Alumni

Theoretical approaches have always played an important role in biology, dating back to Mendel’s peas. In today’s era of genomics and big data in biology, statistical and computational tools are even more vital for biologists seeking to infer causation in living systems. To illustrate the range of methods, from modelling to machine learning, and how they contribute to understanding biological mechanisms, Dr. Teichmann will pick examples from some of the core problems her lab has been investigating as case studies.

Understanding the sorts of explanations and inferences that causal processes countenance is of course of great interest to philosophers and physicists (among others). But what can be said about physical processes that fail to exhibit classical causal structure? Indefinite causal ordering among events made possible by quantum correlations has become a fruitful arena of study recently, yielding new insights for quantum computing and communication, approaches to quantum gravity, and even for foundational issues in quantum mechanics.