Skip to content
View ArtemBasyrov's full-sized avatar

Highlights

  • Pro

Block or report ArtemBasyrov

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Content in all repositories owned by your account will be closed.
Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
ArtemBasyrov/README.md

I'm a postdoctoral researcher in computational cosmology at the AstroParticule & Cosmologie Laboratory (APC, CNRS/IN2P3) in Paris, working on instrumental systematics, data analysis, map-making, and fast spherical-harmonic methods for the next generation of Cosmic Microwave Background experiments.


Research Focus

  • Beam systematics — how an imperfect optical response leaks into the maps, and how to model it before it becomes a bias
  • Map-making — turning time-ordered detector data into polarised sky maps, and keeping the operators linear enough to invert
  • Spherical harmonics algorithms — fast, accurate transforms on HEALPix and other grids on the sphere
  • Accelerated pipelines — JAX and GPU implementations of the above, differentiable where it helps
  • Local LLMs — running open-weight models on my own hardware and building the tooling around them: tool-calling agent loops, quantised inference with llama.cpp and MLX, speculative decoding, and semantic memory over vector search
  • Occasionally other stuff — games in Godot and Pygames, news collections and analysis, predictive financial tools

Main Projects

Project What it does
FURAX JAX-based framework for CMB component separation and map-making
tod_generation_mapbased_beam Sample-based TOD generation: convolves polarised I/Q/U maps with a pixelated beam along the scan (docs)
HP2SPH_python Fast, accurate HEALPix ↔ alm transforms via a double Fourier sphere, NUFFT and Slevinsky's FSHT — scalar and spin-2
news_machine Daily news briefing pipeline running fully on a local LLM — fetch, categorise, summarise, with rolling weekly/monthly digests as memory
LLM_tools Tool-calling agent harness for locally hosted LLMs — file edits, code intelligence, git, search, memory

Contributor

Project What it does
Commander Bayesian end-to-end CMB analysis by Gibbs sampling
s2fft Differentiable, accelerated spherical transforms
jax-healpy JAX implementation and extension of healpy

Tech Stack

Python JAX NumPy Numba C++ Fortran Julia LaTeX Git

Specialties: CMB Analysis | Beam Systematics | Map-Making | Spherical Harmonic Transforms | Automatic Differentiation | HPC | Local LLM Tooling

If the problem demands it, I'll learn the language.


📍 Paris, France  |  🌌 CMB

Pinned Loading

  1. tod_generation_mapbased_beam tod_generation_mapbased_beam Public

    Python 1

  2. HP2SPH_python HP2SPH_python Public

    Python 1

  3. LLM_tools LLM_tools Public

    Python

  4. news_machine news_machine Public

    Python

  5. CMBSciPol/furax CMBSciPol/furax Public

    Framework for Unified and Robust data Analysis with JAX

    Python 10 4

  6. Cosmoglobe/Commander Cosmoglobe/Commander Public

    Commander is an Optimal Monte-carlo Markov chAiN Driven EstimatoR which implements fast and efficient end-to-end CMB posterior exploration through Gibbs sampling.

    Fortran 26 17