PhD student perspectives in CSE: Sarah Jabbour
Sarah Jabbour • 2023
Jenna Wiens receives U-M Sarah Goddard Power Award for outstanding research and advocacy for women in academia
Congratulations to Jenna Wiens for receiving the Sarah Goddard Power Award!
Diversity and inclusiveness are an essential part of the pursuit of AI at CSE
We have been featured in an article on diversity and inclusion from the Michigan AI Lab!
“It’s a supportive and collaborative environment” — making connections as a PhD student in and outside the classroom
Sarah Jabbour shares the story of her journey into computer science and graduate school!
2022 CSE Graduate Student Honors Competition highlights outstanding research
Congratulations to Shengpu Tang for winning second place at the University of Michigan CSE Honors Competition!
Decisive Differences in Healthcare AI
Trenton Chang discusses his research in an interview with Rackham!
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Trenton Chang • MLHC 2022
Reinforcement Learning with Set-Valued Policies • PathCheck Global Health Innovators Seminar
Shengpu Tang • August 12, 2021
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings | MLHC 2021
Shengpu Tang • MLHC 2021
Mind the Performance Gap: Examining Dataset Shift During Prospective Validation | MLHC 2021
Erkin Otles and Jeeheh Oh • MLHC 2021
From Diagnosis to Treatment – Augmenting Clinical Decision Making with Artificial Intelligence • Temerty Speaker Series
Dr. Jenna Wiens • May 11, 2021
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts
Sarah Jabbour • MLHC 2020
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control
Ian Fox • MLHC 2020
Clinician-in-the-loop RL with Set-Valued Policies | ICML 2020
Shengpu Tang | More details on icml.cc
Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction
Presented at 2020 KDH Blood Glucose Level Prediction Challenge
MCURES | CRA Virtual Conference 2020
a COVID predictive model developed specifically for the Michigan Medicine population: the Michigan COVID-19 Utilization and Risk Evaluation System, or MCURES.
Friday Night AI: AI and COVID-19 | 2020
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.
Stanford Medicine Big Data | Precision Health 2018
Jenna Wiens, University of Michigan
How can machine-learning impact healthcare? | 2018 UM-CSE research highlight
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.