Research
My research sits at the intersection of cosmology and machine learning. I work on extracting cosmological information from weak gravitational lensing data, going beyond standard two-point statistics by developing methods that use higher-order correlations of the cosmic shear field. A recurring theme in my work is making these analyses more powerful, robust, and interpretable.
On the methodology side, I develop simulation-based inference pipelines that replace analytic likelihoods with forward-modelled simulations. This approach naturally handles non-Gaussian information and complex systematic effects. To keep these pipelines physically transparent, I build neural network architectures whose learned features have a direct correspondence to N-point correlation functions.
I am a member of the Dark Energy Survey (DES), the Euclid Consortium, and the LSST Dark Energy Science Collaboration (DESC).
Research Areas
Weak Gravitational Lensing
Using the distortion of galaxy shapes by large-scale structure to constrain cosmological parameters, with a focus on higher-order shear statistics beyond the standard two-point functions.
Simulation-Based Inference
Developing likelihood-free inference pipelines that use forward-modelled simulations to constrain cosmology, bypassing the need for analytical likelihood expressions.
Interpretable Machine Learning
Building neural network architectures whose internal representations correspond to known physical quantities, such as N-point correlation functions, to keep machine learning analyses transparent and physically meaningful.
Current Projects
Higher-order weak lensing statistics
Developing and applying the integrated 3-point correlation function for weak lensing, including realistic treatment of systematic effects for Stage-III and Stage-IV surveys.
Physics-informed neural networks for cosmology
Extending the C3NN architecture to simulation-based inference, learning hierarchies of N-point statistics directly from cosmological fields while retaining interpretability.