My research is focused on developing new applications of optimization and machine learning in the field of radiation oncology. In particular, I am interested in methods for handling nonconvex dose-volume constraints in intensity-modulated radiation therapy, hyperparameter selection in treatment planning inverse problems, and feature selection for models to predict patient outcomes and side effects. I am advised by Aleksandr Aravkin and Minsun Kim.
- Maass, K., & Kim, M. (2019). A Markov decision process approach to optimizing cancer therapy using multiple modalities. Mathematical Medicine and Biology: A Journal of the IMA. doi:10.1093/imammb/dqz004.
Maass, K., Kim, M., & Aravkin, A. (2019) A nonconvex optimization approach to IMRT planning with dose-volume constraints. arXiv preprint arXiv:1907.10712.
Adviser: Aleksandr Aravkin
- PhD student Kelsey Maass wins UW poster competition - April 9, 2018
- Departmental Diversity Committee Addresses Inclusivity and Representation - August 28, 2017