Peer-reviewed Publications (journal or conference)

2023

  • C. Utama, C. Meske, J. Schneider, R. Schlatmann, and C. Ulbrich (2023). Explainable artificial intelligence for photovoltaic fault detection: A comparison of instruments. Solar Energy, 249, 139–151. https://doi.org/10.1016/j.solener.2022.11.018

2022

  • T. Kirschbaum, T. Petit, J. Dzubiella, and A. Bande (2022). Effects of oxidative adsorbates and cluster formation on the electronic structure of nanodiamonds.  J. Comput. Chem., 43, 13, 923-929. https://doi.org/10.1002/jcc.26849
  • G. Pfalz, B. Diekmann, J.-C. Freytag, L. Sryrkh, D.A. Subetto, and B.K. Biskaborn (2022). Improving age-depth correlations by using the LANDO model ensemble. Geochronology, 4, 269–295. https://doi.org/10.5194/gchron-4-269-2022
  • T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-C. Freytag, and G. Grosse (2022). From images to hydrologic networks - Understanding the Arctic landscape with graphs. In ACM Proceedings of the 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022). https://doi.org/10.1145/3538712.3538740
  • B. Ghosh, S. Garg, and M. Motagh (2022). Automatic flood detections from Sentinel-1 data using deep learning architectures.ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 201–208. https://doi.org/10.5194/isprs-annals-V-3-2022-201-2022
  • K. Singh, J. Münchmeyer, L. Weber, U. Leser and A. Bande (2022). Graph neural networks for learning molecular excitation spectra.J. Chem. Theory Comp., 18, 7, 4408-4417. DOI: 10.1021/acs.jctc.2c00255
  • J.* Woollam, J.* Münchmeyer, F. Tilmann, A. Rietbrock, D. Lange, T. Bornstein, T. Diehl, C. Giunchi, F. Haslinger, D. Jozinović, A. Michelini, J. Saul, and H. Soto (2022). SeisBench - A Toolbox for machine learning in seismology. Seismological Research Letters, 93(3), 1695–1709. doi: https://doi.org/10.1785/0220210324 *Equal contribution
  • J. Münchmeyer, U. Leser, and F. Tilmann (2022). A probabilistic view on rupture predictability: All earthquakes evolve similarly.Geophysical Research Letters, 49, 13, e2022GL098344. https://doi.org/10.1029/2022GL098344
  • H. Lilienkamp, S. von Specht, G. Weatherill, G. Caire, and based, fully data-driven, and nonergodic approach. Bull. Seismol. Soc. Am. 112, 1565–1582. https://doi.org/10.1785/0120220008
  • P. Tillmann, K. Jäger, A. Karsenti, L. Kreinin, and C. Becker (2022). Model-chain validation for estimating the energy yield of bifacial Perovskite/Silicon tandem solar cells.Sol. RRL, 202200079. https://doi.org/10.1002/solr.202200079
  • O. Kondrateva, B. Scheuermann, and S. Dietzel (2022). Scalable Flow Optimization for Small Satellite Networks using Benders Decomposition. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 221-230. https://doi.org/10.1109/WoWMoM54355.2022.00041
  • R. Shahan, CW. Hsu, T.M. Nolan, B.J. Cole, I.W. Taylor, A.H.C. Vlot, P.N. Benfey, and U. Ohler  (2022).  A single cell Arabidopsis root atlas reveals developmental trajectories in wild type and cell identity mutants.Developmental Cell, 57(4), 543-560.e9. https://doi.org/10.1016/j.devcel.2022.01.008
  • A.H.C. Vlot, S. Maghsudi, and U. Ohler (2022). Cluster-independent marker feature identification from single-cell omics data using SEMITONES.Nucleic Acids Research, gkac639. https://doi.org/10.1093/nar/gkac639
  • J.A. Fries, N. Seelam, G. Altay, L. Weber, M. Kang, D. Datta, R. Su, S. Garda, B. Wang, S. Ott, M. Samwald, W. Kusa (2022). Dataset Debt in Biomedical Language Modeling. In Proceedings of the Workshop on Challenges & Perspectives in Creating Large Language Models, 137-145. https://doi.org/10.18653/v1/2022.bigscience-1.10
  • X. Wang, U. Leser and L. Weber (2022). BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering. In Proceedings of BioNLP, 298-309. 10.18653/v1/2022.bionlp-1.28
  • N. Miranda, J-C. Freytag, J. Nordin, R. Biswas, V. Brinnel, C. Fremling, ... and J. van Santen (2022). SNGuess: A method for the selection of young extragalactic transients. Astronomy & Astrophysics, 665, A99. https://doi.org/10.1051/0004-6361/202243668
  • N. Veigel, H. Kreibich, and A. Cominola (2022). A Gradient Boosting Approach to Identify Behavioral and Policy Determinants of Flood Resilience in the Continental US. IFAC-PapersOnLine,55, 33, 85-91. https://doi.org/10.1016/j.ifacol.2022.11.014
  • L. Weber, M. Sänger, S. Garda, F. Barth, C. Alt, and U. Leser (2022). Chemical-Protein Relation Extraction with Ensembles of Carefully Tuned Pretrained Language Models. Database,  2022, baac098. https://doi.org/10.1093/database/baac098
  • C. Utama, B. Karg, C. Meske, and S. Lucia (2022). Explainable artificial intelligence for deep learning-based model predictive controllers. In Proceedings of the 26th International Conference on System Theory, Control and Computing (ICSTCC), 464-471. https://doi.org/10.1109/ICSTCC55426.2022.9931794
  • F. Buchner, T. Kirschbaum, A. Venerosy, H. Girard, J.-C. Arnault, B. Kiendl, A. Krueger, K. Larsson, A. Bande, T. Petit, C. Merschjann (2022). Early dynamics of the emission of solvated electrons from nanodiamonds in water.Nanoscale, 14,17188-17195. https://doi.org/10.1039/D2NR03919B
  • F. van Geffen, B. Heim, F. Brieger, R. Geng, I. Shevtsova, L Schulte, S. Stuenzl, N. Bernhardt, E. Troeva, L. Pestryakova, E. Zakharov, B. Pflug, U. Herzschuh, and S. Kruse. SiDroForest: A comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labelled trees, synthetically generated tree crowns and Sentinel-2 labelled image patches.Earth Syst. Sci. Data, 14, 4967–4994, 2022. https://doi.org/10.5194/essd-14-4967-2022
  • I. Michaelis, K. Styp-Rekowski, J. Rauberg, C. Stolle, and M. Korte (2022). Geomagnetic data from the GOCE satellite mission. Earth, Planets, and Space, 74, 135. https://doi.org/10.1186/s40623-022-01691-6
  • K. Styp-Rekowski, I. Michaelis, C. Stolle, J. Baerenzung, M. Korte, and O. Kao (2022). Machine Learning-based Calibration of the GOCE Satellite Platform Magnetometers. Earth, Planets, and Space, 74, 138. https://doi.org/10.1186/s40623-022-01695-2

