why

cosmology?

Färgglada 3D-grafik av matematiska och fysikaliska ekvationer samt geometriska former på en mörk bakgrund.

En pixelartbild med många små fyrkantiga block i färgerna lila, gult och rött, på en mörk bakgrund.

Diagram med fyra nivåer som visar temperaturförändringar i ett hav, från blå till röd, med olika filtreringar av data.

En nattlig himmel full av tusentals stjärnor och delar av Vintergatans galax.

En futuristisk svart hål i rymden med en ljusvit ljusring och en mörk skugga i mitten, omgiven av stjärnor och mörka gasmoln i rymden.

I am driven to explore the early epochs and evolution of our universe to better explain why what we observe looks the way it does. I am interested in understanding the physical processes that shape our Universe and how they are encoded in the signals we observe.

In particular, I work with simulations and computational methods to connect models of cosmic evolution with current and future astronomical observations.

Research Interests

Bild av många avlägsna galaxer och stjärnor i rymden med en centralt belägen stark ljuskälla.

EARLY STRUCTURE FORMATION

The early Universe evolved from an almost uniform distribution of matter into the complex structures we observe today.

But how did the first stars and galaxies emerge within this growing cosmic web and shape the Universe around them?

Pixelbild med mörk bakgrund och små ljusfläckar i gult och vitt.

Cosmological Signals

We can trace the first stars and galaxies through the radiation they emitted and their imprint on the matter around them. Signals such as emission lines and the 21 cm line allow us to map otherwise unseen structure, revealing the filaments and nodes of the evolving cosmic web.

But how are physical processes encoded in signals such as the 21 cm line and line-intensity maps, and what can these signals tell us about cosmic history?

Färgglad digital konst som visar ett nät av ljusstrålar i lila och blå nyanser, som liknar ett stjärnsystem eller kosmiskt nät.

Computational Cosmology & AI

Modern simulations allow us to move beyond equations alone and model how complex cosmic systems evolve over billions of years. Combined with statistical inference and rapidly developing machine-learning methods, these models offer new ways of connecting theoretical physics with increasingly complex astronomical observations.

I therefore develop and investigate how simulations, statistical inference, and machine learning can help us extract physical information from increasingly complex astronomical datasets.