Most of my past work has revolved around numerical methods for scientific computing, for which libraries and packages are very useful. For my first research projects I used julia, which gave me a taste for functional programming, and I was an avid user of QuantumOptics.jl for some time.
Then I started machine learning, and for this I first used Flux.jl, which is great but lacks a lot of support for modern tools.
I therefore went over to python, and used netket, a great jax-based tool developed by a F. Vicentini, a former labmate.
Below is the list of my open source contributions.
Open-source contributions
2024
- posteriors
S. Duffield, Me, and J. Chiu, P. Klett and D. Simpson
posteriors is the *go-to library* for uncertainty quantification of LLMs. It is functional, swappable, transformers-compatible and contains the basic important UQ methods, such as Laplace approximations, Monte-Carlo methods and variational inference. I also contributed an efficient conjugate gradient solver using fisher-vector products in pytorch, which doesn't seem to exist anywhere.
Check out the blog post
- thermox
S. Duffield & Me
thermox is the *best OU process simulator*, as it is exact, GPU-compatible and uses associative scans. This is used internally by us for simulation thermodynamic hardware, but can be useful for many other things (finance people contributions welcome!).
Check out the blog post