Jenna Wiens named new Precision Health co-director


Precision Health at the University of Michigan has announced that Jenna Wiens, assistant professor of computer science and engineering in the College of Engineering, will become a co-director effective Sept. 1.

Photo of Jenna Weins
Jenna Weins

She joins co-directors Sachin Kheterpal, professor of anesthesiology and associate dean for research information technology in the Medical School; and Michael Boehnke, Richard G. Cornell Distinguished University Professor of Biostatistics, professor of biostatistics, and director of the Center for Statistical Genetics and the Genome Science Training Program at the School of Public Health.

Weins succeeds Eric Michielssen, Louise Ganiard Johnson Professor of Engineering and professor of electrical engineering and computer science in CoE, who is stepping down as co-director to take sabbatical leave.

“It has been an immense honor and pleasure having been part of the creation of Precision Health at Michigan,” said Michielssen. “Great strides have been made in the creation of a campus community around this new research paradigm and the infrastructure and tools required for success. I could not be more pleased to have Jenna join the leadership team and take the initiative to the next level.”

Wiens is transitioning to co-director from a successful role as a co-lead for Precision Health’s Data Analytics & IT Workgroup, which expanded access to data and research tools across the university with their launch of the Precision Health Analytics Platform.

“As co-lead for the Data Analytics & IT Workgroup, I’ve worked hard to improve ‘data liquidity’ across campus, by facilitating access to secure HIPAA-aligned compute environments,” Wiens said. “This infrastructure will make it easier for researchers across schools and colleges to work together on health data and promises to accelerate interdisciplinary collaborations at U-M.”

Wiens also possesses a strong track record in precision health research, particularly her work developing hospital-specific machine learning models to predict the spread and severity of Clostridium difficile infection among patients.

“Hospitals today are collecting an immense number of patient data — images, lab tests, vitals — but are still ignoring the vast majority,” she said. “My research aims to develop the computational methods needed to help organize, process and transform these data into actionable knowledge, with the ultimate goal of improving patient care.”


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