2021

  • L. Weber, M. Sänger, J. Münchmeyer, M. Habibi, U. Leser, and A. Akbik (2021). HunFlair: An easy-to-use tool for state-of-the-art biomedical named entity recognition.  Bioinformatics, btab042. https://doi.org/10.1093/bioinformatics/btab042
  • S. Redyuk, Z. Kaoudi, V. Markl, and S. Schelter (2021). Automating data quality validation for dynamic data ingestion.  In Proceedings of the International Conference on Extending Database Technology. ISBN 978-3-89318-084-4 on OpenProceedings.org.
  • S. Agarwal, N. Tosi, P. Kessel, S. Padovan, D. Breuer, and G. Montavon (2021). Towards constraining Mars’ thermal evolution using Machine Learning.  Earth and Space Science, 8(4). https://doi.org/10.1029/2020EA001484
  • G. Pfaltz, B. Diekmann, J.-Ch. Freytag, and B.K. Biskaborn (2021). Harmonizing heterogeneous multi-proxy data from lake systems. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104791
  • J. Münchmeyer,  D. Bindi, U. Leser, and F. Tilmann (2021). Earthquake magnitude and location estimation from real time seismic waveforms with a Transformer Network. Geophysical Journal International, 226(2), 1086-1104. https://doi.org/10.1093/gji/ggab139
  • B. Ghosh, M. Haghshenas Haghighi, M. Motagh, and S. Maghsudi (2021). Using generative adversarial networks for extraction of InSAR  signals from large-scale Sentinel-1 interferograms by improving tropospheric noise correctionISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 57–64. https://doi.org/10.5194/isprs-annals-V-3-2021-57-2021
  • T. Rettelbach, M. Langer, I. Nitze, B. Jones. V. Helm, J-Ch. Freytag, and G. Grosse (2021). A quantitative graph-based approach to monitoring ice-wedge trough dynamics in polygonal permafrost landscapes. Remote Sens. 2021, 13, 3098. https://doi.org/10.3390/rs13163098
  • B. Ghosh, M. Motagh, M. Haghshenas Haghighi, M. Stefanova Vassileva, T. Walter, and S. Maghsudi (2021). Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysisIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi.org/10.1109/JSTARS.2021.3097895
  • J.L. Rumberger, X. Yu, P. Hirsch, M. Dohmen, V.E. Guarino, A. Mokarian, L. Mais, J. Funke, and D. Kainmueller (2021). How shift equivariance impacts metric learning for instance segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).
  • B.K. Biskaborn, L. Nazarova, T. Kröger, L.A. Pestryakova, L. Syrykh, G. Pfalz, U. Herzschuh, and B. Diekmann (2021). Late quaternary climate reconstruction and lead-lag relationships of biotic and sediment-geochemical indicators at lake Bolshoe Toko, Siberia.Front. Earth Sci., 9, 703. https://doi.org/10.3389/feart.2021.737353
  • P. Tillmann, B. Bläsi, S. Burger, M. Hammerschmidt, O. Höhn, C. Becker, and K. Jäger (2021).  Optimizing metal grating back reflectors for III-V-on-silicon multijunction solar cells. Opt. Express, 29, 22517. doi: 10.1364/OE.426761
  • P. Rautenstrauch, A.H.C. Vlot, S. Saran, and U. Ohler (2021). Intricacies of single-cell multi-omics data integration. Trends in Genetics.https://doi.org/10.1016/j.tig.2021.08.012
  • L. Weber, M. Sänger, S. Garda, F. Barth, Ch. Alt, and U. Leser (2021). Humboldt @ DrugProt: Chemical-protein relation extraction with pretrained transformers and entity descriptions. In Proceedings of the 7th BioCreative Challenge Evaluation Workshop.
