PeopleofSuperintelligence

Researcher

Deepti Gurdasani

Deepti Gurdasani is an epidemiologist and machine-learning researcher whose work spans African population genetics and AI-based outbreak surveillance. She trained in internal medicine at Christian Medical College Vellore and completed a PhD in genetic epidemiology at Cambridge before joining the Wellcome Sanger Institute in 2013. Her MRC-funded work at Queen Mary applied machine learning to genomic and clinical data, and at UNSW's Kirby Institute she co-supervised research built on EPIWATCH, an AI-driven outbreak early-warning system. As of October 2026, she supervises honours research at the Kirby Institute in artificial intelligence, natural language processing, epidemiology, bioinformatics and genomics.

Works
109
Citations
10,202
h-index
34
Badges

Publication numbers from OpenAlex, as of 19 October 2026.

Timeline

  1. By 2013

    Clinical training and a PhD

    By 2013, she completes clinical training in internal medicine at Christian Medical College Vellore and earns a PhD in genetic epidemiology at the University of Cambridge.

  2. 2013

    African population genetics at Sanger

    She joins the Wellcome Sanger Institute's Sandhu Group as a postdoctoral fellow in 2013, studying genetic diversity and population history among African populations.

  3. 2019–2021

    Predictive analytics with machine learning

    From 2019 to 2021, the MRC funds her Queen Mary University of London project on predictive analytics of integrated genomic and clinical data using machine learning and complex statistical approaches.

  4. 2021

    Senior lecturer at Queen Mary

    In 2021, she is a senior lecturer in machine learning at Queen Mary University of London and writes Guardian pieces on vaccination, school safety and COVID-19 restrictions.

  5. 2023

    EPIWATCH outbreak surveillance

    In 2023, she co-supervises Damian Honeyman's research on EPIWATCH, an AI-driven outbreak early-detection and monitoring system that uses open-source intelligence.

  6. 2025

    An EPIWATCH study of 310 outbreaks

    In February 2025, she coauthors a study that uses EPIWATCH, an AI-based open-source intelligence system, to describe 310 outbreaks of unknown cause in 2020–2022. She directs the study's analysis and methodology.

  7. As of October 2026

    Honours supervision at the Kirby Institute

    As of October 2026, she supervises honours research at UNSW's Kirby Institute in natural language processing, artificial intelligence, epidemiology, bioinformatics and genomics.

Career

  1. Since 2023UNSW SydneyCo-supervisor
  2. 2023–2025University of Western Australia
  3. 2023–2025Kirby Institute
  4. 2019–2021Queen Mary University of LondonSenior lecturer
  5. 2013–2024Wellcome TrustPostdoctoral fellow

Education

  1. 2013Christian Medical College, VelloreInternal medicine training
  2. 2013University of CambridgePhD

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

Most-cited works

  1. Discovery and refinement of loci associated with lipid levels2013 · Nature Genetics · 2,980 citations
  2. Common variants associated with plasma triglycerides and risk for coronary artery disease2013 · Nature Genetics · 864 citations
  3. The African Genome Variation Project shapes medical genetics in Africa2014 · Nature · 636 citations
  4. A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness2014 · PLoS Genetics · 590 citations
  5. Rare variant in scavenger receptor BI raises HDL cholesterol and increases risk of coronary heart disease2016 · Science · 543 citations

From OpenAlex, as of 19 October 2026.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes UNSW Sydney. Our estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.
  2. The company share of AI researchers’ top papers rose from 2% to 32%Includes Queen Mary University of London. We estimate where leading AI researchers worked when their most-cited papers appeared. The company share rose from 2% in 1975–1979 to 32% in 2020–2024.
  3. Google has the highest combined h-index among AI organizationsIncludes Queen Mary University of London. Our estimates of combined and median h-index at AI organizations, using current affiliations and OpenAlex records.
  4. Stanford University trained or employed 20 of the 89 AI major-prize winners, more than any other universityIncludes UNSW Sydney. Stanford University trained or employed 20 of the 89 AI major-prize winners, more than any other university. We compare doctorate and employment links for major-prize and best-paper winners.

Colleagues

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

Sources and credits

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  1. Dr Deepti Gurdasani - HDR UK hdruk.ac.uk. Checked 19 October 2026.
  2. Gurdasani, Deepti - Wellcome Sanger Institute sanger.ac.uk. Checked 19 October 2026.
  3. GtR gtr.ukri.org. Checked 19 October 2026.
  4. Deepti Gurdasani | The Guardian theguardian.com. Checked 19 October 2026.
  5. 2023 David Cooper Scholarship awarded to Damian Honeyman for early pandemic detection research | Kirby Institute kirby.unsw.edu.au. Checked 19 October 2026.
  6. Global Epidemiology of Outbreaks of Unknown Cause Identified by Open-Source Intelligence, 2020–2022 - Volume 31, Number 2—February 2025 - Emerging Infectious Diseases journal - CDC wwwnc.cdc.gov. Checked 19 October 2026.
  7. ILP & BSc(Med) Honours Supervisors | Medicine Program medprogram.med.unsw.edu.au. Checked 19 October 2026.