Barbara Engelhardt works to improve human health by analyzing the enormous reams of data generated by research labs, doctors, and hospitals. Combining mathematical, statistical, and artificial intelligence approaches, her team seeks to understand the cellular mechanisms of disease, the impact of traumatic life events on health, and the clinical treatments that best correlate with positive health outcomes. The team anchors their work in a large network of collaborations, and generates computational and statistical tools aimed at increasing the impact of both medical research and healthcare practice.

Disease Areas

Heart Disease
Mental Health

Areas of Expertise

Computational Biology
Machine Learning
Statistical Models
Working in the Engelhardt lab

Lab Focus

Integrating cell biology, microscopy, and genetic data to model intercellular communications in both healthy tissues and diseased tissues such as cancer tumors.
Understanding the epigenetic marks left by trauma and downstream risk of mental health problems, obesity, or heart disease.
Developing algorithms that predict the best type of care or course of treatment in the clinical setting, with an emphasis on quantifying how doctors might treat patients differently based on race, sex, or comorbidities.

Research Impact

Medical research has seen an explosion of genomic, single-cell, and imaging data that can only be mined productively with structured and robust statistical and computational approaches. Similarly, modern clinical practice is generating vast troves of electronic records on patients, interventions, and health outcomes that could be used to predict the best course of treatment or to correct health inequities.

Barbara Engelhardt has been tackling these challenges with a combination of statistics and machine learning approaches. Her analysis of electronic health care data has led to a policy for weaning patients from a mechanical ventilator, and another that reduced the number of blood draws for hospital tests by 40 percent and accelerated the diagnosis of sepsis by approximately four hours.

Her work in genomics seeks to understand the genetic basis for complex traits and the cellular mechanisms underlying this relationship. Complex traits, including many diseases, manifest as a continuum of symptoms that reflect dysregulation among cell types, genes, and other factors. Engelhardt has developed tools to explore, identify, and quantify these interactions in large genomic datasets, and to predict the impact of specific interventions. In particular, she is pursuing approaches to incorporate the quantitative output of genes (how much RNA they produce) into her analysis of complex traits, which has led her to identify a gene that protects from muscular myopathy in the context of statin treatment. She is also developing tools to extract biological or disease-relevant information from time series gene expression data, and to identify optimal gene markers to improve the analysis of omic data from single cells.


Professional Titles

Senior Investigator, Gladstone Institutes

Professor, Department of Biomedical Data Science, Stanford University


Barbara Engelhardt, PhD, is a senior investigator at Gladstone Institutes. She is also a professor in the Department of Biomedical Data Science at Stanford University.

Engelhardt opened her lab at Gladstone in 2021. Prior to joining Princeton in 2014, she was an assistant professor in biostatistics and bioinformatics and statistical sciences at Duke University. She graduated from Stanford University, received her PhD in electrical engineering and computer science from UC Berkeley, supported by an NSF Graduate Research Fellowship, and trained as a postdoctoral researcher at the University of Chicago. Engelhardt also spent 2 years working at Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe.

Her research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.

Engelhardt received the 2021 Overton Prize from the International Society for Computational Biology, one of the top awards in this field.

Honors and Awards

2021 Overton Prize, International Society for Computational Biology

2020–2021 Fast Grants for Covid-19 Research

2019–2020 Schmidt DataX Project Award (with Toettcher Lab), Princeton University

2018–2022 Faculty Early Career Development (CAREER) Award, National Science Foundation

2018–2019 Grants for the Human Cell Atlas, Silicon Valley Community Foundation and Chan Zuckerberg Initiative

2017–2019 Helen Shipley Hunt Fund Award, Princeton University

2016–2018 Sloan Research Fellowship, Alfred P. Sloan Foundation

2016 E. Lawrence Keyes, Jr./Emerson Electric Co. Faculty Advancement Award, Princeton University

2015 J. Blair Pyne Fund Award, Princeton University

2013 DIBS Research Incubator Awards, Duke Institute for Brain Sciences

2011–2015 K99 Pathway to Independence Career Award, National Human Genome Research Institute

2005–2006 Google Anita Borg Memorial Scholarship

2004 Walter M. Fitch Prize, Society for Molecular Biology and Evolution

2001 Graduate Research Fellowship, National Science Foundation


More Publications


Barbara Engelhardt

Elizabeth Nguyen
Administrative Assistant

Lab Members

Jacob Chang
Rotation Student
Luis Chumpitaz Diaz
Graduate Student
Eric Ham
Research Associate II
Nhen Hunter
Graduate Student
Didong Li, PhD
Aishwarya Mandyam, MS
Graduate Student
Sarah Nyquist, PhD
Bioinformatics Fellow
Arpita Singhal
Graduate Student
Archit Verma, PhD
Postdoctoral Scholar
Chi-Yun Wu, PhD
Bioinformatics Fellow
Boyang Zhang, PhD
Visiting Postdoc