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Arash Michael Sarabian Tehrani

from Laguna Hills, CA
Age ~45
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Also known as:
Arash S Tehrani, Arash Sarabian Tehrani, Arash M Tehrani, Arash Michael Sarabiantehrani, Arash M Sarabiantehrani, Arash Michael Sarabian, Arash Sarabian Sarabian, Arash Sarabian
Related to:
Arash M Teh, 45
Connected to:
Behrooz G Tehrani, 57Benjamin S Tehrani, 64Farid K Tehrani, 40Julia M Tehrani, 69Lilia K Tehrani, 61Mansoureh A Tehrani, 69Nastaran M Tehrani, 41Natasha G Tehrani, 30Nirvana Tehranian, 47Parvaneh Tehrani, 68

Arash Tehrani Phones & Addresses

  • Laguna Hills, CA
  • South Pasadena, CA
  • 3136 Darkwood St, Lancaster, CA 93536
  • Pasadena, CA
  • San Gabriel, CA
  • Glendora, CA

Resumes

Resumes

Arash Tehrani Photo 1

Accounts Payable Supervisor At Questex Media Group

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Location:
Cambridge, Massachusetts
Industry:
Marketing and Advertising
Work:
Questex Media Group Aug 2011 - Feb 2012
Accounting Department
Oliver Wyman Sep 2008 - Jun 2009
Accounts Payable Coordinator
Harmon Law Offices, P.C. 2007 - 2007
Accounts Receivable Coordinator
Education:
Suffolk University - Sawyer School of Management 2010 - 2012
Master of Science (M.S.), Finance University of Massachusetts at Amherst - Isenberg School of Management 2003 - 2008
Bachelor of Business Administration (B.B.A.), Finance
Skills:
Microsoft Excel
Financial Reporting
Financial Analysis
Accounting
Accounts Payable
Arash Tehrani Photo 2

Arash Tehrani

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Location:
Orange County, California Area
Industry:
Aviation & Aerospace

Publications

Us Patents

Method For Prioritizing Candidate Objects

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US Patent:
20190205751, Jul 4, 2019
Filed:
Dec 28, 2018
Appl. No.:
16/235355
Inventors:
- Los Angeles CA, US
- San Ramon CA, US
ARASH SABER TEHRANI - Los Angeles CA, US
VIKTOR K. PRASANNA - Pacific Palisades CA, US
LISA ANN BRENSKELLE - Houston TX, US
Assignee:
University of Southern California - Los Angeles CA
Chevron U.S.A. Inc. - San Ramon CA
International Classification:
G06N 3/08
G06N 20/10
G06N 5/04
G06F 17/18
E21B 47/00
Abstract:
A computer-implemented method for prioritizing candidate objects on which to perform a physical process includes receiving a time series history of measurements from each of a plurality of candidate objects at a data processing framework. The method further includes reducing dimensionality of the time series history of measurements with a convolutional autoencoder to obtain latent features for each of the plurality of candidate objects. The method also includes applying a kernel regression model to the latent features to generate a predicted value of physical output for performing the physical process on each of the plurality of candidate objects. The method additionally includes generating a prioritization of the candidate objects based on the values of physical output. The method involves selecting fewer than all of the plurality of candidate objects on which to perform the physical process. The selected candidate objects are based on the prioritization.
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Arash Michael Sarabian Tehrani from Laguna Hills, CA, age ~45 Get Report