  • S. Agarwal, N. Tosi, P. Kessel,  D. Breuer, and G. Montavon (2021). Deep learning for surrogate modeling of two-dimensional mantle convection. Physical Review Fluids, 6, 113801. https://doi.org/10.1103/PhysRevFluids.6.113801
  • W. J. Foster, G. Ayzel, J. Münchmeyer, T. Rettelbach, N. Kitzmann, T. T. Isson, M. Mutti, and M. Aberhan (2021). Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction. Paleobiology, 1-15.  https://doi.org/10.1017/pab.2022.1 
  • L. Weber, S. Garda, J. Münchmeyer, and U. Leser (2021). Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 1213–1224.
  • S. Baunsgaard, M. Boehm, A. Chaudhary, B. Derakhshan, S. Geißelsöder, P. M. Grulich, M. Hildebrand, K. Innerebner, V. Markl, C. Neubauer, S. Osterburg, O. Ovcharenko, S. Redyuk, T. Rieger, A. R. Mahdiraji, S. B. Wrede, S. Zeuch (2021). ExDRa: Exploratory data science on federated raw data. In Proceedings of the 2021 International Conference on Management of Data, 2450-2463.
  • J.* Münchmeyer, J.* Woollam, F. Tilmann, A. Rietbrock, D. Lange, T. Bornstein, T. Diehl, C. Giunchi, F. Haslinger, D. Jozinović, A. Michelini, J. Saul, and H. Soto (2021). Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers.Journal of Geophysical Research: Solid Earth, 127, 1, e2021JB023499. https://doi.org/10.1029/2021JB023499 *Equal contribution
  • K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao (2021). Calibration of the GRACE-FO satellite platform magnetometers and co-estimation of intrinsic time shift in data.IEEE International Conference on Big Data, 5283-5290. https://doi.org/10.1109/BigData52589.2021.9671977
  • C. Stolle, I. Michaelis, C. Xiong, M. Rother, T. Usbeck, Y. Yamazaki, J.  Rauberg, and K. Styp-Rekowski (2021). Observing Earth’s magnetic environment with the GRACE-FO mission.Earth, Planets and Space, 73, 51. https://doi.org/10.1186/s40623-021-01364-w
  • L. Jaurigue, E. Robertson, J. Wolters, and K. Lüdge (2021). Reservoir computing with delayed input for fast and easy optimisation. Entropy, 23, 1560. https://doi.org/10.3390/e23121560
  • S.A. Vyse, U. Herzschuh, G. Pfalz, L.A. Pestryakova, B. Diekmann, N. Nowaczyk, and B.K. Biskaborn (2021). Sediment and carbon accumulation in a glacial lake in Chukotka (Arctic Siberia) during the late Pleistocene and Holocene: Combining hydroacoustic profiling and down-core analyses. Biogeosciences. https://doi.org/10.5194/bg-18-4791-2021
  • L. Hughes-Allen, F. Bouchard, C. Hatté, H. Meyer, LA. Pestryakova, G. Pfalz, B. Diekmann, D.A. Subetto, and B.K. Biskaborn (2021). 14 000-year Carbon Accumulation Dynamics in a Siberian Lake Reveal Catchment and Lake Productivity Changes, Front. Earth Sci., 9, 1–19. https://doi.org/10.3389/feart.2021.710257
  • Y. Yao, S.R. Kulkarni, K.B. Burdge, I. Caiazzo, K. De, D. Dong, C. Fremling, M.M. Kasliwal, T. Kupfer, J. van Roestel, J. Sollerman, A. Bagdasaryan, E.C. Bellm, …, N. Miranda, T.A. Prince, R. Riddle, B. Rusholme and M.T.  Soumagnac (2021). Multi-wavelength Observations of AT2019wey: A New Candidate Black Hole Low-mass X-ray Binary.The Astrophysical Journal, 920(2), 120. https://doi.org/10.3847/1538-4357/ac15f9

