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Hi there 👋

  • 🔭 Working on pushing the limits of machine learning for time series modeling. I'm specifically working on efficient automatic procedures that work on massive numbers of heterogeneous time series.
  • 🌱 I’m currently learning psychology, botanics and advanced economics (among other stuff)
  • 👯 I’m looking to collaborate on fun niche interesting products and research
  • 💬 Ask me about anything related to tech/data or living things !
  • 📫 How to reach me: LinkedIn message or ztd6dmlva [at] relay.firefox.com
  • I write here (Notion thoughts journal), here (personal blog) and sometime here (Medium) !

My tech stack:

I usually just learn whatever tool I need to use, as I consider myself a polymath. Still, I've been consistently using these for years now:

  • Python, R, bash
  • SQL, dbt, Airflow
  • Linux, Ansible, Terraform, Docker
  • Google Cloud Platform services (Cloud Run, BigQuery, etc.)
  • A bunch of libraries related to data processing, machine learning, forecasting, etc.

Anas El Khaloui's Projects

amundsen icon amundsen

Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data.

bitcoin icon bitcoin

Bitcoin Core integration/staging tree

datasets icon datasets

CSV datasets used in Plotly API examples

docs icon docs

random docs for sharing

first-order-model icon first-order-model

This repository contains the source code for the paper First Order Motion Model for Image Animation

gekko icon gekko

A bitcoin trading bot written in node - https://gekko.wizb.it/

heroku_ebooks icon heroku_ebooks

A Heroku worker script to post to an _ebooks version of your Twitter account

logistic icon logistic

Experimental logistic regression code supporting multiple result categories, many levels of categorical modeling variables, good optimization, L2 regularization and more.

poincaremaps icon poincaremaps

The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry which is not an optimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method harnessing the power of hyperbolic geometry into the realm of single-cell data analysis.

r icon r

ML is easy :)

ta icon ta

Technical Analysis Library using Pandas and Numpy

vqgan-clip icon vqgan-clip

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

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