Xpo Logistics, Inc. Aug 2009 - Mar 2016
Operations Research Principal
Celgene Aug 2009 - Mar 2016
Senior Operations Research Scientist and Senior Manager
Informs Jan 2012 - Dec 2013
Vice President, Chapters and Fora
Emptoris Nov 2002 - Aug 2009
Lead Scientist
Nokia Bell Labs May 2000 - Sep 2002
Member of Technical Staff
Education:
Moscow State University 2016 - 2017
Master of Science, Masters
Columbia University In the City of New York 1997 - 2003
Doctorates, Doctor of Philosophy
Lomonosov Moscow State University (Msu) 1992 - 1997
Master of Science, Masters, Mathematics, Computer Science
Skills:
Operations Research Optimization Data Analysis Data Mining Analytics Mathematical Modeling Optimizations Algorithms Business Analysis Analysis Research Software Development Java Matlab Business Analytics Programming Product Management Technical Leadership Sql Thought Leadership Supply Chain Optimization Analytical Skills Cplex Text Mining Tableau
William Galen - Arlington MA, US Joshua Kanner - Cambridge MA, US Olga Raskina - Cambridge MA, US Rina Scheur - Lexington MA, US
International Classification:
G06F017/60
US Classification:
705037000
Abstract:
Prospective suppliers competing in an auction format to fulfill a requisition receive assistance in responding to the requisition over a computer network. A plurality of bids responsive to the requisition are received from at least one prospective supplier. At least one initial winning bid is selected from the received bids that at least partially satisfies the requisition. Using the received plurality of bids, at least one proposed bid is determined that, if adopted by a prospective supplier, would constitute a new winning bid. The proposed bid is made available to at least one prospective supplier that had not previously submitted a winning bid.
Method & Apparatus For Identifying Contract Characteristics
Olga Raskina - Arlington MA, US Robert Marc Jamison - San Jose CA, US Ammiel Kamon - Burlingame CA, US
International Classification:
G06F 17/27 G06F 17/21
US Classification:
704 9, 715256
Abstract:
A contract characteristic identification application includes a user interface, a plurality of contract characteristic definitions, a natural language processing module and a characteristic identification function. At least one contract characteristic is defined and evaluated and the text of at least one contract is entering into the application. A document evaluation function included in the natural language processing module operates to evaluate the contents of the text of the contract against the defined contract characteristic and returns a listing of contract text that is closest to the defined contract characteristic of interest.
Automatic Generation Of A Scenario Used To Optimize A Bid Award Schedule
Olga Raskina - Arlington MA, US Sean Correll - Dover MA, US Jeffrey Robbins - Lexington MA, US
International Classification:
G06Q 30/00 G06Q 10/00
US Classification:
705 80, 705 7
Abstract:
An sourcing event management system includes a presentation layer, a business logic layer, an infrastructure layer and storage and operates to facilitate the creation of a bidding scenario, the opening of a bidding process, reception of bids from suppliers, the closing of the bidding process and the analysis of the bids received from the suppliers to determine an optimal bid award schedule. The sourcing event management system also includes a modified mathematical model that is solved, subject to constraints selected by a buyer and to variables specified by the buyer, to determine how the constraints can be modified to further optimize the bid award schedule.
Method & Apparatus For Identifying A Secondary Concept In A Collection Of Documents
OLGA RASKINA - Arlington MA, US Robert Marc Jamison - San Jose CA, US Ammiel Kamon - Burlingame CA, US
Assignee:
Emptoris, Inc. - Burlington MA
International Classification:
G06F 17/30
US Classification:
707760, 707E17073
Abstract:
A Methodology for identifying secondary concepts that are included in one or more documents in a collection of documents is disclosed. Training information is manually created from a subset of a collection of documents and used by a primary concept identification function to process textual information contained in the documents included in the collection of documents to identify primary concepts included in the collection of documents. Each of the primary concepts included in the collection of documents is used as input to a secondary concept identification function which results in the identification of secondary concepts included in each of the primary concepts. A query is generated and used as input to both the primary and secondary concept identification functions and the result of both the operation of both of these functions on the query is compared to the identified secondary concepts. The distance between the query and each of the secondary concepts is determined and those secondary concepts that are within a predetermined distance of the query are displayed.
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