 10/06
G06N 20/00
G06Q 20/20
G06K 9/62
G06Q 10/06
G06N 20/00
G06Q 20/20
Abstract:
This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.
Methods And Apparatus For Anomaly Detections
View pageUS Patent:
20200380571, Dec 3, 2020
Filed:
May 30, 2019
Appl. No.:
16/427241
Inventors:
- Bentonville AR, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US
Elham SHAABANI - Redwood City CA, US
Chao LI - San Carlos CA, US
Matyas A. SUSTIK - San Francisco CA, US
International Classification:
G06Q 30/02
G06K 9/00
G06F 17/11
G06Q 10/06
G06N 20/00
G06Q 20/20
G06K 9/00
G06F 17/11
G06Q 10/06
G06N 20/00
G06Q 20/20
Abstract:
This application relates to apparatus and methods for identifying anomalies within data, such as pricing data. In some examples, a computing device receives data updates and selects a machine learning model to apply to the data update. The computing device may train the machine learning model with features generated based on historical purchase order data. An anomaly score is generated based on application of the machine learning model. Based on the anomaly score, the data update is either allowed, or denied. In some examples, the computing device re-trains the machine learning model with detected anomalies. In some embodiments, the computing device prioritizes detected anomalies for further investigation. In some embodiments, the computing device identifies the cause of the anomalies by identifying at least one feature that is causing the anomaly.