Jesper Dramsch

Scientist

Forecast, Innovation Platform

Summary:

Jesper Dramsch implements state-of-the-art machine learning solutions for numerical weather prediction.

They are currently contributing to the core development of AIFS, the fully data-driven NWP model at ECMWF.

Current Contributions

Key Responsibilities

Key Achievements

Previous Contributions

Previously, Jesper was approaching different topics, e.g. post-processing, in the 10 years ML roadmap at ECMWF.

Professional interests:

Validation of machine learning models in real-world contexts
Machine learning in science and reproducibility
Machine learning for weather and climate predictions
Research software engineering & sustainable software
Outreach, education & communication of research
Testing implicit assumptions of modeling choices

Career background:

Work Experience

2021 - present: Scientist for Machine Learning, ECMWF
2020 - 2021: Machine Learning Engineer, GMV NSL
2019 - 2020: Postdoc, Technical University of Denmark
2019 - 2020: Machine Learning and Python educator, Agile*
2018 - 2019: Visiting Scholar, Heriot-Watt University Edinburgh
2016 - 2019: PhD, Technical University of Denmark
2016 - 2016: Research Assistant, Technical University of Denmark
2016 - 2016: Research Assistant, GfZ Potsdam
2014 - 2015: Geotechnical Student Assistant, O+P Geotechnik
2013 - 2014: Lab Assistant, DESY
2012 - 2012: Geophysics Intern, Schlumberger
2011 - 2011: Geophysics Intern, Fugro FSI
2010 - 2012: Student Research Assistant, University of Hamburg
2007 - 2007: Intern Depth Imaging, GfZ Potsdam

Teaching Experience

2023: Guest lecturer ML for NWP, Brown University
2023: Co-organizer and lecturer ML for Weather and Climate Prediction, ECMWF MOOC
2022 - today: Lecturer and organizer for ML training courses, ECMWF
2021: Guest lecturer ML for geoscience, University of Hamburg
2020: e-lecture on ML in geoscience, EAGE
2020 - today: Online courses about ML, AI, and Data Science, Skillshare
2019 - 2020: Teaching Python and ML, Consultant Agile*
2016: Teaching Assistant Physics, Technical University Denmark
2013: TA Seismic Wave Theory, University of Hamburg
2010: TA Programming in Fortran and Matlab, University of Hamburg

Open Source Contributions

Maintainer

DOCUMENTATION

External recognitions

Memberships, Awards, Recognitions

2024: Co-chair WG Modeling of the Resolutions Global Initiative
2022: Fellow of the Software Sustainability Institute for ML for Science
2022: Reviewer of WMO S2S AI challenge
2022: Youtube Partner
2021: Contributing member to WG Data, ITU Focus Group for AI 4 Natural Disaster Management
2020: Kaggle TPU star
2019: Top 81 worldwide Kaggle Code
Multiple hackathon wins

Selected Presentations & Media Appearances

Invited: Guest lecture at Brown U, Lecture Climate Research Centre Singapore, Presentation National University of Singapore, Guest Lecture at University of Hamburg, Keynote at EAGE Workshop on Seismic interpretation with AI, Session Chair Atmospheric Science Conference, Invited Talk NVBM Symposium, PyData Global Big Data Panel, Pydata Global Impact Panel; CfP: EuroScipy Talk, EuroScipy Tutorial, PyData Global Talks, PyData Global Workshop; Podcast: Data Scientist Show, Code for Thought, Software World, MidMeetPy, Undersampled Radio; Academic: Session Chair ECMWF ML Workshop, EAGE Presentations & Posters, SEG Presentation Other: ECMWF Training, MOOC, SSI Fellows Update, SSI Community Call

