-

ASDF, the Advanced Scientific Data Format

-

Tensor Trains, mostly as probability distributions

Deep Unfolded Convolutional Dictionary Learning for motif discovery.

GPU-accelerated algorithms for flux sampling in CUDA.jl

-

-

-

FFT solvers in Julia language

-

Viral haplotype calling by linkage disequilibrium

-

Block respecting compression of MPOs and iMPOs

Deep-learning sliding window attention with circular boundary conditions. No tokenization, no patchify-ing.

-

ParticleHolography.jl: Holographic particle measurement in Julia.

Maximum Likelihood Gaussian Blob Fitter

Using a population of profiles to approximate the real motifs

Convenience wrappers to incomplete factorizations from CUSPARSE to be used for iterative solvers of sparse linear systems on the GPU

-

Fast, accurate, and flexible spectral analysis for compressible quantum fluids

-

Python extensions relating to Caesar.jl

-

-

-

-

-

Type for fast updating of correlation functions

Unfolded convolutional learning for motif discovery

-

-

-

Simulating quantum computing verification

-

Finite Differencing Method in Julia language

Various closures models (closure -> seal -> seal 🦭 -> walrus) for ocean flavoured fluid dynamics

-

-