Assaf Zeevi from Brooklyn, NY

Resumes

Professor, Columbia University

view source

Position:

Scientific Advisory Board at Ultimate Risk Solutions, Scientific Advisory Board at Nomis Solutions, Professor at Columbia Business School

Location:

Greater New York City Area

Industry:

Research

Work:

Ultimate Risk Solutions - Greater New York City Area since Jan 2012
Scientific Advisory Board
Nomis Solutions - San Mateo, CA since Jan 2006
Scientific Advisory Board
Columbia Business School since Jul 2001
Professor

Education:

Stanford University 1997 - 2001
PhD

Skills:

Statistical Modeling
Operations Research
Economic Modeling
Probability
Financial Modeling
Insurance
Pricing

Assaf Zeevi

view source

Name / Title

Company / Classification

Phones & Addresses

Assaf Zeevi
Vice Presi

ANG INTERNATIONAL CORP

709 Villa Antigua Ct, El Paso, TX 79932
44-02 23 St #404, Long Island City, NY 11101

Assaf Zeevi
Vice President

ANG INTERNATIONAL CORPORATION

6 Williams Blvd APT 2L, Lake Grove, NY 11755
4402 23 St, Long Island City, NY 11101

Assaf Zeevi
Principal

Orion Promotions Inc
Public Relations Services

8 Williams Blvd, Lake Grove, NY 11755

Us Patents

Self-Learning Valuation
view source
US Patent:

20170255953, Sep 7, 2017
Filed:

Mar 7, 2017
Appl. No.:

15/451780
Inventors:

- New York NY, US
Assaf Zeevi - New York NY, US
International Classification:

G06Q 30/02
Abstract:

A method of valuating a plurality of digital content items and offering the valuated plurality of digital content items for sale to consumers on devices connected to a network, the method including: on a processor connected to the network receiving the plurality of digital content items; for each received content item: establishing a plurality of attributes from content and characteristics of the content item; establishing a lifetime indicating span of consumers interest in the content item; producing a valuation function for calculating a market value of the content item during its lifetime by finding and ranking nearest one or more previously processed content items having the plurality of attributes with closest similarity to the plurality of attributes of the received content item, performing weighted averaging of the valuation functions produced for the nearest one or more of the previously processed content items; and adapting, using “edge cutting” adaptation, the produced valuation function to a market response reflecting behavior of the consumers in response to the market values of the content item calculated by the valuation function.