PeopleofSuperintelligence

Researcher

Harrison Wilde

Also known as Harry Wilde

Harrison Wilde is a researcher whose work links machine learning with synthetic data, privacy and healthcare. The first in his family to attend university, he studied Data Science at Warwick before a Feuer Scholarship opened a path to a PhD. In 2019, he worked on machine-learning models for Homeless Link alerts through a Data Science for Social Good fellowship, and coauthored a paper on outreach recommendations for people sleeping rough. His 2021 research developed Bayesian methods for learning from synthetic data and reducing statistical bias; a 2022 study tested how differential privacy affects feature importance. He contributed to Turing.jl with a 2020 issue on model initialization, built NHS Synth to assess synthetic-data utility, privacy and fairness, and is a senior data scientist at Nesta as of October 2026.

Now at
Nesta

Timeline

  1. 2019

    A fellowship with Homeless Link

    During the 2019 Data Science for Social Good fellowship at the Alan Turing Institute and the University of Warwick, he works with Homeless Link on machine-learning predictions for StreetLink alerts. The models help prioritize alerts for review.

  2. By 2020

    From Data Science to a PhD

    The first in his family to attend university, he is a keen mathematician from a young age. By 2020, he studies Data Science at Warwick and receives a Feuer Scholarship that lets him begin a PhD after a master's programme.

  3. May 2020

    An initialization issue for Turing.jl

    In May 2020, he opens a Turing.jl issue about passing an initial theta into a model. He describes a collaborator's quick fix and shows the code for using it.

  4. 2021

    A paper on outreach recommendations

    In 2021, he coauthors a paper on a recommendation and risk-classification system for connecting people sleeping rough with essential outreach services.

  5. 2021

    Bayesian learning from synthetic data

    In 2021, he coauthors *Foundations of Bayesian Learning from Synthetic Data*, which uses Bayesian decision theory to study learning from synthetic data. He also coauthors work proposing privacy-compliant importance weighting to mitigate bias in differentially private synthetic data.

  6. 2022

    Feature importance under privacy

    In 2022, he coauthors a study comparing feature-importance estimates from raw and differentially private synthetic data. It finds that agreement varies across datasets and privacy levels, and that differences can change modelling decisions in real-world settings.

  7. January 2023–May 2023

    Building NHS Synth

    In the NHS England Data Science PhD Internship from January to May 2023, he builds NHS Synth, a package for generating synthetic data and comparing model architectures across utility, privacy and fairness.

  8. As of October 2026

    Senior data scientist at Nesta

    As of October 2026, he is a senior data scientist at Nesta. Before joining Nesta, he pursues a PhD in Statistics and works as a research fellow in machine learning for health at UCL, with a focus on fair and explainable machine learning.

Career

  1. NowNestaSenior Data Scientist
  2. University College LondonResearch Fellow in Machine Learning for Health

Education

  1. University of Warwick

From the biography.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University College London. 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.
  2. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes University College London. 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.
  3. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University College London. 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.
  4. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes University College London. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.

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 Harrison Wilde yet. Send us a free-licensed photo.

  1. Artificial Intelligence: Meet the students researching its future newswise.com. Checked 21 October 2026.
  2. A recommendation and risk classification system for connecting rough sleepers to essential outreach services cambridge.org. Checked 21 October 2026.
  3. Should the initial parameter samples / values be recorded for each chain · Issue #1282 · TuringLang/Turing.jl github.com. Checked 21 October 2026.
  4. Foundations of Bayesian Learning from Synthetic Data proceedings.mlr.press. Checked 21 October 2026.
  5. Mitigating statistical bias within differentially private synthetic data arxiv.org. Checked 21 October 2026.
  6. Faking feature importance: A cautionary tale on the use of differentially-private synthetic data arxiv.org. Checked 21 October 2026.
  7. Completed Intern Projects – NHS England Data Science PhD Internships nhsx.github.io. Checked 21 October 2026.
  8. Harry Wilde | Nesta nesta.org.uk. Checked 21 October 2026.