2020

  • P. Tillmann, K. Jäger, and C. Becker (2020). Minimising the levelised cost of electricity for bifacial solar panel arrays using Bayesian optimization. Sustainable Energy Fuels, 4, 254-264.  10.1039/C9SE00750D
  • S. Agarwal, N. Tosi, D. Breuer, S. Padovan, P. Kessel, and G. Montavon (2020). A machine-learning-based surrogate model of Mars’ thermal evolution. Geophysical Journal International, 222(3), 1656-1670. https://doi.org/10.1093/gji/ggaa234
  • L. Weber, K. Thobe, O.A.M. Lozano, J. Wolf, and U. Leser (2020). PEDL: Extracting protein-protein associations using deep language models and distant supervision.  Bioinformatics, 36, Suppl. 1, 490–498. https://doi.org/10.1093/bioinformatics/btaa430.
  • W. D. Xing, L. Weber, and U. Leser (2020). Biomedical event extraction as multi-turn question answering. In Proceedings of the 11th Int. Workshop on Health Text Mining and Information Analysis, 88-96. 10.18653/v1/2020.louhi-1.1
  • J. Ren, L. Lin, K. Lieutenant, C. Schulz, D. Wong, T. Gimm, A. Bande, X. Wang, and T. Petit (2020). Role of dopants on the local electronic structure of polymeric carbon nitride photocatalysts. Small Methods, 2000707. https://doi.org/10.1002/smtd.202000707
  • K. Jäger, P. Tillmann, E.A. Katz, and C. Becker (2020) Perovskite/silicon tandem solar cells: Effect of luminescent coupling and bifaciality. Sol. RRL.https://doi.org/10.1002/solr.202000628
  • J. Münchmeyer, D. Bindi, U. Leser and F. Tilmann (2020). The transformer earthquake alerting model: A new versatile approach to earthquake early warning. Geophysical Journal International, ggaa609. doi.org/10.1093/gji/ggaa609
  • K. Jäger, P. Tillmann, and C. Becker (2020). Detailed illumination model for bifacial solar cells. Opt. Express, 28, 4, 4751-4762. https://doi.org/10.1364/OE.383570
  • H. Grotheer, V. Meyer, T. Riedel, G. Pfalz, L. Mathieu, J. Hefter et al. (2020). Burial and origin of permafrost‐derived carbon in the nearshore zone of the southern Canadian Beaufort Sea.Geophysical Research Letters, 47, e2019GL085897. https://doi.org/10.1029/2019GL085897

