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Research:
My research focuses in the the statistical and computational challenges arising in the analysis of large scale structure data in cosmology. Cosmology is currently experiencing an unprecedented rise in the quantity and quality of data. The Dark Energy Spectroscopic Instrument (DESI) has already recorded more galaxy spectra in its first two years of operations than previously done in the whole history of humanity. Moreover, next-generation surveys such as Legacy Survey of Space and Time (LSST), Euclid, the Nancy Grace Roman space telescope, the Simons Observatory promise to further accelerate this trend in the next decade. In order to match the quality of the data, physicists are starting to incorporate into their theoretical predictions more of the physical, observational and instrumental effects which, until now, could be overlooked. In practice this translates into a dramatic increase in the number of parameters that future analyses will have to consider. This combination of large data sets with complex models will overwhelm current inference methods, greatly limiting the scope of future cosmological analyses to the point of defeating the purpose of the newly commissioned facilities.
My work tackles the challenges posed by the up-coming generation of data in two distinctive ways. I am interested in increasing the scope of cosmological models as well as improving models of systematic effects to resolve the current inconsistencies between different cosmological analyses. I also aim to accelerate the inference methods by which models are constrained given the greatest and latest data sets. In order to do so I make extensive use of machine learning methods and new computational methods.
Open Source Codes: