Jannes Münchmeyer

Phd student

Machine learning researcher @ GFZ Potsdam and HU Berlin in the HEIBRiDS graduate school; Working on fast assessment of earthquakes

Developer of Seisbench - A toolbox for machine learning in Seismology

Previously working on BioNLP and gene regulatory networks

Research interests

Earthquake early warning

Probabilistic machine learning

Earthquake nucleation

Uncertainty quantification

Magnitude scale calibration

Domain adaptation and transfer learning

Publications

2021

Münchmeyer, J.*, Woollam, J.*, Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J. & Soto, H. (2021). Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers. arxiv preprint. *: Equal contribution [paper] [code]

Woollam, J.*, Münchmeyer, J.*, Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J. & Soto, H. (2021). SeisBench - A Toolbox for Machine Learning in Seismology. arxiv preprint. *: Equal contribution [paper] [code]

Weber, L., Garda, S., Münchmeyer, J. & Leser, U. (2021). Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling. EMNLP 2021, Punta Cana, Dominican Republic. [paper] [code]

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network. Geophysical Journal International. [paper] [code]

Weber, L., Sänger, M., Münchmeyer, J., Habibi, M., Leser, U., & Akbik, A. (2021). HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition. Bioinformatics. [paper] [code]

2020

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2020). The transformer earthquake alerting model: A new versatile approach to earthquake early warning. Geophysical Journal International. [paper] [code]

2019

Münchmeyer, J., Bindi, D., Sippl, C., Leser, U., & Tilmann, F. (2019). Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile. Geophysical Journal International, 220(1), 142-159. [paper] [code]

Weber, L.*, Münchmeyer, J.*, Rocktäschel, T., Habibi, M., & Leser, U. (2019). HUNER: Improving Biomedical NER with Pretraining. Bioinformatics. *: Equal contribution [paper] [code]

Weber, L., Minervini, P., Münchmeyer, J., Leser, U., & Rocktäschel, T. (2019). NLprolog: Reasoning with Weak Unification for Question Answering in Natural Language. ACL 2019, Florence, Italy. [paper] [code]

2017

Trescher, S., Münchmeyer, J., & Leser, U. (2017). Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization. BMC systems biology, 11(1), 41. [paper]

Conference contributions

2021

Münchmeyer, J., Leser, U., & Tilmann, F. (2021). A probabilistic view of earthquake rupture predictability. AGU Fall Meeting 2021, New Orleans, USA. [abstract]

Woollam, J., Münchmeyer, J., Giunchi, C., Jozinovic, D., Diehl, T., Saul, J., Michelini, A., Haslinger, F., Lange, D., Tilmann, F., & Rietbrock, A. (2021). SeisBench: A Python Toolbox for Benchmarking and Deploying ML Models in Seismology. AGU Fall Meeting 2021, New Orleans, USA. [abstract]

Münchmeyer, J., Woollam, J., Giunchi, C., Jozinovic, D., Diehl, T., Saul, J., Michelini, A., Haslinger, F., Lange, D., Rietbrock, A., & Tilmann, F. (2021). SeisBench: A framework for machine learning in seismology. General Assembly of the European Seismological Commission. Virtual conference 2021.

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). Insights into deep learning for earthquake magnitude and location estimation. EGU virtual conference 2021. [abstract]

Woollam, J., Münchmeyer, J., Giunchi, C., Jozinovic, D., Diehl, T., Saul, J., Michelini, A., Haslinger, F., Lange, D., Tilmann, F., & Rietbrock, A. (2021). SeisBench: A toolbox for benchmarking and applying machine learning in seismology. EGU virtual conference 2021. [abstract]

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2021). The Transformer Earthquake Alerting Model: A Data Driven Approach to Early Warning. SSA virtual conference 2021.

2020

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2020). The Transformer Earthquake Alerting Model: Improving Earthquake Early Warning with Deep Learning. AGU virtual conference 2020. [recorded talk]

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. (2020). End-to-end PGA estimation for earthquake early warning using transformer networks. EGU virtual conference 2020. [presentation slides]

2019

Münchmeyer, J., Bindi, D., Leser, U., & Tilmann, F. J. (2019). Convolutional event embeddings for fast probabilistic earthquake assessment. AGU Fall Meeting 2019, San Francisco, USA.

Münchmeyer, J., Bindi, D., Sippl, C., & Tilmann, F. (2019). Increasing magnitude scale consistency by combining multiple waveform features through machine learning. EGU General Assembly 2019, Vienna, Austria.

Professional activities

2021

Convener of session "Decoding Geophysical Signatures With Machine Learning: Novel Methods and Results" at AGU Fall Meeting 2021, New Orleans, together with Pyrak-Nolte, L., Chen, T., & Woollam, J. [link]

2020

Convener of session "Data Science and Machine Learning for Natural Hazards and Seismology" at EGU virtual conference 2020 together with Tang, H., Chen, K., Olen, S. & Corbi, F. [link]

Contact

Email
munchmej (at) gfz-potsdam (dot) de