My goal is to control and adapt to open-ended evolution in biological and social systems. By studying the empirical dynamics of cancer and other natural systems, I uncover the algorithms of evolution, understand the computational complexity of the natural world, and use that knowledge to develop novel strategies for treating cancer.

Currently, I am a James S. McDonnell Foundation independent postdoctoral fellow in dynamic and multi-scale systems hosted by the University of Pennsylvania Department of Biology.

In 2020, I received my DPhil in Computer Science from the University of Oxford with the support of the Cleveland Clinic Department of Translational Hematology and Oncology Research. While at the University of Oxford, I taught computer science at Oriel College, first as a Graduate Teaching and Research Scholar and then as College Lecturer.

Before my time in the UK, I was at the Moffitt Cancer Center Department of Integrated Mathematical Oncology and McGill University.


Any process can be framed as an algorithm; its power and its limits can then be analysed with the techniques of theoretical computer science. This “algorithmic lens” can be used to view both artificial and natural processes. When viewing natural processes of biological evolution or ecology, we get algorithmic biology.

My research combines theoretical computer science and evolutionary biology: I use the tools and techniques of computer science to ask and answer questions in evolutionary biology. I aim to better understand evolution using game theory, computational complexity, combinatorial optimization, and computational learning theory; and to link this new understanding to empirical measurements of evolutionary dynamics in cancer and other biological and social systems.