Machine Learning for Data-Driven Decisions

Machine Learning for Data-Driven Decisions (MLD3)

Welcome to the Machine Learning for Data-Driven Decisions Group at the University of Michigan!

Our group is part of the AI Lab within the Division of Computer Science and Engineering.

We work at the interface of artificial intelligence (AI), machine learning (ML), and healthcare.

Our current research portfolio focuses on major public health problems – including infectious disease, Alzheimer’s disease, and diabetes, among others. We develop and apply state-of-the-art AI and machine learning methods to analyze large longitudinal health datasets. Our work spans several aspects of AI including time-series analysis, reinforcement learning, computer vision, and causal inference. We aim to develop the computational methods needed to help organize, process, and transform data into actionable knowledge with the ultimate goal of improving health.

You can contact the group by emailing Dr. Jenna Wiens at

Latest News

Two papers from our lab are accepted at AAAI 2021! Congratulations to students Donna Tjandra (A Guided Approach to Multi-Event Survival Analysis) and Fahad Kamran (Estimating Calibrated Individualized Survival Curves with Deep Learning)!

Two papers from our lab are accepted at MLHC 2020! Congratulations to students Ian Fox (Deep RL for blood glucose control) and Sarah Jabbour (exploiting & preventing shortcuts in Deep Learning applied to Chest X-Rays)!

Our paper on clinician-in-the-loop reinforcement learning with near-optimal set-valued policies is accepted at ICML 2020! Congratulations to students Shengpu Tang and Aditya Modi!

Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts | MLHC 2020

Deep Reinforcement Learning for Closed-Loop Blood Glucose Control | MLHC 2020

Clinician-in-the-loop RL with Set-Valued policies | ICML 2020

Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction


Friday Night AI:
AI and COVID-19

Erkin Otles speaks about M-CURES, a machine learning model developed by people from our lab. M-CURES can help clinicians tell which COVID-19 patients are most likely to deteriorate.

How can machine learning impact healthcare?

Prof. Jenna Wiens uses machine learning to make sense of the immense amount of patient data generated by modern hospitals. This can help alleviate physician shortages, physician burnout, and the prevalence of medical errors.

Jenna Wiens, University of Michigan – Stanford Medicine Big Data | Precision Health 2018