معرفی
Barbara Elizabeth Engelhardt is a Professor (Research) of Biomedical Data Science at Stanford University's School of Medicine, with courtesy appointments in Statistics and Computer Science. She is also a Senior Investigator at Gladstone Institutes since 2021. Her academic journey includes Assistant Professor at Duke University (2011-2014) and progressive faculty positions at Princeton University in Computer Science (2014-2022), culminating in a Full Professorship before joining Stanford.
Dr. Engelhardt received her B.S. and M.S. in Symbolic Systems and Computer Science from Stanford University, followed by a PhD from UC Berkeley in EECS under Prof. Michael I Jordan. She completed postdoctoral training with Prof. Matthew Stephens at the University of Chicago. Her professional experience extends to industry roles at Jet Propulsion Labs, Google Research, 23andMe, and Genomics plc.
Her research focuses on developing and applying models for structured biomedical data to capture patterns, predict intervention results, assist decision-making, and prioritize experiments for biological system design. Key areas include spatial genomics, Gaussian processes, single-cell RNA sequencing analysis, and computational methods for biomedical applications. Her work bridges statistics, machine learning, and biomedical research to address complex challenges in understanding biological systems.
Dr. Engelhardt's publications reveal a strong emphasis on developing novel statistical and machine learning methods for biomedical data analysis, particularly in spatial genomics, single-cell sequencing, and clinical applications. Her recent work shows increasing focus on translational applications in healthcare, including critical care decision support and patient trajectory modeling.
- NSF GRFP
- Google Anita Borg Scholarship
- SMBE Walter M. Fitch Prize (2004)
- Sloan Faculty Fellowship
- NSF CAREER
- ISCB Overton Prize (2021)
- International Society for Computational Biology Fellow (March, 2024)
As an advisor, Dr. Engelhardt mentors numerous doctoral students across multiple institutions, serving as Dissertation Reader, Advisor, and Co-Advisor. She has secured significant research funding through prestigious awards like the NSF CAREER. Her lab focuses on developing computational methods that address fundamental challenges in biomedical data science, with particular emphasis on spatially resolved genomic data and clinical applications of machine learning.
Dr. Engelhardt leads research teams working at the intersection of statistics, machine learning, and biomedical applications, with ongoing projects in spatial genomics, single-cell analysis, and clinical decision support systems. Her group develops open-source software tools to make their methodologies accessible to the broader research community.
