Scholar
Jenna Wiens
Google Scholar ID: fvEfKxkAAAAJ
University of Michigan
Citations & Impact
Citations
5,235
H-index
36
i10-index
70
Publications
20
Co-authors
46
list available
Contact
No contact links provided.
Publications
Resume (English only)
Academic Achievements
- Named Forbes 30 under 30 in Science and Healthcare in 2015; received an NSF CAREER Award in 2016; named to the MIT Tech Review's list of 35 Innovators Under 35 in 2017; received a Sloan Fellowship in Computer Science in 2020; received the Carl Friedrich von Siemens Humboldt Research Award in 2024; published papers in NeurIPS, JAMA Network Open, and other significant journals.
Research Experience
- Head of the MLD3: Machine Learning for Data-Driven Decisions research group; Principal Investigator on multiple projects including 'Human-AI Collaborations to Improve Accuracy and Mitigate Bias in Acute Dyspnea Diagnosis' funded by NIH-NHBLI, and 'Data-driven interventions for Reducing C. difficile Incidence' funded by AHRQ.
Education
- PhD from MIT in 2014, where she worked with Prof. John Guttag in the Computer Science and Artificial Intelligence Lab (CSAIL). Her PhD research focused on developing accurate patient risk-stratification approaches that leverage spatiotemporal patient data, with the ultimate goal of discovering information that can be used to reduce the incidence of healthcare-associated infections.
Background
- Associate Professor of Computer Science and Engineering (CSE) at the University of Michigan, Associate Director of the Artificial Intelligence (AI) Lab, and co-Director of AI & Digital Health Innovation. Her primary research interests lie at the intersection of machine learning (ML), artificial intelligence (AI), and healthcare. She takes a use-inspired approach to research, collaborating closely with domain experts and clinicians, integrating ML models developed by her research group into clinical workflows that have had a positive impact on patient care.
Miscellany
- September 2025: Moved the needle on C. diff and antimicrobial stewardship; January 2025: Delivered the Ingrid Daubechies Lecture at Duke University; June 2024: Visited Imperial College London's UKRI Centre for Doctoral Training in AI for Healthcare to deliver a seminar; January 2024: Received the Carl Friedrich von Siemens Research Award of the Alexander von Humboldt Foundation and visited Karsten Borgwardt's group at the Max Planck Institute of Biochemistry; September 2023: Spoke in the Biomedical Data Science seminar series at Stanford while on sabbatical; January 2023: Received the 2023 Sarah Goddard Power Award; December 2022: Presented two papers at NeurIPS 2022; September 2022: Presented work on using ML to guide infection prevention to the President's Council of Advisors in Science and Technology (PCAST) work group on patient safety; September 2021: R01 for 'Human-AI Collaborations to Improve Accuracy and Mitigate Bias in Acute Dyspnea Diagnosis' was funded by NIH-NHBLI; February 2020: R01 for 'Data-driven interventions for Reducing C. difficile Incidence' was funded by AHRQ.
