My research involves studying how large datasets from cosmic microwave background (CMB) experiments and galaxy surveys can constrain fundamental physics. I use numerical computation, data analysis and sophisticated statistical methods such as Bayesian statistics and Monte Carlo techniques.

**Extreme data compression and super-fast parameter estimation**

Using Karhunen-Loeve methods I have developed a data compression algorithm, which is able to estimate model parameters (marginalized likelihoods) in under a minute, much faster than Markov Chain Monte Carlo (MCMC) methods. The data compression automatically marginalizes over all other parameters. Instead of carrying out a full likelihood evaluation over the whole parameter space, we need to evaluate the likelihood only for the parameter of interest. We therefore achieve **extreme data compression** (EC) by i) compressing the entire dataset into just a few numbers, and ii) reducing the dimensionality of the parameter space that needs to be explored. You can find the paper here.

**Constraining neutrino masses with galaxy surveys**

As part of my PhD thesis, I studied the effects of massive neutrinos and different dark energy models on large-scale structure, in particular on the angular clustering of galaxies in bins of photometric redshift in the Dark Energy Survey (DES). I developed a **likelihood analysis** pipeline to test how well DES will be able to constrain the sum of the neutrino masses. I carried out a joint constraints analysis, combining mock data from DES and the Planck CMB experiment. You can find the paper here.

I am interested in:

- Neutrino effects on structure formation
- Lossless data compression algorithms
- Physics of the cosmic microwave background
- Nature of dark matter and dark energy
- Primordial Gravitational Waves as signatures of Inflation
- Cosmic Reionization in the 21cm