Jesper Dramsch

Scientist

Forecast Department, Innovation Platform Team

Summary:

Jesper Dramsch implements state-of-the-art machine learning solutions for numerical weather prediction, approaching different topics in the 10 years ML roadmap at ECMWF.

Professional interests:

Machine learning in science
Validation of machine learning models in real-world contexts
Systems thinking
Semi-supervised and unsupervised learning

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

Open Source Contributions

Maintainer

Open Geoscience Awesome List (Former)
Keras Complex

DOCUMENTATION

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 (March 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 (March 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 (March 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 (March 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 (March 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 (March 2020) Complex-Valued Neural Networks in Keras with Tensorflow, figshare. DOI: 10.6084/m9.figshare.9783773.v8
- Jesper Sören Dramsch (March 2020) 3D decision volume of SVM, Random Forest, and Deep Neural Network, figshare. DOI: 10.6084/m9.figshare.12640226.v1
- Jesper Sören Dramsch (March 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 (March 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 (March 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 (March 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 (March 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 (March 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 (March 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 (March 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 (March 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 (March 2018) Trace inteprolation with partial CRS stack, Figshare. DOI: 10.6084/M9.FIGSHARE.6958529
- Jesper Sören Dramsch (March 2018) KFold in Deep Learning Lightning Talk, Figshare. DOI: 10.6084/M9.FIGSHARE.7035908
- Jesper Sören Dramsch, Mikael Lüthje (March 2018) Deep-learning seismic facies on state-of-the-art CNN architectures, Figshare. DOI: 10.6084/m9.figshare.7301645.v1
- Jesper Sören Dramsch (March 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 (March 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 (March 2018) Trace inteprolation with partial CRS stack, Figshare. DOI: 10.6084/m9.figshare.6958529.v1
- Jesper Sören Dramsch (March 2018) A practitioner's guide to deep learning in geophysical imaging, Figshare. DOI: 10.6084/m9.figshare.7170299.v1
- J.S. Dramsch, M. Lüthje (December 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 (March 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 (December 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 (July 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 (March 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