The Biological Nonlinear Dynamics Data Science Unit investigates complex systems explicitly taking into account the role of time. Instead of averaging occurrences using statistics, we treat observations as frames of a movie. If patterns reoccur we can use behaviors in the past to predict their future.
In most cases the systems we study are part of complex networks of interactions and cover multiple scales. These include but are not limited to:
- systems neuroscience
- gene expression
- posttranscriptional regulatory processes
- ecology
- societal and economic systems
- systems with complex interdependencies.
The processes we are most interested in are those where the data has a particular geometry known as low dimensional manifolds. These are geometrical objects generated from embeddings of data that allow us to:
- predict their future behaviors
- investigate causal relationships
- find if a system is becoming unstable
- find early warning signs of critical transitions or catastrophes
- and more.