Four pillars:
Goal: model complex phenomena over time
Problem:
Question 1: How do we combine inaccurate physical models with machine learning?
Gaussian process: a probabilistic model for functions.
Formally known as a stochastic process.
Multivariate Gaussian is normally defined as a mean vector, $\boldsymbol{\mu}$, and a covariance matrix, $\mathbf{C}$.
This model inter-relates different functions with mechanistic understanding.
What if you need to inter-relate across different modalities of data at different scales.
E.g. biopsy images + genetic test + mammogram for breast cancer diagnostics.
Four pillars:
Goal: model complex phenomena over time
Problem:
Question 2: How do we formulate the right representations to integrate different data modalities?
Complex systems:
Solutions: