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Casey Greene

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Casey Greene Named Director of New Center for Health Artificial Intelligence

Author School of Medicine | Publish Date October 20, 2020

Casey Greene, PhD, has been named director of the new Center for Health Artificial Intelligence at the University of Colorado School of Medicine, where he will lead the creation of a center building communities that use sophisticated data analysis methods to advance research and improve clinical practice on the Anschutz Medical Campus.

Greene, who has been an associate professor of systems pharmacology at the University of Pennsylvania Perelman School of Medicine and director of the Childhood Cancer Data Lab for Alex’s Lemonade Stand Foundation, will also be a professor of biochemistry and molecular genetics at the University of Colorado School of Medicine. He joins the CU faculty effective November 16.

“Casey brings a robust research
program and impressive record
of accomplishment to the
Anschutz Medical Campus and
we welcome him to our faculty.”
- John J. Reilly, Jr., MD

“Casey brings a robust research program and impressive record of accomplishment to the Anschutz Medical Campus and we welcome him to our faculty,” said John J. Reilly, Jr., MD, dean of the School of Medicine and vice chancellor for health affairs. “He will be a key leader of one of the most important initiatives on our campus for our future: to expand our use of the data we collect, understand its connections to existing information, and enrich our ability to apply what we learn.”

Greene is an experienced leader in the field of data analytics. After completing his PhD in computational genetics at Dartmouth College 2009, Greene was a computational biology and functional genomics postdoctoral fellow at the Lewis-Sigler Institute of Integrative Genomics at Princeton University until 2012. He joined the Dartmouth faculty that year and moved to University of Pennsylvania School of Medicine in 2015.

The Greene Lab develops algorithms that integrate publicly available data from multiple datasets to help model and understand complex biological systems. This approach allows investigators to infer the key contextual information required to interpret the data, and facilitates the process of asking and answering basic science and translational research questions

“We have existing public data resources that are vast, but they are very challenging to analyze,” Greene said. “That’s partly because when we upload the data we don’t know what other people are going to want to do with it. Even when we’re trying to be helpful and put as much information there as possible, people will leave out things that to us weren’t important but might be hugely meaningful to people who are later trying to do an analysis.”

Greene’s goal is to better connect disparate sets of research data so that useful information doesn’t go to waste. Billions of dollars have been invested in studies to produce these datasets, so making better connections between their methods and results will provide bonus returns on those investments.

“We recognize that our lab won’t have all the answers, or even all of the questions, so we aim to develop tools that any biologist can reuse,” Greene said. “Our approach to research prioritizes transparency, rigor, and reproducibility.

“Our lab wants these data to be as easy to use as it is to find cat videos on the internet,” he quipped.

Greene is the author or co-author of more than 60 articles in peer-reviewed journals and multiple editorials, reviews, chapters and reports, including a comment published last week in Nature that calls for researchers in the AI community to share computer code. Just as the results of a laboratory experiment depend on reproducibility for verification, “transparency in the form of the actual computer code used to train a model and arrive at its final set of parameters is essential for research reproducibility.”

The Center for Health AI will build new capabilities on campus for advanced data analysis while complementing existing data and analysis centers of excellence including the Colorado Center for Personalized Medicine and the Center for Innovative Design and Analysis. Artificial intelligence approaches have already dramatically transformed our lives and our interactions with technology. Health care and biomedical research are not far behind.

“Our goal is to develop techniques that advance research and practice using artificial intelligence as a bridge to reveal unexpected connections,” said Greene. “To do this in a way that promotes equity and improved care, we must develop approaches that are both predictive and interpretable, which will be key areas of focus for the center.”

Upgrading the quality of data analytics on the Anschutz Medical Campus is a major priority for the School of Medicine that Reilly outlined in his State of the School address in January 2020.

“It does not make any sense to have made the investments in all these new technologies and new science on the campus … and not be able to process the data with state-of-the-art techniques,” Reilly said. “We’re going to have to start recruiting people, both a workforce to meet the service needs as well as people who have the capacity to grow into thought leaders and developers in these fields, and that will be a primary focus for us in the years ahead.”