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 both more powerful and more 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.
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 shear 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.