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).