2024
- Zied Ben Bouallègue, Mariana C. A. Clare, Linus Magnusson, Estibaliz Gascón, Michael Maier-Gerber, Martin Janoušek, Mark Rodwell, Florian Pinault, Jesper S. Dramsch, Simon T. K. Lang, Baudouin Raoult, Florence Rabier, Matthieu Chevallier, Irina Sandu, Peter Dueben, Matthew Chantry, Florian Pappenberger (June 2024) The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of Machine Learning–Based Weather Forecasts in an Operational-Like Context, Bulletin of the American Meteorological Society. DOI: 10.1175/bams-d-23-0162.1
2022
- Vitart F, A.W. Robertson, A. Spring, F. Pinault, R. Roškar, W. Cao, S. Bech, A. Bienkowski, N. Caltabiano, E. De Coning, B. Denis, A. Dirkson, Jesper Sören Dramsch, P. Dueben, J. Gierschendorf, H. S. Kim, K. Nowak, D. Landry, L. Lledó, L. Palma, S. Rasp, S. Zhou (September 2022) Outcomes of the WMO Prize Challenge to Improve Sub-Seasonal to Seasonal Predictions Using Artificial Intelligence, Bulletin of the American Meteorological Society. DOI: 10.1175/bams-d-22-0046.1
- Jesper Soren Dramsch, Anders Nymark Christensen, Colin MacBeth, Mikael Luthje (September 2022) Deep Unsupervised 4-D Seismic 3-D Time-Shift Estimation With Convolutional Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, pp. 1-16. DOI: 10.1109/TGRS.2021.3081516
2021
- Jesper Sören Dramsch, Mikael Lüthje, Anders Nymark Christensen (January 2021) Complex-valued neural networks for machine learning on non-stationary physical data, Computers & Geosciences, pp. 104643. DOI: 10.1016/j.cageo.2020.104643
- Runhai Feng, Niels Balling, Dario Grana, Jesper Soren Dramsch, Thomas Mejer Hansen (October 2021) Bayesian Convolutional Neural Networks for Seismic Facies Classification, IEEE Transactions on Geoscience and Remote Sensing n. 10, pp. 8933-8940. DOI: 10.1109/TGRS.2020.3049012
2020
- Tala Maria Aabø, Jesper Sören Dramsch, Camilla Louise Würtzen, Solomon Seyum, Michael Welch (February 2020) An integrated workflow for fracture characterization in chalk reservoirs, applied to the Kraka Field, Marine and Petroleum Geology, pp. 104065. DOI: 10.1016/j.marpetgeo.2019.104065
- (June 2020) 70 years of machine learning in geoscience in review.
- Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (September 2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v4
- Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (September 2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v6
- Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (September 2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v7
- Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (September 2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v8
- Jesper Sören Dramsch (September 2020) 3D decision volume of SVM, Random Forest, and Deep Neural Network, figshare. DOI: 10.6084/m9.figshare.12640226.v1
- Jesper Sören Dramsch (September 2020) 3D decision volume of SVM, Random Forest, and Deep Neural Network, figshare. DOI: 10.6084/m9.figshare.12640226
- Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (September 2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v5
- Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (September 2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v9
- Gustavo Côrte, Jesper Dramsch, Hamed Amini, Colin MacBeth (September 2020) Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets, Geophysical Prospecting. DOI: 10.1111/1365-2478.12982
2019
- Jesper Sören Dramsch (November 2019) Trace Interpolation with Partial CRS-Stacks. DOI: 10.31237/osf.io/mvxuh
- Jesper Sören Dramsch (November 2019) Seismic Subsalt Imaging with Prestack Data Enhancement Methods. DOI: 10.31237/osf.io/aec7p
- Jesper Sören Dramsch, Gustavo Corte, Hamed Amini, Mikael Luthje, Colin Macbeth (September 2019) Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data, Proceedings of the Second EAGE Workshop Practical Reservoir Monitoring. DOI: 10.31223/osf.io/zytp2
