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Chaochao Cai

from Bellevue, WA
Age ~38
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Also known as:
Chao C Cai, Cai Chaochao
Phone and address:
15019 SE 49Th St, Bellevue, WA 98006
Related to:
Mariah I LarsonLawrence Q Luo, 48
Connected to:
Biao Cai, 53Bin Cai, 62Christine Cai, 37Cody Cai, 29Dongmei Cai, 37Feng S Cai, 45Guo Q Cai, 71Hancheng Cai, 46Hieu D Cai, 38Jesse Z Cai, 27

Chaochao Cai Phones & Addresses

    s
  • 15019 SE 49Th St, Bellevue, WA 98006
  • Seattle, WA
  • Redmond, WA
  • Los Angeles, CA

Work

Company: Bank of new york mellon, hedgemark - Los Angeles, CA Sep 2011 Position: Risk analyst

Education

School / High School: UNIVERSITY OF CALIFORNIA- Los Angeles, CA Sep 2008 Specialities: M.S. of Biostatistics in Genetics & M.S

Resumes

Resumes

Chaochao Cai Photo 1

Senior Software Engineer, Machine Learning

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Location:
Los Angeles, CA
Industry:
Internet
Work:
Amazon Jun 2013 - May 2015
Machine Learning Scientist
Facebook Jun 2013 - May 2015
Senior Software Engineer, Machine Learning
Ucla Oct 2008 - Jun 2013
Research Assistant
Hedgemark International, Llc Sep 2011 - Sep 2012
Intern
Education:
University of California, Los Angeles 2008 - 2013
Doctorates, Doctor of Philosophy Zhejiang University 2004 - 2008
Bachelors, Bachelor of Science University of California
Skills:
Machine Learning
Statistical Modeling
Large Scale Data Analysis
Python
Online Advertising
R
Hive
Sql
Linux
Risk Management
Interests:
Photograph
Table Tennis
Badminton
Tennis
Languages:
Mandarin
English
Chaochao Cai Photo 2

Chaochao Cai

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Chaochao Cai Photo 3

Chaochao Cai Los Angeles, CA

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Work:
BANK OF NEW YORK MELLON, HEDGEMARK
Los Angeles, CA
Sep 2011 to Sep 2011
Risk Analyst
HEARST ANALYTICS COMPETITION
Los Angeles, CA
Jun 2011 to Aug 2011
Group Leader
SCHOOL OF PUBLIC HEALTH, UCLA
Los Angeles, CA
Mar 2011 to Mar 2011
Consultant of Biostatistics
DEPARTMENT OF BIOSTATISTICS/GENETICS, UCLA
Los Angeles, CA
Jun 2009 to Jun 2009
Research assistant
Education:
UNIVERSITY OF CALIFORNIA
Los Angeles, CA
Sep 2008
M.S. of Biostatistics in Genetics & M.S

Publications

Us Patents

Generating Models To Measure Performance Of Content Presented To A Plurality Of Identifiable And Non-Identifiable Individuals

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US Patent:
20180218286, Aug 2, 2018
Filed:
Jan 31, 2017
Appl. No.:
15/421060
Inventors:
- Menlo Park CA, US
Liang Xu - Bellevue WA, US
Chaochao Cai - Bellevue WA, US
International Classification:
G06N 99/00
G06F 17/30
Abstract:
An online system measures performance of content presented to a plurality of identifiable and non-identifiable individuals based on matching user identifying information included in data describing presentation of the content and data describing performance of an action associated with the content. To reduce measurement inaccuracy resulting from incomplete matching of user identifying information associated with non-identifiable individuals, the online system generates models to extrapolate data describing an amount of unique individuals presented with the content, an amount of unique individuals who performed an action associated with the content, and an amount of unique individuals who performed the action associated with the content attributable to presentation of the content by a content publisher. The models are applied to data collected by the online system describing presentation of the content and performance of actions associated with the content. Metrics describing performance of the content are generated based on the models.

Interest Prediction For Unresolved Users In An Online System

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US Patent:
20180189676, Jul 5, 2018
Filed:
Jan 3, 2017
Appl. No.:
15/397530
Inventors:
- Menlo Park CA, US
Chaochao Cai - Bellevue WA, US
International Classification:
G06N 99/00
G06N 7/00
Abstract:
Disclosed is an online system that infers interests of unresolved users for whom the interests are not known. The online system determines certain features about the unresolved users, but does not have certain information about the users themselves (e.g., their interests), so instead infers these attributes based on the features of the user. The online system provides the features as input to a classifier trained to predict a particular interest, and the classifier outputs a prediction of whether the user has the corresponding interest. In one embodiment, the online system trains a classifier for various interest values by forming training sets for the interests using the features for users who are logged into the online system and hence have known interests.

Predicting Characteristics Of Users Of A Third Party System That Communicates With An Online System And Determining Accuracy Of The Predicted Characteristics

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US Patent:
20180097815, Apr 5, 2018
Filed:
Sep 30, 2016
Appl. No.:
15/282693
Inventors:
- Menlo Park CA, US
Erjie Ang - Sunnyvale CA, US
Yongfeng Liu - East Palo Alto CA, US
Liang Xu - Bellevue WA, US
Chaochao Cai - Bellevue WA, US
International Classification:
H04L 29/06
G06F 21/62
Abstract:
An online system maintains characteristics for its users and may access characteristics of users maintained by a third party system. The online system may select content for a user of the third party system based on characteristics maintained by the third party system. If the third party system does not maintain a characteristic for its users, the generates a model predicting the characteristic for third party system users based on a set of online system users identified based on characteristics of third party system users. The online system clusters third party system users based on the predicted characteristic for other third party system users connected to the third party system user. Using verified characteristics for third party system users from a trusted third party system, the online system determines an accuracy of the predicted characteristic for third party system users in a cluster.

Estimation Of Reach Overlap And Unique Reach For Delivery Of Content Items

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US Patent:
20180060753, Mar 1, 2018
Filed:
Aug 29, 2016
Appl. No.:
15/250452
Inventors:
- Menlo Park CA, US
Chaochao Cai - Bellevue WA, US
Goran Predovic - Redmond WA, US
International Classification:
G06N 99/00
H04L 29/08
Abstract:
An online system obtains a set of resolved impressions based on historical data about multiple publishers. A set of features is then extracted, for each resolved impression, based on a comparison of historical data about the first publisher and the second publisher. The online system performs training of a machine-learned model based on the set of features. Data about a plurality of new impressions are input into the trained machine-learned model to obtain an output of the trained machine-learned model. A reach overlap metric and unique reach metric can be computed based on the output of the trained machine-learned model.
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Chaochao Cai from Bellevue, WA, age ~38 Get Report