Research

For a full list of publications, please see the Google Scholar profile. Some major areas of research in our lab:

• Quantifying bias in adaptively-collected data (Xinkun).
• Understanding the effect of memory in reinforcement learning (Ruishan).
• Understanding the geometry of deep learning and adversarial learning (Amirata).
• Developing tools to perform contrastive learning for biomedical applications (Abubakar).
• Algorithms and models for spatial transcriptomics (Ruishan).
• UK Biobank: disease risk prediction using large-scale genotype, behavioral and environmental data.
• Robustness of interpretations of black-box models (Amirata and Abubakar).