Computational Foundations

Our team will develop fundamental and generalizable computational advances.

Key objectives:

  • Develop a computational theory of local-to-global dynamics over multi-scale multi-layer (MSML) networks.
  • Develop computational foundations for forecasting, control, and optimization problems in epidemiology.
  • Discover fundamental limits to forecasting and inference.
  • Develop new statistical and machine learning techniques for ensemble modeling, spatial detection of weak signals, and change detection.
  • Explore the use of crowdsourced and active learning methods for inferring individual and community level awareness and behavioral changes.
  • Characterize the joint effects of network structure and local interactions on spreading processes.
  • Develop HPC-enabled rigorous solutions for scalable validation, calibration, sensitivity analysis, and uncertainty quantification for spreading processes on MSML networks.

Expected outcomes and computational advances:

  • Foundations of computational network science, especially with respect to:
    • an algorithmic local-to-global theory that seeks to understand the composed dynamics as a function of network structure and local dynamics, and
    • algorithmic foundations of spreading processes over MSML networks.
  • Rigorous algorithms and practical scalable methods for inferring properties of MSML networks.
  • New theory-guided deep learning methods that work on MSML networks and operate on sparse observational data.
  • New methods for transfer learning that can be used to develop innovative interventions based on a particular disease in a specific region to another disease in a related region.