PeopleofSuperintelligence

Researcher

Arthur Asuncion

Also known as Arthur U. Asuncion

Arthur Asuncion is a computer scientist whose research connects topic models, distributed inference and privacy-preserving content systems. He coauthored work on asynchronous topic learning in 2008, topic-model inference in 2009 and distributed Gibbs sampling in 2012, then earned a computer science PhD at UC Irvine in 2011. He also coauthored a 2014 paper about Google's internal machine-learning course and served as a librarian for the UCI Machine Learning Repository by 2023. In July 2026, he was named on a Google privacy patent; as of October 2026, he is at Google in Seattle.

Now at
Google
Works
34
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Publication numbers from OpenAlex, as of 22 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.

Timeline

  1. 2008

    Asynchronous topic learning

    In 2008, he coauthors “Asynchronous Distributed Learning of Topic Models” with Padhraic Smyth and Max Welling. The paper develops asynchronous algorithms for distributed learning with Latent Dirichlet Allocation and hierarchical Dirichlet processes.

  2. 2009

    Smoothing topic models

    In 2009, he coauthors “On Smoothing and Inference for Topic Models” with Max Welling, Padhraic Smyth, and Yee Whye Teh. Their comparison finds that optimized smoothing hyperparameters bring the inference algorithms to similar accuracy, giving researchers room to select computationally efficient methods.

  3. 2011

    A UC Irvine PhD

    At UC Irvine's Smyth Research Group, he earns a PhD in computer science in 2011.

  4. 2012

    Distributed Gibbs sampling

    He coauthors the chapter “Distributed Gibbs Sampling for Latent Variable Models” with Padhraic Smyth, Max Welling, David Newman, Ian Porteous, and Scott Triglia for the 2012 book Scaling up Machine Learning. The volume collects approaches for scaling machine learning and data mining across parallel and distributed platforms.

  5. 2014

    Google's machine-learning course

    In 2014, he coauthors a paper about Google's internal engineering education program. It describes a massive online machine-learning class that combines theory with Google software tutorials and aims to build engineering capacity for future products.

  6. By 2023

    The UCI data repository

    By 2023, he is a past librarian for the UCI Machine Learning Repository, which collects databases, domain theories, and data generators for empirical analysis of machine-learning algorithms.

  7. July 2026

    A Google privacy patent

    In July 2026, Google names him with Wei Huang, Michael William Daub, and Robert F. Day as inventors on its privacy-preserving group-based content distribution patent. The patent describes selecting digital components based on predicted user attributes and keeping group membership on the user's device to protect privacy.

  8. As of October 2026

    Google in Seattle

    As of October 2026, he is at Google in Seattle.

Career

  1. NowGoogle
  2. University of California, IrvineLibrarian

Education

  1. 2011University of California, IrvinePhD

From the biography.

  1. Google had the most people shaping AI every year from 2014 to 2025Includes Google. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  2. Google has the highest combined h-index among AI organizationsIncludes Google. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.
  3. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Google. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  4. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Google. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. Asynchronous Distributed Learning of Topic Models papers.nips.cc. Checked 21 October 2026.
  2. On Smoothing and Inference for Topic Models proceedings.mlr.press. Checked 21 October 2026.
  3. Smyth Research Group (The DataLab) ics.uci.edu. Checked 21 October 2026.
  4. Distributed Gibbs sampling for latent variable models research.google. Checked 21 October 2026.
  5. Corporate learning at scale: Lessons from a large online course at Google research.google. Checked 21 October 2026.
  6. About — UCI Machine Learning Repository archive.ics.uci.edu. Checked 21 October 2026.
  7. US12695728B2 — Privacy preserving group-based content distribution patents.google.com. Checked 21 October 2026.