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Barbara Engelhardt
Born
Barbara Elizabeth Engelhardt
Alma mater Stanford University (BS, MS) University of California, Berkeley (PhD)
Awards Overton Prize (2021)
Scientific career
Fields Statistical genetics
Bayesian statistics
Machine learning
Statistical inference
Genomics
Institutions Princeton University
Chicago University
Jet Propulsion Laboratory
Thesis Predicting protein molecular function (2007)
Doctoral advisor Michael I. Jordan

Barbara Elizabeth Engelhardt is an American computer scientist and expert in bioinformatics. Bioinformatics is a field that uses computer science to understand biology. She is a professor at Stanford University. Her work helps us understand complex biological data, especially from our genes. In 2021, she won the Overton Prize for her important research.

Education

Barbara Engelhardt studied at Stanford University. There, she earned two degrees. She received a Bachelor of Science in Symbolic Systems. She also got a Master of Science in Computer Science.

Later, she went to the University of California, Berkeley. She earned her PhD in 2008. Her doctoral advisor was Michael I. Jordan, a well-known computer scientist.

Career and Research

Before becoming a professor, Engelhardt worked at the Jet Propulsion Laboratory. This is a famous NASA center. She helped design plans for robots in space. After that, she focused on biology and computers.

Early Research

During her PhD, she created computer models. These models helped predict what proteins do. Proteins are tiny machines inside our bodies. She also developed ways to organize biological information.

After her PhD, she worked at the University of Chicago. She studied how to find hidden patterns in genetic data. This helps us understand human populations better.

Professor and Genomics

In 2011, Engelhardt became a professor at Duke University. Then, in 2014, she moved to Princeton University. She became a full professor there in 2017. In 2022, she joined Stanford University. She also works at the Gladstone Institute of Data Science and Biotechnology.

Her main research involves using computers to explore genomic data. Genomic data is information from our DNA. She develops special computer models called latent variable models. These models help find hidden connections in large datasets.

She also studies quantitative trait locus (QTLs). QTLs are parts of our DNA that affect traits, like how tall you are. Her group helped discover important QTLs in a big project. This project was called the Genotype Tissue Expression (GTEx) Consortium.

New Discoveries

More recently, her work has expanded. She now uses her models to study individual cells. This is called single cell sequencing. She also looks at spatial transcriptomics. This helps scientists understand where cells are located in tissues.

She also works on Bayesian experimental design. This helps scientists plan experiments better. It can even help find new species. Her research also uses machine learning to study electronic healthcare records. This can help doctors understand patient information better.

Barbara Engelhardt's work has been featured in Quanta Magazine. In 2017, she gave a TEDx talk. It was titled 'Not What but Why: Machine Learning for Understanding Genomics.'

Honors and Awards

Barbara Engelhardt has received many awards for her research. The National Institutes of Health has funded much of her work.

Some of her awards include:

  • An Alfred P. Sloan Fellowship in Computational Biology.
  • A National Science Foundation CAREER Award.
  • Two Chan Zuckerberg Initiative grants for the Human Cell Atlas. This project aims to map every human cell.
  • A Fast Grant for her work on COVID-19.

In 2021, she received the Overton Prize. This is a major award from the International Society for Computational Biology. It recognizes young scientists who have made important contributions.

Service and Leadership

Engelhardt is also a leader in the scientific community. She served on the Board of Directors for Women in Machine Learning. She also helps promote diversity at the International Conference on Machine Learning (ICML). In 2019, she advised the National Institutes of Health on AI.

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