Fellow Detail

Photo of Jesper Sören Dramsch
Name Jesper Sören Dramsch
Affiliation ECMWF
Department Forecast Department
Group Innovation Platform
Research area code (I4) Artificial intelligence
Fellowship Inauguration Year 2022
Institutional Website https://www.ecmwf.int/en/about/who-we-are/staff-profiles/jesper-dramsch
Website https://dramsch.net
RSS/Atom Feed http://dramsch.net/rss.xml
ORCID 0000-0001-8273-905X
Google Scholar https://scholar.google.co.uk/citations?user=2nrI28QAAAAJ
GitHub JesperDramsch/
Twitter jesperdramsch
LinkedIn mlds/

Reproducibility for Machine Learning in the Sciences, Physics, Programming, Gaming, Board games, Climbing, Content Creation

Short Biography

Jesper Dramsch works at the intersection of machine learning and physical, real-world data. Currently, they're working as a scientist for machine learning in numerical weather prediction at the coordinated organisation ECMWF.

Before, Jesper has worked on applied exploratory machine learning problems, e.g. satellites and Lidar imaging on trains, and defended a PhD in machine learning for geoscience. During the PhD, Jesper wrote multiple publications and often presented at workshops and conferences, eventually holding keynote presentations on the future of machine learning.

Moreover, they worked as consultant machine learning and Python educator in international companies and the UK government. Their courses on Skillshare have been watched over 25 days by over 2000 students. Additionally, they create educational notebooks on Kaggle, reaching rank 81 worldwide.

Previous events

Title Start date End date
Collaborations Workshop 2024 Tuesday, 30 April 2024 Sunday, 02 June 2024
CW23 Tuesday, 02 May 2023 Thursday, 04 May 2023
ML.Recipes a Jupyter Book to make ML reproducible in science ( Blog post ) Monday, 06 March 2023 Wednesday, 05 March 2025

Blog Posts

Blog Publish date
The siren call of AI alignment: How can scientists avoid harm while using Machine Learning? Thursday, 15 February 2024
The Hard Work of Building Inclusive Communities Thursday, 15 February 2024
Speedrunning a Workshop on Reproducibility for Machine Learning in Science Friday, 10 March 2023
How to increase citations, ease reviews and facilitate collaboration for ML in applied science Monday, 13 February 2023
https://docs.google.com/document/d/1vqiJ4F1v7PZs4XoBQns5BgF2cymKVCcZ7qn0vV5th4A/edit?usp=sharing Monday, 12 December 2022