PeopleofSuperintelligence

Researcher

David Burt

David Burt is a machine learning researcher whose work spans Gaussian process inference and spatial prediction. After studying mathematics at Williams College, he earned an MPhil in Machine Learning, Speech and Language Technology at Cambridge in 2018, began a PhD under Carl Rasmussen, and shared ICML's 2019 Best Paper Award for sparse variational Gaussian process research. He coauthored a paper introducing a lower bound for Gaussian process regression in 2021, examined scalable inference and model selection in his 2022 Cambridge thesis, and coauthored a method for validating spatial predictions in 2025. He was a postdoc in Tamara Broderick's MIT group in November 2025 and is an assistant professor in UMass Amherst's Department of Statistics as of October 2026.

Timeline

  1. 2018

    Mathematics and machine learning

    After a mathematics BA at Williams College, he earns an MPhil in Machine Learning, Speech and Language Technology from Cambridge University in 2018.

  2. 2018

    A Cambridge PhD

    He begins a PhD in Cambridge's Machine Learning Group in 2018 under Professor Carl Rasmussen.

  3. 2019

    An ICML Best Paper Award

    At ICML 2019, he wins the Best Paper Award with Carl E. Rasmussen and Mark van der Wilk for Rates of Convergence for Sparse Variational Gaussian Process Regression.

  4. 2021

    Tighter bounds for Gaussian processes

    In 2021, he coauthors Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients. The paper gives a lower bound without requiring factorisation of the full kernel matrix.

  5. 2022

    A thesis on scalable inference

    His 2022 Cambridge Engineering thesis, Scalable Approximate Inference and Model Selection in Gaussian Process Regression, examines approximate inference and model selection for Gaussian processes. It provides diagnostics for sparse variational inference and conjugate-gradient bounds for model selection.

  6. 2025

    Validation for spatial predictions

    In 2025, he coauthors Consistent Validation for Predictive Methods in Spatial Settings with Yunyi Shen and Tamara Broderick. The paper formalizes a check for spatial validation and proposes a method that passes it as validation data becomes denser.

  7. November 2025

    Postdoc at MIT LIDS

    In November 2025, he is a postdoc in Professor Tamara Broderick's group at MIT's Laboratory for Information and Decision Systems, where he works on validation and uncertainty quantification for spatiotemporal data.

  8. As of October 2026

    Assistant professor at UMass

    As of October 2026, he is an assistant professor in the Department of Statistics at UMass Amherst. He develops methods for validation and uncertainty quantification in spatiotemporal data, with applications to air pollution.

Career

  1. NowUniversity of Massachusetts AmherstAssistant Professor
  2. Massachusetts Institute of TechnologyPostdoc

Education

  1. 2018University of CambridgeMPhil in Machine Learning, Speech and Language Technology
  2. From 2018University of CambridgePhD
  3. Williams CollegeBA

From the biography.

  1. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes University of Massachusetts Amherst. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  2. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of Massachusetts Amherst. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  3. People shaping AI work at a median of 3 organizations over a career: 3 for university starters and institute and lab starters, versus 2 for company startersIncludes University of Massachusetts Amherst. Across 6,853 people shaping AI with a dated work role, we estimate a median of 3 parent organizations over a career (mean 3.6), comparing role groups, starting sectors and starting decades from the 1950s to 2020–2026.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of Massachusetts Amherst. 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.

Colleagues

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

Sources and credits

Photo: We have no published photo of David Burt yet. Send us a free-licensed photo.

  1. David Burt mlg.eng.cam.ac.uk. Checked 21 October 2026.
  2. PhD student David Burt has won the Best Paper Award at the International Conference on Machine Learning eng.cam.ac.uk. Checked 21 October 2026.
  3. Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients proceedings.mlr.press. Checked 21 October 2026.
  4. Consistent Validation for Predictive Methods in Spatial Settings proceedings.mlr.press. Checked 21 October 2026.
  5. Machine Learning and Friends Lunch: David Burt, Consistent Validation for Predictive Methods in Spatial Settings cics.umass.edu. Checked 21 October 2026.
  6. David R. Burt davidrburt.github.io. Checked 21 October 2026.