Jenna Wiens
Professor, EECS – Computer Science and Engineering; Co-Director, AI & Digital Health Innovation; Associate Director, Artificial Intelligence Lab
Studies machine learning for data-driven clinical decisions, including risk prediction from electronic health records.
Jenna Wiens's lab at Michigan works in Machine Learning. 32 papers since 2023, 10 at top venues, mostly AISTATS and JAMIA. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 31%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 5 papers2025 · T1 4
- 15 papers2024 · T1 4
- 12 papers2023 · T1 2
T1 venuesOtherVenues: AISTATS, JAMIA, NeurIPS, AAAI, ECCV
Research focuspaper titles that use each topic's words
- risk2 papers
- decisions1 paper
- electronic health0 papers
- health records0 papers
- records0 papers
Paper list
From the lab homepage.
20255 papers
- JAMIA (T1)AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation
- Journal of the American Medical Association (JAMA) Network OpenGuiding Clostridioides difficile Infection Prevention Efforts in a Hospital Setting With AI
- AISTATS (T1)Leaning Laplacian Positional Encodings for Heterophilous Graphs
- JAMIA (T1)Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis
- AISTATS (T1)Understanding GNNs and Homophily in Dynamic Node Classification
202415 papers
- ECCV (T1)DEPICT: Diffusion-Enabled Permutation Importance for Image Classification Tasks
- NEJM AIEvaluation of Sepsis Prediction Models before Onset of Treatment
- ICML (T1)From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions
- Computers in Biology and MedicineLearning control-ready forecaster for Blood Glucose Management
- AISTATS (T1)Learning to Rank Optimal Treatment Allocation Under Resource Constraints
- NeurIPS Safe Generative AI WorkshopsMeasuring Steerability in Large Language Models
- CHILMultiple Instance Learning with Absolute Position Information
- Nature MedicineOff-label use of AI models in Healthcare
- Alzheimer’s & Dementia: Diagnosis, Assessment & Disease MonitoringPredicting 5-year dementia conversion in veterans with mild cognitive impairment
- PLOS Global Public HealthRacial differences in laboratory testing as a potential mechanism for bias in AI: a matched cohort analysis in emergency department visits
- IEEE Transactions on Biomedical EngineeringShortcomings in the Evaluation of Blood Glucose Forecasting
- Circulation: Cardiovascular Quality and OutcomesSummertime for Cardiovascular AI
202312 papers
- Infection Control & Hospital EpidemiologyClostridioides difficile infection surveillance in intensive care units and oncology wards using machine learning
- NeurIPS (T1)Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation
- CHILDenoising Autoencoders for Learning from Noisy Patient-Reported Data
- AAAI (T1)Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose
- CHILLeveraging an Alignment Set in Tackling Instance-Dependent Label Noise
- Antimicrobial Stewardship & Healthcare EpidemiologyLeveraging electronic data to expand infection detection beyond traditional settings and definitions
- Presented at Workshop on Counterfactuals in Minds and Machines at ICML and FrontiersLCD Workshop at ICMLLeveraging Factored Action Spaces for Off-Policy Evaluation
- MSphereMachine learning classification by fitting amplicon sequences to existing OTUs
- JAMAMeasuring the impact of AI in the diagnosis of hospitalized patients: a randomized clinical vignette survey study
- Infection Control & Hospital EpidemiologyProspective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers
- Antimicrobial Stewardship & Healthcare EpidemiologyThe future of automated infection detection: innovation to transform practice
- Machine Learning for Healthcare ConferenceUpdating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance
Where alumni went
Alumni are not listed on the lab homepage.
Apply to MichiganFall 2027
Michigan CSE Computer Science PhD
- Round
- Single round
- Opens
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- GRE
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- English
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- Fee
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Not yet confirmed for this intake.
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