Azizpour group’s machine learning research can help life science applications by making data-driven models more effective, reliable and explainable. The current directions include (i) “interpretable modeling” and “uncertainty estimation” to improve trustworthiness, (2) “under-supervised learning”, “feature selection”, and “knowledge transfer” to handle imperfect and/or low data, and (3) “generative modeling” for robust predictive modeling, design tasks, and understanding biological mechanisms.
Hossein Azizpour
KTH
Key Publications
Continuous Urban Change Detection From Satellite Image Time Series With Temporal Feature Refinement and Multitask Integration
IEEE Transactions on Geoscience and Remote Sensing, 2025
Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening
Radiology, 2024