My research focuses on understanding the formation and evolution of galaxies, particularly galaxies like Milky Way and their lower-mass companions. My research group uses a variety of medium- to large-scale astronomical surveys, primarily photometric, from ground- and space-based observatories for my work. Data science is central to my group’s work, using employ statistical techniques and machine learning algorithms to extract physically-important insights from observational datasets. My group develops and applies custom pipelines for data cleaning, feature extraction, and classification, typically in Python. We integrate simulation results with observational datasets, applying methods such as Bayesian inference and likelihood-free inference to model stellar populations and extract insights into galaxy merger histories from the structure of stellar halos. This multi-pronged, data-driven approach enables us to address key questions about galactic archaeology and the cosmological context of galaxy formation.
How did you end up where you are today? (Your research journey)
I’m the first in my extended family to go to college, coming from (in great part) a village in the rural North of Scotland. I was blessed along the way with supportive mentors, and structural support (at that time) from the UK’s higher education system, attending the University of Glasgow and getting my PhD in Physics at Durham University in North East England. After a postdoc at the University of Arizona, I joined the scientific staff at the Max Planck Institute for Astronomy in Heidelberg, Germany. I joined the faculty at the University of Michigan Ann Arbor in 2009, and haven’t looked back since!
What is the most significant scientific contribution you would like to make?
One thing I’d really love to be able to do over the next decade is to build a coherent observational framework linking the properties of galaxies – their star formation histories, the motions of their stars and gas, their structure – to the dark matter halo mass, satellite population and merger histories of those galaxy groups, for a representative set of galaxies that are the largest ones in their dark matter haloes (‘central’ galaxies). There’s a lot of work to do on the observational side – resolved star color-magnitude diagrams and maps, kinematics of stars and gas – and on the inference side – connecting with sophisticated simulation suites to infer star formation histories, dark matter halo masses, and merger histories for example. These insights are critical to hold our galaxy formation framework and simulations to account to accurately reproduce the look-up table between dark matter halo masses and merger histories (the ‘initial conditions’) and the emergent properties of galaxies that we see today.