Projects

ML for Cosmology & Physics

SaUCE
High-performance Rust-backed library for fast, unbiased galaxy clustering and correlation function estimation from sparse merger trees
RustPythonClustering
ANVIL
Simulation-based inference (SBI) framework and diagnostic suites for neural posterior validation
PythonPyTorchSBI
KARMA
Field-level simulation-based inference framework for cosmological observations
PythonPyTorchSBI
DiffCMB
Differentiable full-sky CMB inference and joint Gibbs sampling framework
PythonJAXMCMC
HOD
Halo Occupation Distribution modeling and galaxy clustering analysis tools
PythonCosmology
Dynamical Friction
Modeling merger timescales and satellite orbital dynamics in semi-analytic galaxy formation models
PythonFortranCosmology
DESI Analysis
Physical modeling of target selection and emission line galaxy occupations for DESI surveys
PythonDESICosmology
Screening
Modified gravity and fifth-force screening mechanism modeling with simulated galaxy mocks
PythonCosmology
Assembly Bias
Investigating physical drivers and environmental properties of galaxy assembly bias
PythonAstrophysics
IMF Calibration
Parameter calibration and modeling of stellar initial mass functions in galaxy evolution
PythonBayesian Inference
GALFORM Clustering Calibration
Direct simulation-based inference calibration pipelines for semi-analytic models
PythonSBISLURM
GALFORM Analysis
High-performance Python analysis tools for GALFORM semi-analytic galaxy formation models
PythonHDF5Polars
GALFORM Execution
HPC execution and parameter sweep orchestration for GALFORM simulations
PythonSLURMHPC
CMB Cosmology with Advanced Sampling
Accurate CMB power spectrum sampling using TensorFlow Probability and advanced MCMC techniques (HMC & NUTS). Masters project.
PythonRustTensorFlow Probability
Photon BEC Phase Characterisation
Machine learning methods for characterizing photon Bose-Einstein condensate phase diagrams. BSc project.
Python

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