PeopleofSuperintelligence

Researcher

Gregory W. Wornell

Also known as Gregory Wornell, G W Wornell, G. W. Wornell, G. Wornell, G.W. Wornell, Greg Wornell

Gregory W. Wornell is an MIT professor who applies information theory to machine learning and wireless communication; he earned degrees at the University of British Columbia and MIT and joined MIT's EECS faculty in 1991. He coauthored work on cooperative diversity protocols for wireless networks in 2004, machine-learning confidence methods in 2023, information criteria for Gibbs learning in 2024, and deep-learning approaches to RF interference in 2025. He received IEEE's 2019 Leon K. Kirchmayer Graduate Teaching Award and, as of October 2026, is the Sumitomo Electric Industries Professor in Engineering at MIT.

Works
496
Citations
35,201
h-index
63
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 1985–1991

    Electrical engineering at UBC and MIT

    He earns the BASc in electrical engineering from the University of British Columbia in 1985, then SM and PhD degrees from MIT in 1987 and 1991; he joins MIT's EECS faculty in 1991.

  2. 1992–1996

    AT&T leave and Green professorship

    During the 1992–1993 academic year, he takes leave at AT&T Bell Laboratories. In June 1996, he is selected as the next holder of MIT's Cecil and Ida Green Career Development Professorship, which begins in July 1996.

  3. 2004

    Cooperative diversity protocols

    In 2004, with J. Nicholas Laneman and David Tse, he coauthors a paper on cooperative diversity for wireless networks. Their relaying protocols use cooperating terminals to forward signals and improve message reliability across multiple propagation paths.

  4. 2019

    Graduate teaching award

    In 2019, he receives the IEEE Leon K. Kirchmayer Graduate Teaching Award for mentoring research students and developing graduate curricula that incorporate research perspectives.

  5. 2023

    Estimating machine-learning confidence

    In 2023, he coauthors a technique for machine-learning models to estimate prediction uncertainty using no additional data and fewer computing resources.

  6. 2024

    Gibbs criteria for over-parameterized learning

    In 2024, with Haobo Chen and Yuheng Bu, he proposes information-theoretic AIC and BIC criteria for Gibbs learning and extends the analysis to over-parameterized random-feature models.

  7. 2025

    Deep learning for RF interference

    In 2025, he coauthors the RF Challenge paper, which uses deep-learning methods for radio-frequency interference rejection and introduces a public dataset for RF signal analysis.

  8. As of October 2026

    Professor in MIT EECS

    As of October 2026, he is the Sumitomo Electric Industries Professor in Engineering in MIT's EECS department and a principal investigator of the Signals, Information, and Algorithms Laboratory. His research areas include AI and machine learning, while the laboratory's current interests include information-theoretic methods and algorithms for machine learning and high-dimensional statistical inference.

Career

  1. Since 1991Massachusetts Institute of TechnologyProfessor in EECS, Cecil and Ida Green Career Development Professor
  2. 1992–1993Bell Labs

Education

  1. 1991Massachusetts Institute of TechnologyPhD
  2. 1987Massachusetts Institute of TechnologySM
  3. 1985University of British ColumbiaBASc

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Massachusetts Institute of Technology. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Massachusetts Institute of Technology. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. Google had the most people shaping AI every year from 2014 to 2025Includes Massachusetts Institute of Technology. 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.
  4. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Massachusetts Institute of Technology. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Awards and Honors news.mit.edu. Checked 21 October 2026.
  2. Three named to CD chairs news.mit.edu. Checked 21 October 2026.
  3. Journal Articles - Signals, Information, and Algorithms Laboratory sia.mit.edu. Checked 21 October 2026.
  4. Signals, Information, and Algorithms Laboratory - Research sia.mit.edu. Checked 21 October 2026.
  5. 2019 IEEE Technical Field Award Recipients Announced signalprocessingsociety.org. Checked 21 October 2026.
  6. Efficient technique improves machine-learning models’ reliability eecs.mit.edu. Checked 21 October 2026.
  7. Gibbs-Based Information Criteria and the Over-Parameterized Regime proceedings.mlr.press. Checked 21 October 2026.
  8. [2409.08839] RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge arxiv.org. Checked 21 October 2026.
  9. Gregory Wornell - MIT EECS eecs.mit.edu. Checked 21 October 2026.
  10. Gregory Wornell | MIT CSAIL csail.mit.edu. Checked 21 October 2026.