Jesper Dramsch
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.
@JesperDramsch
Institute / Company –ECMWF
Sessions
Every scientific conference has seen a massive uptick in applications that use some type of machine learning. Whether it’s a linear regression using scikit-learn, a transformer from Hugging Face, or a custom convolutional neural network in Jax, the breadth of applications is as vast as the quality of contributions.
This tutorial aims to provide easy ways to increase the quality of scientific contributions that use machine learning methods. The reproducible aspect will make it easy for fellow researchers to use and iterate on a publication, increasing citations of published work. The use of appropriate validation techniques and increase in code quality accelerates the review process during publication and avoids possible rejection due to deficiencies in the methodology. Making models, code and possibly data available increases the visibility of work and enables easier collaboration on future work.
This work to make machine learning applications reproducible has an outsized impact compared to the limited additional work that is required using existing Python libraries.
Every scientific conference has seen a massive uptick in applications that use some type of machine learning. Whether it’s a linear regression using scikit-learn, a transformer from Hugging Face, or a custom convolutional neural network in Jax, the breadth of applications is as vast as the quality of contributions.
This tutorial aims to provide easy ways to increase the quality of scientific contributions that use machine learning methods. The reproducible aspect will make it easy for fellow researchers to use and iterate on a publication, increasing citations of published work. The use of appropriate validation techniques and increase in code quality accelerates the review process during publication and avoids possible rejection due to deficiencies in the methodology. Making models, code and possibly data available increases the visibility of work and enables easier collaboration on future work.
This work to make machine learning applications reproducible has an outsized impact compared to the limited additional work that is required using existing Python libraries.