This workshop explores research in methods aimed at connecting mathematical models of cancer evolution and clinical data. It focuses on ecology and evolution of cancer. Traditionally, ecology and evolution have been the most "mathematical" (and perhaps the first mathematical) areas of biology. Mathematical contributions to these fields made in the 20th century are now fundamental parts of scientific knowledge. More recently, methods of ecology and evolution have found their way into oncology, bridging traditional molecular biology and mathematics including stochastic processes, dynamical systems, and nonlinear partial differential equations. New biological challenges lead to difficult mathematical problems and beautiful solutions, which are both innovative mathematically and impactful from the point of view of public health. These will be the focus of the proposed workshop.

The workshop will combine several distinct (but interconnected) topics:

1. Evolutionary parameters: determining evolutionary parameters from genomic data, reconstructing evolutionary histories from tumor ’omics data, from genes to function.
2. Laws of evolution and cancer: spatial evolution, evolution in hierarchical populations, cooperation and predation, selection, resistance.
3. Cellular plasticity in cancer and associated estimation/inference problems, epigenetics. 
4. Combining machine learning and modeling approaches to forecasting disease progression.

This workshop focuses on cutting-edge topics in the mathematical oncology community. The proposed topics span a wide range from evolutionary modeling, to data-driven approaches, including machine learning, parameter estimation and prediction. By bringing together these communities, we hope to foster collaborations that integrate the insights of mechanistic modeling with data-driven approaches, to address current problems in oncology.