PeopleofSuperintelligence

Researcher

Eric Price

Eric Price is a computer scientist whose work spans algorithms for sparse signals, machine-learning fairness and language models. With MIT collaborators, he developed the sparse Fourier transform in 2012, completed a computer science PhD at MIT in 2013 and received the George Sprowls doctoral dissertation award. After postdoctoral work at the Simons Institute and IBM Almaden, he joined UT Austin in 2014 and coauthored a paper on fair supervised learning in 2016. He coauthored Microsoft's 2024 Phi-4 technical report, which introduced a 14-billion-parameter language model. He is an associate professor at UT Austin on leave at Microsoft Research, where he works on language models; he also coauthored 2025 research on diffusion-model sampling and 2026 research on comparing autoregressive language models.

Links
Website and Google Scholar

Timeline

  1. 2012

    Sparse Fourier transform

    With Piotr Indyk, Dina Katabi and Haitham Hassanieh, he develops a sparse Fourier transform for data streams; Technology Review includes it on its 2012 list of 10 emerging technologies.

  2. 2013

    An MIT PhD and thesis award

    He completes a computer science Ph.D. at MIT in 2013 and receives the George M. Sprowls Doctoral Dissertation Award.

  3. 2013–2014

    Postdoctoral work at UT Austin

    After graduating in 2013, he works as a postdoc at the Simons Institute in Berkeley and the IBM Almaden Research Center before joining UT Austin as an assistant professor in Fall 2014.

  4. 2016

    Fairness in supervised learning

    In 2016, he coauthors Equality of Opportunity in Supervised Learning with Moritz Hardt and Nathan Srebro; the paper proposes a criterion for discrimination tied to a sensitive attribute.

  5. 2024

    The Phi-4 technical report

    In 2024, he coauthors Microsoft's Phi-4 technical report, which introduces a 14-billion-parameter language model developed with a focus on data quality.

  6. October 2025

    Diffusion models for posterior sampling

    In October 2025, he coauthors Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics, which proves conditional sampling in polynomial time.

  7. July 2026

    Language model distance estimation

    In July 2026, he coauthors Total Variation Distance Estimation in Autoregressive Models, which studies estimating the total variation distance between two autoregressive language-model distributions.

  8. Since 2024

    Language models at Microsoft Research

    He is an associate professor at UT Austin on leave at Microsoft Research, where he works on language models; the leave begins in Fall 2024.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

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. Faster Fourier transform named one of world’s most important emerging technologies news.mit.edu. Checked 19 October 2026.
  2. New Faculty Profile: Eric Price cs.utexas.edu. Checked 19 October 2026.
  3. Awards toc.csail.mit.edu. Checked 19 October 2026.
  4. Eric Price cs.utexas.edu. Checked 19 October 2026.
  5. Equality of Opportunity in Supervised Learning arxiv.org. Checked 19 October 2026.
  6. Phi-4 Technical Report microsoft.com. Checked 19 October 2026.
  7. Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics arxiv.org. Checked 19 October 2026.
  8. Total Variation Distance Estimation in Autoregressive Models arxiv.org. Checked 19 October 2026.