Michigan AI Laboratory
Prospective graduate students
This collaborative environment, coupled with our diverse perspectives, leads to a valuable interchange of ideas within and across research groups.
Widely used AI tool for early sepsis detection may be cribbing doctors’ suspicions
When using only data collected before patients with sepsis received treatments or medical tests, the model’s accuracy was no better than a coin toss.
Hearing emotion: Redefining mental health monitoring via voice-based mood detection
Researchers at U-M have received a $3.6 million NIH grant to support their development of new digital phenotyping tools to better detect and measure symptoms of bipolar disorder via audio…
2024 EECS Outstanding Achievement Awards
The EECS Department has honored four faculty for their sustained excellence in instruction and curricular development, distinguished participation in service activities, or for their significant…
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.
Making AI explainable
Researchers in Prof. Nikola Banovic’s lab work to make AI models understandable to the people who ultimately have to use them – clinicians, policymakers, engineers, artists, designers, and the broader public. Most models rely on complex mathematical outputs to communicate things like how they arrived at a certain answer, or how “confident” the model is in its output. Instead, Banovic and his students design interactive tools to connect the dots for end-users.