- Jesper Sören Dramsch, Anders Nymark Christensen, Mikael Lüthje (September 2019) Physics and Deep Learning - Incorporating prior knowledge in deep neural networks, Figshare. DOI: 10.6084/m9.figshare.8217518
- Jesper Sören Dramsch, Gustavo Corte, Hamed Amini, Colin MacBeth, Mikael Lüthje (September 2019) Including Physics in Deep Learning - An example from 4D seismic pressure saturation inversion, Figshare. DOI: 10.6084/m9.figshare.8218421
- Jesper Sören Dramsch, Chiheb Trabelski, Olexa Bilaniuk, Dmitriy Serdyuk (September 2019) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v3
- Jesper Sören Dramsch, G. Corte, H. Amini, C. Macbeth, Mikael Luthje (September 2019) Including Physics in Deep Learning – An Example from 4D Seismic Pressure Saturation Inversion, 81st EAGE Conference and Exhibition 2019 (Workshops), pp. 215-220. DOI: 10.3997/2214-4609.201901967
- Jesper Dramsch, Anders Christensen, Colin MacBeth, Mikael Lüthje (October 2019) Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks. DOI: 10.31223/OSF.IO/82BNJ
- Jesper Sören Dramsch (September 2019) Machine Learning in 4D Seismic Data Analysis: Deep Neural Networks in Geophysics.
2018
- (May 2018) Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks, ArXiv e-prints. DOI: 10.3997/2214-4609.201800734
- Jesper Sören Dramsch, Mikail Baykulov, Dirk Gajewski (September 2018) Trace inteprolation with partial CRS stack, Figshare. DOI: 10.6084/M9.FIGSHARE.6958529
- Jesper Sören Dramsch (September 2018) KFold in Deep Learning Lightning Talk, Figshare. DOI: 10.6084/M9.FIGSHARE.7035908
- Jesper Sören Dramsch, Mikael Lüthje (September 2018) Deep-learning seismic facies on state-of-the-art CNN architectures, Figshare. DOI: 10.6084/m9.figshare.7301645.v1
- Jesper Sören Dramsch (September 2018) A practitioner's guide to deep learning in geophysical imaging, Figshare. DOI: 10.6084/m9.figshare.7170299
- Lukas Mosser, Wouter Kimman, Jesper Sören Dramsch, Steve Purves, Alfredo De la Fuente, Graham Ganssle (September 2018) Rapid seismic domain transfer: Seismic velocity inversion and modeling using deep generative neural networks, Figshare. DOI: 10.6084/m9.figshare.6958517.v1
- Jesper Sören Dramsch, Mikail Baykulov, Dirk Gajewski (September 2018) Trace inteprolation with partial CRS stack, Figshare. DOI: 10.6084/m9.figshare.6958529.v1
- Jesper Sören Dramsch (September 2018) A practitioner's guide to deep learning in geophysical imaging, Figshare. DOI: 10.6084/m9.figshare.7170299.v1
- J.S. Dramsch, M. Lüthje (November 2018) Information Theory Considerations In Patch-Based Training Of Deep Neural Networks On Seismic Time-Series, First EAGE/PESGB Workshop Machine Learning. DOI: 10.3997/2214-4609.201803020
- Jesper Sören Dramsch, Mikael Lüthje (September 2018) Deep Learning: From Cats to 4D Seismic - Reducing cycle time and model training cost in asset management, Figshare. DOI: 10.6084/m9.figshare.7422629
- Jesper S. Dramsch, Mikael Lüthje (August 2018) Deep-learning seismic facies on state-of-the-art CNN architectures, SEG Technical Program Expanded Abstracts 2018. DOI: 10.1190/segam2018-2996783.1
- J.S. Dramsch, F. Amour, M. Lüthje (November 2018) Gaussian Mixture Models For Robust Unsupervised Scanning-Electron Microscopy Image Segmentation Of North Sea Chalk, First EAGE/PESGB Workshop Machine Learning. DOI: 10.3997/2214-4609.201803014
2017
- T.M. Aabø, J.S. Dramsch, M.J. Welch, M. Lüthje (June 2017) Correlation of Fractures From Core, Borehole Images and Seismic Data in a Chalk Reservoir in the Danish North Sea, 79th EAGE Conference and Exhibition 2017. DOI: 10.3997/2214-4609.201701283
- T.M. Aabø, M.J. Welch, Jesper Sören Dramsch, Mikael Luthje, S. Seyum, Frédéric Amour, C.L. Würtzen (September 2017) Fracture Characterization and Modelling in the Kraka Field, Danish Hydrocarbon Research and Technology Centre Technology Conference 2017, Lyngby, Denmark, 14/11/2017.
2011
- J. S. Dramsch, D. J. Gajewski (May 2011) Trace Interpolation with Partial CRS Stacks, 73rd EAGE Conference and Exhibition incorporating SPE EUROPEC 2011. DOI: 10.3997/2214-4609.20149421