“I worked under Nima in 1st Half of 2015 and it was a pleasure working for him. Nima is a technological savant and ad-tech guru. The breadth and depth of knowledge Nima has in both front-end and back-end technologies is astonishing. Systems that were designed and architecture by Nima were the most modular, robust and scaled beautifully. Nima has great communication skills and it came naturally to Nima to switch to right abstraction while communicating with anyone from Marketing Director to engineer to CTO. Nima played a pivotal role in building trust and communication channels between business and engineering divisions. Nima is a super nice person and I would love to work with him again.”
Nima Khajehnouri
Mountain View, California, United States
2K followers
500+ connections
About
Leading the AI Data Engineering team at Google, focused on developing comprehensive data…
Activity
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I’m excited to introduce Verishop, our new e-commerce venture that we plan to launch later this year. We’re building an online commerce platform with…
I’m excited to introduce Verishop, our new e-commerce venture that we plan to launch later this year. We’re building an online commerce platform with…
Liked by Nima Khajehnouri
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Excited to be featured in our first episode of Solutions in a Snap! Check out what our incredible product team has been building for eCommerce…
Excited to be featured in our first episode of Solutions in a Snap! Check out what our incredible product team has been building for eCommerce…
Liked by Nima Khajehnouri
Experience
Education
Patents
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Search Monetization of Images Embedded in Text
Issued US 20150262255
Methods and systems for integrating images with the associated text-based content signals and data about users' preferences to determine an image or user intent. Methods and implementations for monetizing these images is also described.
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Concept-level User Intent Profile Extraction and Applications
Issued US US20140067535
Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.
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Generating a conceptual association graph from large-scale loosely-grouped content
Issued US US2011113032
A method for generating a conceptual association graph from structured content includes grouping content nodes into one or more topically biased clusters, the content nodes comprising structured digital content and unstructured digital content, the grouping based at least in part on the connectedness of each content node member to other content node members in the same cluster. The method also includes, responsive to the grouping, tagging the content nodes with one or more descriptive concepts.…
A method for generating a conceptual association graph from structured content includes grouping content nodes into one or more topically biased clusters, the content nodes comprising structured digital content and unstructured digital content, the grouping based at least in part on the connectedness of each content node member to other content node members in the same cluster. The method also includes, responsive to the grouping, tagging the content nodes with one or more descriptive concepts. The method also includes, responsive to the tagging, establishing one or more associations between the one or more concepts, the one or more associations indicating a relevance of the one or more associations, the indicating based at least in part on patterns of co-occurrence of concepts in the tagged content nodes.
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Behavioral Targeting For Tracking, Aggregating, And Predicting Online Behavior
Issued US US2009300009
A pre-computed concept map represents concepts, concept metadata, and relationships between the plurality of concepts. Online user behavior may be predicted by correlating one or more online events of a user with one or more features of the concept map, aggregating a concept map history of the user to obtain online behavior over time, aggregating online behavior of the user and one or more other users to obtain aggregated online user behavior, and predicting future online behavior of the user…
A pre-computed concept map represents concepts, concept metadata, and relationships between the plurality of concepts. Online user behavior may be predicted by correlating one or more online events of a user with one or more features of the concept map, aggregating a concept map history of the user to obtain online behavior over time, aggregating online behavior of the user and one or more other users to obtain aggregated online user behavior, and predicting future online behavior of the user based at least in part on the online behavior of the user and the aggregated online user behavior. The predicted behavior may be used to target ads that the user is likely to find relevant.
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Apache Storm and Kafka Together: A Real-time Data Refinery bit.ly/1J2T6mG #hadoop
Apache Storm and Kafka Together: A Real-time Data Refinery bit.ly/1J2T6mG #hadoop
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