Astronomy Research

The underlying theme of my research is to understand galaxy evolution through cosmic time, with a particular focus on chemical enrichment of the interstellar and intergalactic media. The work is built on three pillars which span the main tools we use to study galaxy evolution: observations, simulations and machine learning.

My research has been funded by NSERC Discovery grants ($821,695, from 2007-2023), a $120,000 NSERC Discovery Accelerator grant (2007-2010) and half a million dollars in infrastructure from CFI (2003-2012).

A list of past and current students and postdocs in my group and a full list of publications is available..

Observational studies

My group has made extensive use of large observational datasets to tackle a broad range of questions in the field of galaxy evolution. We are perhaps most well known for our work on galaxy mergers, where we have used the SDSS to extensively characterize the effect of interactions at low redshift with superlative statistics. This work has been complemented by both public and PI multi-wavelength observations spanning the X-ray to the radio, in order to study AGN, triggered star formation, gas depletion (or lack thereof!) and chemistry.

Most recently, my group has been leading research investigating the spatial variations of galaxy properties using large IFU surveys, such as MaNGA. This work has focused both on studies of quenching and star formation in normal galaxies and mergers. Observations of molecular gas are critical to understanding these processes and I am co-PI of the ALMaQUEST survey, which combines MaNGA and ALMA observations to study the interplay of gas and star formation, in normal galaxies, starbursts and to investigate quenching.

Finally, I am actively involved in the CFIS and UNIONS imaging survey. In particular, we are exploiting the deep imaging capabilities to identify galaxy mergers, in order to study their star formation and AGN activity.

Simulation studies

To complement the observational work in my group, I maintain close ties with theoretical colleagues, and support galaxy simulation research in my group. Again, this work has had a significant focus on galaxy mergers, using a range of cosmological, zoom-in and binary galaxy simulations. Previous work includes the study of the impact of mergers on the circum-galactic medium, the use of mid-IR colours in identifying merger triggered AGN and tracing the spatial dependence of triggered star formation in mergers.

Most recently, we have been utilizing large cosmological simulations to study the statistical effect of merger on star formation in the pre and post merger era, as well as its impact on quenching.

Machine learning

The availability of massive public galaxy surveys, such as the SDSS, has revolutionized the power of archival research in extra-galacic astronomy. Multi-wavelength complements, over large fractions of the sky, leverage incredible diagnostic power, at energies ranging from the UV (e.g. GALEX) to the mid-IR (e.g. WISE) all the way to the radio (e.g. ALFALFA). This revolution is set to continue in the coming decades with even more massive datasets from projects such as Euclid and LSST. My group has been at the leading edge of using machine learning techniques in astronomy, publishing on topics that range from predictions of gas content and star formation rates, to analysing the likely physical mechanisms that quench and regulate star formation.

In recognition of the enormous scope of machine learning applications in extra-galactic astronomy, I lead the GalNet collaboration, one branch of UVic's ARCNet research centre.

Past and present students and postdocs at UVic

A variety of projects are available for undergraduate and prospective graduate students to work on, depending on available funding.

Media coverage