Michele Riva

Michele Riva

San Francisco, California, United States
11K followers 500+ connections

About

Software engineer and technology leader with over 12 years of experience building…

Experience

  • Orama Graphic

    Orama

    San Francisco, California, United States

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    Geneva, Switzerland

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    San Francisco, California, United States

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    Waterford, County Waterford, Ireland

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    Waterford, County Waterford, Ireland

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    New York, United States

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    Monza e Brianza, Italia

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    Monza e Brianza, Italia

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    Agrate Brianza, Lombardy, Italy

Education

  • Purdue University Graphic

    Purdue University

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    Honors: Dean's List (3.98 GPA)
    Honor Society: Phi Theta Kappa, Alpha Alpha Alpha
    Clubs: Entrepreneurship, science, and philosophy

    Ongoing (graduation in March 2026)

Licenses & Certifications

Publications

  • Real-World Next.js

    Packt

    Next.js is a scalable and high-performance React.js framework for modern web development. It provides a large set of features out of the box, such as hybrid rendering, route prefetching, automatic image optimization, and internationalization. With this book, you’ll learn how to effectively use this exciting technology for a wide range of purposes, from creating an e-commerce website or a simple website to a blog without compromising on performance or user experience.

    Starting from the…

    Next.js is a scalable and high-performance React.js framework for modern web development. It provides a large set of features out of the box, such as hybrid rendering, route prefetching, automatic image optimization, and internationalization. With this book, you’ll learn how to effectively use this exciting technology for a wide range of purposes, from creating an e-commerce website or a simple website to a blog without compromising on performance or user experience.

    Starting from the basics of Next.js, you will understand how the framework can help you reach your development goals. You’ll also explore Next.js’s versatility by building real-world applications with the help of step-by-step explanations, understanding how to write unit tests and integration tests for an app, integrating different backends with the app, and more. Later, the book shows you how to choose the right rendering methodology for your website, how to secure it, and how to deploy it to different providers.

    By the end of this Next.js book, you’ll have the skills you need to be able to design, build, and deploy modern architectures using Next.js with any headless CMS or data source.

    See publication

Patents

  • Operating a distributed search index in a content delivery network

    Issued 11921759

    A facility for distributing a search index for a corpus of documents is described. The facility accesses multiple search index segments collectively making up the search index. Each of the segments is executable to traverse an index subtree embedded in the segment to find in the index subtree a node representing a query term specified in an argument. Each of the segments corresponds to a particular indexed document field that is indexed by the search index.
    For each of the segments, the…

    A facility for distributing a search index for a corpus of documents is described. The facility accesses multiple search index segments collectively making up the search index. Each of the segments is executable to traverse an index subtree embedded in the segment to find in the index subtree a node representing a query term specified in an argument. Each of the segments corresponds to a particular indexed document field that is indexed by the search index.
    For each of the segments, the index subtree embedded the segment has nodes representing query terms that all produce a particular hash result. The facility calls a programmatic publication interface for a content delivery network to publish the plurality of search index segments on the content delivery network.

    See patent
  • Operating in a content delivery network a distributed search index for performing vector search

    Filed US20250225170A1

    An index shard data structure that is part of a search index is described. The data structure includes an executable routine that takes as an argument a semantic meaning representation of a query, embeds list of mappings from semantic meaning representations each of a different one of the documents of the corpus to a document ID identifying the document, traverses the list of mappings to select the document IDs mapped-to from semantic meaning representations that are within a threshold level of…

    An index shard data structure that is part of a search index is described. The data structure includes an executable routine that takes as an argument a semantic meaning representation of a query, embeds list of mappings from semantic meaning representations each of a different one of the documents of the corpus to a document ID identifying the document, traverses the list of mappings to select the document IDs mapped-to from semantic meaning representations that are within a threshold level of similarity to the semantic meaning representation of a query contained by the argument, and returns a list of the selected document identifiers, and such that a particular shard can be executed with respect to a distinguished query semantic meaning representation to obtain a list of document identifiers that identify documents of the corpus each having similar semantic meaning representations.

    See patent
  • Operating in a content delivery network a distributed search index based on an automatically-selected set of documents properties

    Filed US20250322019A1

    A facility is described for automatically selecting document properties to be included in an index on a corpus of documents. In some cases, the facility performs this automatic selection by subjecting a list of the document properties for which the documents have contents to an inference model. In some cases, the facility conducts this automatic selection by submitting to a large language model a prompt automatically constructed to include, for each of one or more documents, the document's…

    A facility is described for automatically selecting document properties to be included in an index on a corpus of documents. In some cases, the facility performs this automatic selection by subjecting a list of the document properties for which the documents have contents to an inference model. In some cases, the facility conducts this automatic selection by submitting to a large language model a prompt automatically constructed to include, for each of one or more documents, the document's contents for each document property, in each case together with the document property's name.

    See patent

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