Wells Fargo Jan 2003 - Oct 2011
Loan Servicing Specialist
Wells Fargo Financial Jul 1997 - Dec 2002
Loan Operations Specialist
Jul 1997 - Dec 2002
Francisco Junior High School
Education:
City College of San Francisco 1972 - 1976
Associates, Associate of Arts
Francisco Junior High School
Interests:
Minor Automotive Repair As A Hobby Sport Fishing Home Maintenance
Magma Design Automation Dec 2001 - Apr 2008
Senior Director
Youtube Dec 2001 - Apr 2008
Software Engineer - Youtube Syndication Team
Intel Corporation 2007 - 2008
Contractor
Pmc-Sierra Dec 2000 - Nov 2001
Senior Engineer
Alta Vista Solutions 1998 - 2000
Senior Principal Engineer
Amazon
Senior Product Manager - Marketplace Abuse
Symantec Jun 2008 - Jun 2017
Director, Global Brand Protection
Epson Jun 2006 - May 2008
Intellectual Property Enforcement Coordinator
Epson Europe B.v. Jun 2004 - May 2006
Brand Protection Manager
Epson Europe B.v. Jan 2002 - Jun 2004
Senior Commercial Marketing Manager - Consumables Emea
Education:
Uc San Diego Extension 2007 - 2008
Central Michigan University 1987 - 1992
Bachelors, Bachelor of Science, International Relations, Political Science
Uc San Diego
Skills:
Product Marketing Product Management Go To Market Strategy Management Cross Functional Team Leadership Enterprise Software Intellectual Property Channel Partners E Commerce Fraud Cloud Computing Business Intelligence Business Development Business Strategy Licensing Japanese Language Proficiency Test Brand Awareness Programs Contract Negotiation Channel Demand Generation Competitive Analysis Partner Management Multi Channel Marketing Business Alliances Domain Names
Languages:
Japanese
Certifications:
Tefl (Teaching English As Foreign Language) Certification Jlpt (Japanese-Language Proficiency Test) - Level 2 Proficiency
First 5 Santa Clara County
Early Learning Data Coordinator
Second Family Childcare 2005 - Apr 2018
Program Administrator at Second Family Child Care
Digital Media Academy May 2015 - Aug 2015
Logistics Coordinator
California State University, East Bay Aug 2012 - Jul 2014
Data Manager For Hayward Promise Neighborhood
United Administrative Services 2009 - 2009
Programmer Analyst
Education:
San Francisco State University 1982 - 1985
Bachelors, Bachelor of Science, Computer Science
Bronx Hs of Science 1974 - 1978
Bronx High School of Science
Skills:
Program Management Databases Software Documentation Analysis Software Project Management Integration Project Management Training Software Development Sql Quality Assurance Data Analysis Team Management Leadership Business Intelligence Computer Science Software Quality Assurance Technical Writing Logistics Event Planning Programming Enterprise Resource Planning Social Services Business Manager Python Tutoring Process Improvement Rpg Standardized Test Preparation Volleyball Coaching Soccer Coaching
Daniel Dwight Grove - Mercer Island WA, US Ivan Posva - Menlo Park CA, US Jack H. Choquette - Mountain View CA, US Jeffrey Gee - Daly City CA, US
Assignee:
Azul Systems, Inc. - Mountain View CA
International Classification:
G06F 11/00
US Classification:
714 47, 714 50, 714 38, 711163
Abstract:
Detecting a race condition is disclosed. An indication of a store operation to a memory address is received. An identifier of the memory address is stored. The identifier is used to detect an occurrence of a memory operation that is not associated with a previous ordering operation.
Daniel Dwight Grove - Mercer Island WA, US Ivan Posva - Menlo Park CA, US Jack H. Choquette - Mountain View CA, US Jeffrey Gee - Daly City CA, US
Assignee:
Azul Systems, Inc. - Sunnyvale CA
International Classification:
G06F 11/00
US Classification:
714 50, 714 471, 714 3811, 711163
Abstract:
Detecting a race condition is disclosed. An indication of a store operation to a memory address is received. An identifier of the memory address is stored. The identifier is used to detect an occurrence of a memory operation that is not associated with a previous ordering operation.
Method For Tracking Placement Of Products On Shelves In A Store
- South San Francisco CA, US Mirza Akbar Shah - South San Francisco CA, US Lorin Vandegrift - South San Francisco CA, US Luke Fraser - South San Francisco CA, US Jariullah Safi - South San Francisco CA, US Jeffrey Gee - South San Francisco CA, US
One variation of a method for tracking placement of products in a store includes: accessing an image recorded by a mobile robotic system within a store; detecting a shelf in a region of the image; based on an address of the shelf, retrieving a list of products assigned to the shelf by a planogram of the store; retrieving a set of template images—from a database of template images—defining visual features of products specified in the list of products; extracting a set of features from the region of the image; determining that a unit of the product is mis-stocked on the shelf in response to deviation between the set of features and features in a template image, in the set of template images, representing the product; and in response to determining that the unit of the product is mis-stocked on the shelf, generating a restocking prompt for the product.
