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Xiaoyuan Yang

from Pleasanton, CA
Age ~44
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Related to:
Mary WareByron M Ware, 83Helen J WeirPatrick Duane Ware, 66
Connected to:
Abby Yang, 32Addison Yang, 43Aileen C Yang, 51Alexandria L Yang, 33Allerina Yang, 33Alyssa J Yang, 36Amee Yang, 31Amy Yang, 57Andrew Yang, 34Anna Yang, 40

Xiaoyuan Yang Phones & Addresses

  • 1311 Greenwood Rd, Pleasanton, CA 94566
  • Livermore, CA
  • San Jose, CA
  • Allston, MA
  • Malden, MA
  • 3550 Pacific Ave APT 114, Livermore, CA 94550

Work

Company: Sandia national lab Jun 2013 Position: Postdoctoral researcher

Education

School / High School: University of Massachusetts Boston Post-Doc Research Associate- Boston, MA Dec 2011 Specialities: MA

Resumes

Resumes

Xiaoyuan Yang Photo 1

Principal Data And Applied Science Manager

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Location:
201 3Rd St, San Francisco, CA 94103
Industry:
Computer Software
Work:
Microsoft
Principal Data and Applied Science Manager
The Climate Corporation
Senior Manager, Data Insights
The Climate Corporation Jan 2019 - Oct 2019
Data Science Manager, Remote Sensing and Geospatial
The Climate Corporation Jan 2019 - Oct 2019
Science Lead, Remote Sensing and Weather Product
The Climate Corporation Mar 2016 - Jul 2019
Science Lead, Remote Sensing Product
Education:
Boston University 2008 - 2011
Skills:
R
Data Analysis
Science
Fortran
Image Processing
Matlab
Arcgis
Idl
Numerical Analysis
Latex
Remote Sensing
Scientific Computing
Mathematical Modeling
Machine Learning
Physics
Python
Languages:
English
Mandarin
Xiaoyuan Yang Photo 2

Xiaoyuan Yang

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Xiaoyuan Yang Photo 3

Xiaoyuan Yang Livermore, CA

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Work:
Sandia National Lab

Jun 2013 to 2000
Postdoctoral Researcher

Education:
University of Massachusetts Boston Post-Doc Research Associate
Boston, MA
Dec 2011 to 2000
MA
Boston University
Boston, MA
Sep 2006 to Nov 2011
Research
Boston University
Boston, MA
Sep 2006 to May 2011
Ph.D. in Geography
Nanjing Institute of Meteorology
Nanjing, CN
Sep 2003 to Jun 2006
M.S.
Nanjing Institute of Meteorology
Nanjing, CN
Sep 1999 to Jun 2003
B.S. in Atmospheric Science

Publications

Us Patents

Using Optical Remote Sensors And Machine Learning Models To Predict Agronomic Field Property Data

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US Patent:
20210209490, Jul 8, 2021
Filed:
Jan 7, 2021
Appl. No.:
17/143536
Inventors:
- San Francisco CA, US
Xiaoyuan Yang - Pleasanton CA, US
Ho Jin Kim - San Francisco CA, US
Steven Ward - Moraga CA, US
International Classification:
G06N 5/04
G06N 20/00
Abstract:
In some embodiments, a computer-implemented method for predicting agronomic field property data for one or more agronomic fields using a trained machine learning model is disclosed. The method comprises receiving, at an agricultural intelligence computer system, agronomic training data; training a machine learning model, at the agricultural intelligence computer system, using the agronomic training data; in response to receiving a request from a client computing device for agronomic field property data for one or more agronomic fields, automatically predicting the agronomic field property data for the one or more agronomic fields using the machine learning model configured to predict agronomic field property data; based on the agronomic field property data, automatically generating a first graphical representation; and causing to display the first graphical representation on the client computing device.

Mapping Soil Properties With Satellite Data Using Machine Learning Approaches

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US Patent:
20200184214, Jun 11, 2020
Filed:
Dec 9, 2019
Appl. No.:
16/707168
Inventors:
- San Francisco CA, US
XIAOYUAN YANG - Pleasanton CA, US
STEVEN WARD - Moraga CA, US
International Classification:
G06K 9/00
A01C 21/00
A01B 79/00
G06N 20/20
Abstract:
In an embodiment, a computer-implemented method for predicting subfield soil properties for an agricultural field comprises: receiving satellite remote sensing data that includes a plurality of images capturing imagery of an agricultural field in a plurality of optical domains; receiving a plurality of environmental characteristics for the agricultural field; generating a plurality of preprocessed images based on the plurality of satellite remote sensing data and the plurality of environmental characteristics; identifying, based on the plurality preprocessed images, a plurality of features of the agricultural field; generating a subfield soil property prediction for the agricultural field by executing one or more machine learning models on the plurality of features; transmitting the subfield soil property prediction to an agricultural computer system.

Machine Learning Techniques For Identifying Clouds And Cloud Shadows In Satellite Imagery

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US Patent:
20200125844, Apr 23, 2020
Filed:
Oct 18, 2019
Appl. No.:
16/657957
Inventors:
- San Francisco CA, US
Pramithus Khadka - Ofallon MO, US
Wei Guan - Pleasanton CA, US
Xiaoyuan Yang - Pleasanton CA, US
Demir Devecigil - St. Charles MO, US
International Classification:
G06K 9/00
G06N 20/00
G06N 3/04
G06F 17/16
Abstract:
Systems and methods for identifying clouds and cloud shadows in satellite imagery are described herein. In an embodiment, a system receives a plurality of images of agronomic fields produced using one or more frequency bands. The system also receives corresponding data identifying cloud and cloud shadow locations in the images. The system trains. a machine learning system to identify at least cloud locations using the images as inputs and at least data identifying pixels as cloud pixels or non-cloud pixels as outputs. When the system receives one or more particular images of a particular agronomic field produced using the one or more frequency bands, the system uses the one or more particular images as inputs into the machine learning system to identify a plurality of pixels in the one or more particular images as particular cloud locations.
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Xiaoyuan Yang from Pleasanton, CA, age ~44 Get Report