Interpretable ML for stress forecasting and management
Batuhan Koyuncu (Ph.D. Student)
Modeling temporal stress patterns of people is a challenging task since stress patterns are complex and have a heterogeneous nature. However, it is a crucial task for forecasting and mitigating stress. The objectives of this Ph.D. project are understanding stress causes, forecasting stress levels over time, and providing personal recommendations to reduce stress levels through probabilistic models. In our methodology, we will rely on building interpretable predictive and generative models for the temporal modeling of asynchronous multimodal data.
|Primary Host:||Isabel Valera (Saarland University & Max Planck Institute for Intelligent Systems)|
|Exchange Host:||Ole Winther (University of Copenhagen & Technical University of Denmark)|
|PhD Duration:||20 September 2021 - Ongoing|
|Exchange Duration:||- Ongoing - Ongoing|