There are two plenary speakers in the fifth meeting:

  • Richard Durrett — Duke University
  • Bin Yu — University of California, Berkeley

The title and abstract of Professor Durrett's plenary lecture are:

Title: Spatial Evolutionary Games

Abstract: Recently, a rigorous mathematical theory has been developed for spatial games with weak selection, i.e., when the payoff differences between strategies are small. The key to the analysis is that when space and time are suitably rescaled, the spatial model converges to the solution of a partial differential equation (PDE). This approach can be used to analyze all 2 x 2 games, but there are a number of 3 x 3 games for which the behavior of the limiting PDE, a system of reaction-diffusion equations is not known. In this talk, we give rules for determining the behavior of a large class of 3 x 3 games and show their validity using simulation. In words, the effect of space is equivalent to making changes in the payoff matrix, and once this is done, the behavior of the spatial game can be predicted from the behavior of the replicator equation for the modified game.

The title and abstract of Professor Yu's plenary lecture are:

Title: Three Principles of Data Science: Predictability, Stability, and Computability

Abstract: In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title. They will be demonstrated in the context of two collaborative projects in neuroscience and genomics, respectively. The first project in neuroscience uses transfer learning to integrate fitted convolutional neural networks (CNNs) on ImageNet with regression methods to provide predictive and stable characterizations of neurons from the challenging primary visual cortex V4. The second project proposes iterative random forests (iRF) as a stablized RF to seek predictable and interpretable high-order interactions between biomolecules.