2019

  • J. Münchmeyer, D. Bindi, C. Sippl, U. Leser, and F. Tilmann (2019). Low uncertainty multi-feature magnitude estimation with 3D corrections and boosting tree regression: application to North Chile. Geophysical Journal International, 220(1), 142-159. doi.org/10.1093/gji/ggz416
  • L. Weber, J. Münchmeyer, T. Rocktäschel, M. Habibi, and U. Leser (2019). HUNER: Improving biomedical NER with pretraining. Bioinformatics, 36(1), 295-302. 10.1093/bioinformatics/btz528
  • L. Weber, P. Minervini, J. Münchmeyer, U. Leser, and T. Rocktäschel (2019). NLProlog: Reasoning with weak unification for question answering in Natural Language. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics,  6151-6161. 10.18653/v1/P19-1618
  • H.J. Meyer, H. Grunert, T. Waizenegger, L. Woltmann, C. Hartmann, W. Lehner, M. Esmailoghli, S. Redyuk, R. Martinez, Z. Abedjan, and A. Ziehn (2019). Particulate matter matters - The Data Science Challenge @ BTW 2019. Datenbank-Spektrum19(3), pp.165-182.
  • M. Esmailoghli, S. Redyuk, R. Martinez, Z. Abedjan, T. Rabl, and V. Markl (2019). Explanation of air pollution using external data sources. BTW 2019–Workshopband.
  • J. Nordin, V. Brinnel, J. van Santen, M. Bulla, U. Feindt, A. Franckowiak, C. Fremling, A. Gal-Yam, M. Giomi, M. Kowalski, A. Mahabal, N. Miranda, L. Rauch, M. Rigault, S. Schulze, S. Reusch, J. Sollerman, R. Stein, O. Yaron, S. van Veltzen and C. Ward, (2019). Transient processing and analysis using AMPEL: Alert Management, Photometry and Evaluation of Light Curves. Astronomy & Astrophysics, 631, A147. https://doi.org/10.1051/0004-6361/201935634

Other (presentations at conferences or preprints)

