PeopleofSuperintelligence

Researcher

Alexis Battle

Alexis Battle is a Johns Hopkins researcher whose work applies machine learning to genomics and human health. She studied Symbolic Systems and computer science at Stanford, worked as a software engineer and manager at Google, and joined Johns Hopkins in 2014. She co-developed RIVER, a Bayesian model for rare genetic variants, and co-authored a Bayesian multi-view clustering method for complex genomic data. As of October 2026, she is the Wu and Zhang Professor of biomedical engineering and computer science at Johns Hopkins and a strategic advisor for partnerships at the Data Science and AI Institute.

Works
272
Citations
36,354
h-index
62
Badges

Publication numbers from OpenAlex, as of 18 October 2026.

Timeline

  1. 2003–2014

    From Stanford to Johns Hopkins

    At Stanford, she earns a bachelor’s degree in Symbolic Systems in 2003 and completes a computer-science PhD in 2013. Before joining Johns Hopkins in 2014, she works as a software engineer and manager at Google and completes postdoctoral research at Stanford.

  2. 2016–2017

    Searle and Catalyst awards

    She is named a Searle Scholar in 2016. In 2017, a Johns Hopkins Catalyst Award supports her work using genomic data to predict rare Mendelian disease variants.

  3. 2017

    RIVER and rare genetic variants

    In 2017, Battle and her collaborators develop RIVER, a Bayesian model that uses gene-expression data to predict regulatory effects of rare variants more accurately than models based on genomic annotations alone.

  4. 2019

    A Microsoft Investigator Fellowship

    She receives the 2019 Microsoft Investigator Fellowship. Her research group uses machine learning and probabilistic methods to analyze large-scale genomic data and study the effects of genetic variation.

  5. 2022

    Bayesian multi-view clustering

    She and Benjamin D. Shapiro publish a Bayesian multi-view clustering method in 2022. It models complex links among data types and improves clusters of breast-cancer multi-omics data.

  6. 2022

    A President’s Frontier Award

    Johns Hopkins awards her the President’s Frontier Award in 2022.

  7. February 2026

    AI models for cancer research

    In February 2026, she works with the Sidney Kimmel Comprehensive Cancer Center to develop AI models that aim to predict patient outcomes and treatment responses. Her approach analyzes genetic and clinical data for patterns that can inform care.

  8. October 2026

    A DSAI partnership role

    She is the Wu and Zhang Professor of biomedical engineering and computer science at Johns Hopkins. In October 2026, Johns Hopkins appoints her strategic advisor for partnerships at the Data Science and AI Institute, with a brief to strengthen ties across Johns Hopkins, industry, government, and nonprofit organizations.

Career

  1. Since 2026Johns Hopkins UniversityWu and Zhang Professor of Biomedical Engineering and Computer Science
  2. Until 2014GoogleSoftware Engineer and Manager
  3. 2013–2014Stanford UniversityPostdoctoral Researcher

Education

  1. 2013Stanford UniversityPhD in Computer Science
  2. 2003Stanford UniversityBachelor's degree in Symbolic Systems

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

Most-cited works

  1. The GTEx Consortium atlas of genetic regulatory effects across human tissues2020 · Science · 5,475 citations
  2. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans2015 · Science · 5,056 citations
  3. Genetic effects on gene expression across human tissues2017 · Nature · 4,026 citations
  4. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression2021 · Nature Genetics · 2,568 citations
  5. Efficient sparse coding algorithms2007 · The MIT Press eBooks · 2,448 citationsWith Andrew Ng

From OpenAlex, as of 18 October 2026.

  1. Stanford University trained or employed 27 of the 111 AI major-prize winners, more than any other universityIncludes Stanford University. Stanford University trained or employed 27 of the 111 AI major-prize winners, more than any other university. We compare doctorate and employment links for major-prize and best-paper winners.
  2. Google has the highest combined h-index among AI organizationsIncludes Johns Hopkins University. Our estimates of combined and median h-index at AI organizations, using current affiliations and OpenAlex records.
  3. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Johns Hopkins University. Our estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.
  4. The company share of AI researchers’ top papers rose from 3% to 32%Includes Johns Hopkins University. We estimate where leading AI researchers worked when their most-cited papers appeared. The company share rose from 3% in 1975–1979 to 32% in 2020–2024.

Colleagues

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

Sources and credits

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  1. Alexis Battle - Department of Computer Science cs.jhu.edu. Checked 18 October 2026.
  2. Microsoft Investigator Fellowship - Microsoft Research microsoft.com. Checked 18 October 2026.
  3. The impact of rare variation on gene expression across tissues nature.com. Checked 18 October 2026.
  4. Bayesian Multi-View Clustering given complex inter-view structure f1000research.com. Checked 18 October 2026.
  5. AI decoder: Unlocking cancer’s hidden patterns - Department of Computer Science cs.jhu.edu. Checked 18 October 2026.
  6. CS faculty appointed DSAI leaders to advance research, education, collaboration - Department of Computer Science cs.jhu.edu. Checked 18 October 2026.