Shaochen Huang from 388 Fulton St UNIT 614, San Francisco, CA 94102

Shaochen Huang Phones & Addresses

388 Fulton St UNIT 614, San Francisco, CA 94102
San Jose, CA
1068 Tiller Ln, San Mateo, CA 94404
Foster City, CA
Redwood City, CA
Pittsburgh, PA
88 Bush St UNIT 2152, San Jose, CA 95126

Work

Company:

Responsys
Nov 2010
Position:

Senior software engineer

Education

Degree:

Master
School / High School:

Carnegie Mellon University
2007 to 2008
Specialities:

Information System Management

Skills

Analytics • Experimentation • Distributed Systems • Espresso • Apache Kafka • A/B Testing • Distributed Caching • Java • Agile Methodologies • Sql • Cloud Computing • Spring • Jdbc • Oracle • Project Management • Hadoop • Linux • Databases • Software Development • Web Applications • Business Intelligence • Algorithms • R • Kafka

Languages

English • Mandarin • Japanese

Ranks

Certificate:

Cfa Level I

Industries

Internet

Resumes

Engineering Manager @ Targeting, Ramping And Experimentation Platform

view source

Location:

San Francisco, CA

Industry:

Internet

Work:

Responsys since Nov 2010
Senior Software Engineer
Oracle Mar 2009 - Nov 2010
Application Engineer
Morgan Stanley Jun 2008 - Aug 2008
IT Analyst
General Electric May 2007 - Jul 2007
IT Analyst

Education:

Carnegie Mellon University 2007 - 2008
Master, Information System Management Tongji University 2003 - 2007
Bachelor, Information Management and Information System

Skills:

Analytics
Experimentation
Distributed Systems
Espresso
Apache Kafka
A/B Testing
Distributed Caching
Java
Agile Methodologies
Sql
Cloud Computing
Spring
Jdbc
Oracle
Project Management
Hadoop
Linux
Databases
Software Development
Web Applications
Business Intelligence
Algorithms
R
Kafka

Languages:

English
Mandarin
Japanese

Certifications:

Cfa Level I
Responsys Certified Campaign Professional
Coursera Course Certificates, License Dbrsablzcc8K
Coursera Course Certificates, License Vlvebv4H2P7D
Cfa
Responsys
Coursera Course Certificates, License Y3Jnw3Y4Gj25
R Programming
The Data Scientist’s Toolbox
Getting and Cleaning Data
Exploratory Data Analysis
Reproducible Research
Statistical Inference
Regression Models
Practical Machine Learning
Machine Learning
License Dbrsablzcc8K
License Vlvebv4H2P7D
License Y3Jnw3Y4Gj25
License K88U8Vw7Kv25
License Vl2Ne9L3Kwcm

Us Patents

Unified Management Of Targeting Attributes In A/B Tests
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US Patent:

20200104398, Apr 2, 2020
Filed:

Sep 28, 2018
Appl. No.:

16/146603
Inventors:

- Redmond WA, US
Jingbang Liu - Cupertino CA, US
Shaochen Huang - San Francisco CA, US
Jiahui Qi - Mountain View CA, US
Shaohua Xie - Santa Clara CA, US
Assignee:

Microsoft Technology Licensing, LLC - Redmond WA
International Classification:

G06F 17/30
G06F 9/48
G06Q 30/02
G06F 11/07
Abstract:

The disclosed embodiments provide a system for performing unified management of targeting attributes in A/B tests. During operation, the system obtains attribute configurations for attributes to be used in subsequent targeting of users by A/B tests. Next, the system configures, based on the attribute configurations, onboarding of the attributes from an offline environment, a near-real-time environment, and an online environment. During an A/B test, the system retrieves values of one or more of the attributes for a user from locations specified in the attribute configurations. Finally, the system outputs the values with targeting conditions for the A/B test for use in selecting a treatment assignment for the user in the A/B test.

Automatic Ramp-Up Of Controlled Experiments
view source
US Patent:

20190095828, Mar 28, 2019
Filed:

Sep 27, 2017
Appl. No.:

15/717719
Inventors:

- Sunnyvale CA, US
Weitao Duan - Mountain View CA, US
Shaochen Huang - San Francisco CA, US
Mingyue Tan - Burlingame CA, US
Shaohua Xie - Santa Clara CA, US
Assignee:

LinkedIn Corporation - Sunnyvale CA
International Classification:

G06Q 10/06
G06Q 30/02
Abstract:

The disclosed embodiments provide a system for managing an A/B test. During operation, the system calculates a first risk associated with ramping up exposure to a first A/B test by a first ramp amount. Next, the system uses a first sequential hypothesis test to compare the first risk with a first risk tolerance for the first A/B test. When the first sequential hypothesis test indicates that the first risk is within the first risk tolerance, the system automatically triggers a ramp-up of exposure to the first A/B test by the first ramp amount.

Shaochen Huang