I am a computational evolutionary paleobiologist who is primarily interested in developing novel methodologies and tools in order to answer questions about macroevolutionary dynamics in deep time.

Currently, I am a researcher at Stockholm University in the Department of Ecology, Environment and Plant Sciences (DEEP). I am working on applying deep learning and computer vision towards understanding the evolution of pollen in response to evolutionary transitions between pollination modes. This work is part of the project “Harnessing evolutionary transitions, machine learning, and genomics to decode pollen”, on which I am a co-PI. This project is supported by a grant awarded by the Knut and Alice Wallenberg Foundation.

My research uses machine learning, high-throughput imaging, and statistical modelling to understand morphological evolution and macroevolutionary patterns and processes. I am particularly interested in bringing a next-generation approach (e.g., automated Big data collection) to studying morphological evolution.

I work with both vertebrate and invertebrate systems. Taxonomic groups I have worked with include turtles, squamate lizards, birds, mammals, planktonic foraminifera, and angiosperms (flowering plants).

By bridging the fields of computer science and evolutionary biology, I aim to deliver a unique and creative perspective towards understanding evolution and the history of life on earth.