Adam Hulman, MSc, PhD

Associate Professor & Senior Data Scientist
+45 2370 7481

Research team

Research areas

  • Clinical prediction
  • Machine learning and its clinical applications
  • Deep learning
  • Synthetic data
  • Longitudinal studies
  • Latent class analysis

Research description

  • My research focuses on how longitudinal multimodal data (tabular, time series, images) can be incorporated into clinical risk prediction models. To achieve this, we study state-of-the-art deep learning methods and, using transfer learning, repurpose them for clinical and epidemiological research.
  • Supported by the NNF Data Science Emerging Investigator Programme (grant period: 2023-2028).

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