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Spencer Sharpe2113 Columbia Dr, Cheyenne, WY 82009

Spencer Sharpe Phones & Addresses

2113 Columbia Dr, Cheyenne, WY 82009    307-6354499   

400 S 10Th St UNIT A, Laramie, WY 82070   

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Spencer Sharpe

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Work

Company: University of wyoming May 2010 to Aug 2014 Position: Lecturer

Education

Degree: Masters, Master of Arts School / High School: University of Wyoming 2010 to 2015 Specialities: Neuroscience

Skills

Computer Science • Mathematics • Computer Vision • Research • Public Speaking • Teaching • Java • Software Development • Sql • Microsoft Office • Project Management

Industries

Public Safety

Mentions for Spencer Sharpe

Spencer Sharpe resumes & CV records

Resumes

Spencer Sharpe Photo 33

Lead Data Scientist

Location:
25175 Regency Dr, Novi, MI 48375
Industry:
Public Safety
Work:
University of Wyoming May 2010 - Aug 2014
Lecturer
Ul May 2010 - Aug 2014
Lead Data Scientist
Education:
University of Wyoming 2010 - 2015
Masters, Master of Arts, Neuroscience
University of Wyoming 2003 - 2007
Bachelors, Bachelor of Arts, Mathematics
Skills:
Computer Science, Mathematics, Computer Vision, Research, Public Speaking, Teaching, Java, Software Development, Sql, Microsoft Office, Project Management

Publications & IP owners

Us Patents

Technologies For Classifying Feedback Using Machine Learning Models

US Patent:
2020032, Oct 15, 2020
Filed:
Apr 12, 2019
Appl. No.:
16/383202
Inventors:
- Northbrook IL, US
Surekha Durvasula - Skokie IL, US
Spencer Sharpe - Laramie WY, US
Kyle Michael Caulfield - Mount Prospect IL, US
International Classification:
G06K 9/62
G06F 3/0482
G06Q 30/02
Abstract:
Systems and methods for classifying product feedback by an electronic device are described. According to certain aspects, an electronic device may receive consumer feedback entries associated with various products, where each entry may include an initial classification. The electronic device may analyze each entry using a machine learning model to determine a subsequent classification for the entry. When there is a mismatch between classifications, the electronic device may present information associated with the entry for review by a user, where the user may specify a final classification for the entry, and the electronic device may update the machine learning model for use in subsequent analyses.

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