Method For Automatically Generating Planograms Of Shelving Structures Within A Store
- South San Francisco CA, US Mirza Akbar Shah - San Francisco CA, US Jariullah Safi - San Francisco CA, US Luke Fraser - San Francisco CA, US Lorin Vandegrift - San Francisco CA, US Jeffrey Gee - San Francisco CA, US
One variation of a method for automatically generating a planogram for a store includes: dispatching a robotic system to autonomously navigate within the store during a mapping routine; accessing a floor map of the floor space generated by the robotic system from map data collected during the mapping routine; identifying a shelving structure within the map of the floor space; defining a first set of waypoints along an aisle facing the shelving structure; dispatching the robotic system to navigate to and to capture optical data at the set of waypoints during an imaging routine; receiving a set of images generated from optical data recorded by the robotic system during the imaging routine; identifying products and positions of products in the set of images; and generating a planogram of the shelving segment based on products and positions of products identified in the set of images.
Recommendations Using Session Relevance And Incremental Learning
- Redmond WA, US Konstantin Salomatin - San Francisco CA, US Jeffrey Douglas Gee - San Francisco CA, US Onkar Anant Dalal - Santa Clara CA, US Gungor Polatkan - San Jose CA, US Sara Smoot Gerrard - Redwood City CA, US Deepak Kumar - Mountain View CA, US Rupesh Gupta - Sunnyvale CA, US Jiaqi Ge - Sunnyvale CA, US Lingjie Weng - Sunnyvale CA, US Shipeng Yu - Sunnyvale CA, US
In some embodiments, a computer system generates a recommendation for a user of an online service based on user actions that have been performed by the user within a threshold amount of time before the generation of the recommendation. For each user action, the computer system determines an intent classification that identifies an activity of the user and that corresponds to different types of user actions, as well as a preference classification that identifies a target of the activity, and then stores these intent and preference classifications as part of indications of the user actions for use in generating different types of recommendations using different types of recommendation models. Additionally, the computer system may use mini-batches of data from an incoming stream of logged data to train an incremental update to one or more recommendation models.
Method For Automatically Generating Waypoints For Imaging Shelves Within A Store
- San Francisco CA, US Bradley Bogolea - San Francisco CA, US Jeffrey Gee - San Francisco CA, US Jariullah Safi - San Francisco CA, US Luke Fraser - San Francisco CA, US
One variation of a method for automatically generating waypoints for imaging shelves within a store includes: dispatching a robotic system to autonomously generating a map of a floor space within the store; accessing an architectural metaspace defining target locations and addresses of the set of shelving structures within the store; distorting the architectural metaspace into alignment with the map to generate a normalized metaspace representing real locations and addresses of the set of shelving structures in the store; defining a set of waypoints distributed longitudinally along and offset laterally from a first shelving structure represented in the normalized metaspace based on a known position of an optical sensor in the robotic system; and dispatching the robotic system to record optical data while occupying the set of waypoint optv-mo3 in:#finance s during an imaging routine.
One variation of a method for tracking stock level within a store includes: at a robotic system, navigating along a first inventory structure in the store, broadcasting radio frequency interrogation signals according to a first set of wireless scan parameters, and recording a first set of wireless identification signals returned by radio frequency identification tags coupled to product units arranged on the first inventory structure; generating a first list of product units arranged on the first inventory structure based on the first set of wireless identification signals; detecting a first product quantity difference between the first list of product units and a first target stock list assigned to the first inventory structure by a planogram of the store; and generating a stock correction prompt for the first inventory structure in response to the first product quantity difference.
Method For Stock Keeping In A Store With Fixed Cameras
- South San Francisco CA, US Mirza Akbar Shah - San Francisco CA, US Lorin Vandegrift - San Francisco CA, US Luke Fraser - San Francisco CA, US Jariullah Safi - San Francisco CA, US Jeffrey Gee - San Francisco CA, US Durgesh Tiwari - San Francisco CA, US
One variation of a method for stock keeping in a store includes: accessing an image captured by a fixed camera within the store; retrieving a field of view of the fixed camera; estimating a segment of an inventory structure in the store depicted in the image based on a projection of the field of view onto a planogram of the store; identifying a set of slots within the inventory structure segment; retrieving a product model representing a set of visual characteristics of a product type assigned to a slot, in the set of slots, by the planogram; extracting a constellation of features from the image; if the constellation of features approximates the set of visual characteristics in the product model, detecting presence of a product unit of the product type occupying the inventory structure segment; and representing presence of the product unit, occupying the inventory structure segment, in a realogram.