2023

2022

  • P. Graniero. Comparison of Unsupervised Algorithms for PV Fault Detection, and Data Sources for Power Nowcasting. (Oral presentation), Cost Action Pearl PV’s Conference- Enabling the Terawatt Transition,  Enschede, The Netherlands, 14-16 March 2022.
  • L. Esguerra, L. Meßner, E. Robertson, N. V. Ewald, M. Gündoğan, and J. Wolters (2022). Optimization and readout-noise analysis of a hot vapor EIT memory on the Cs D1 line. Quantum Physics. https://doi.org/10.48550/arXiv.2203.06151 [Preprint]
  • T. Rettelbach, I. Nitze, and G. Grosse. Polar-6 airborne expedition Perma-X West Alaska 2021. (Oral presentation), 12. Treffen des AK Permafrost der DGP, Online, 6 May 2022.
  • T. Rettelbach, I. Nitze, S. Schäffler, S. Barth, I. Grünberg, J. Hammar, M. Gessner, T. Bucher, J. Brauchle, T. Sachs, J. Boike, and G. Grosse. Super-high-resolution Earth observation datasets of North American permafrost landscapes. (Oral and poster presentation), 8th NASA ABoVE Science Team Meeting, Fairbanks, Alaska, USA, 9-12 May 2022.
  • B. Groenke, F. Miesner, M. Langer, G. Gallego and J. Boike. An energy conserving method for simulating heat transfer in permafrost with hybrid modeling. (Oral presentation), Climate Informatics 2022, Online, 9-13 May 2022.
  • T. Rettelbach, C. Witharana, A. Liljedahl, M. Langer, I. Nitze, J-C. Freytag, and G. Grosse. The Evolution of ice-wedge polygon networks in tundra fire scars. (Poster presentation), 16th International Circumpolar Remote Sensing Symposium, Fairbanks, Alaska, USA, 16-20 May 2022.
  • T. Rettelbach, M. Langer, I. Nitze, V. Helm, J-C. Freytag, and G. Grosse. Quantifying rapid permafrost thaw with computer vision and graph theory. (Poster presentation), ESA Living Planet Symposium, Bonn, Germany, 23-27 May 2022. 
  • B. Ghosh, M. Motagh, S. Garg, M. Sips, D. Eggert. Deep learning, remote sensing and visual analytics to support automatic flood detection. (Oral presentation), EGU General Assembly, Vienna, Austria / Online, 23-27 May 2022.
  • N. Veigel, H. Kreibich, and A. Cominola. Exploring Behavioral Determinants of Flood Insurance Adoption with Explainable Machine Learning in the Continental US.EGU General Assembly, Vienna, Austria / Online, 23-27 May 2022. https://doi.org/10.5194/egusphere-egu22-5839
  • G. Pfalz, B. Diekmann, J.-C. Freytag, L. Syrykh, D. A. Subetto, and B. K.  Biskaborn, Using LANDO as a universal wrapper for applying multiple age-depth modeling systems for sediment records from Arctic lake systems.EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022. https://doi.org/10.5194/egusphere-egu22-8743
  • J. Münchmeyer, J. Woollam, F. Tilmann, A. Rietbrock, D. Lange, T. Bornstein, T. Diehl, C. Giunchi, F. Haslinger, D. Jozinović, A. Michelini, J. Saul, and H. Soto. Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers.EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022. https://meetingorganizer.copernicus.org/EGU22/EGU22-4071.html
  • B. Groenke, M. Langer, G. Gallego and J. Boike. A probabilistic analysis of permafrost temperature trends with ensemble modeling of heat transfer. (PICO presentation), EGU General Assembly, Vienna, Austria, 23–27 May 2022. https://doi.org/10.5194/egusphere-egu22-10509
  • K. Styp-Rekowski, I. Michaelis, C. Stolle, and O. Kao. Magnetic Datasets from Non-dedicated Satellites. (Oral Presentation), Living Planet Symposium, Bonn, Germany, 23-27 May 2022.
  • H. Stolte, J. Sinapius, I. Sadeh, E. Pueschel, D. Berge, M. Weidlich. Detecting VHE blazar flares with deep learning. (Oral presentation), International Conference on Machine Learning for Astrophysics - ML4Astro, Catania, Italy, 30 May - 2 June 2022.
  • P. Tillmann, K. Jäger, A. Karsenti, L. Kreinin, C. Becker. Validation of Energy Yield Model for Bifacial Solar Cells and Prediction of Perovskite/silicon Tandem Solar Cell Performance. (Poster presentation), TandemPV, Freiburg, 30 May - 1 June 2022.
  • J. Münchmeyer, J. Woollam, F. Tilmann, A. Rietbrock, D. Lange, T. Bornstein, T. Diehl, C. Giunchi, F. Haslinger, D. Jozinović, A. Michelini, J. Saul, and H. Soto. (2022). SeisBench: A toolbox for machine learning in seismology. Helmholtz AI Conference, Dresden, Germany, 2-3 June 2022.
  • B. Ghosh, S. Garg, M. Motagh.Automatic flood detection from Sentinel-1 data using Deep learning architectures. (Oral presentation), ISPRS Congress, Nice, France, 6-11 June 2022. 
  • T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-C. Freytag, and G. Grosse. From images to hydrologic networks - Understanding the Arctic landscape with graphs. (Oral presentation), 34th International Conference on Scientific and Statistical Database Management, Copenhagen, Denmark, 6-8 July 2022.
  • L. Nazaretyan, M. Kircher, and U. Leser. Benchmarking machine learning methods for identification of mislabeled data. (Poster presentation), ECCB 2022, Sitges, Spain, September 18-21, 2022.
  • T. Kirschbaum, B. von Seggern, J. Dzubiella, A. Bande and F. Noé. Machine Learning Frontier Orbital Energies of Nanodiamonds. (Poster presentation), 58th Symposium of Theoretical Chemistry, Heidelberg, Germany, 18-22 September 2022.
  • L. Nazaretyan, M. Kircher and M. Schubach. The Regulatory Mendelian Mutation score for GRCh38. bioRxiv 2022.03.14.484240. https://doi.org/10.1101/2022.03.14.484240 [Preprint]
  • B. Groenke, M. Langer, J. Nitzbon, S. Westermann, G. Gallego, and J. Boike (2022). Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer. EGUsphere.https://doi.org/10.5194/egusphere-2022-630 [Preprint]
  • J.A. Fries, L. Weber, N. Seelam, G. Altay, et al. (2022). BigBIO: A Framework for data-centric biomedical natural language processing. https://arxiv.org/abs/2206.15076 [Preprint]
  • H. Laurençon, L. Saulnier, T. Wang, C. Akik, A. V. del Moral, T. Le Scao, ... L. Weber, ... et al. (2022). The BigScience corpus: A 1.6 TB composite multilingual dataset. https://openreview.net/forum?id=UoEw6KigkUn [Preprint]
  • P. Graniero, G.A.F. Basulto, R. Schlatmann, R. Klenk, C. Ulbrich. Online Implementation of a Multiple Linear Regression Model for CIGS Photovoltaic Module Performance. (Poster presentation), 8th World Conference on Photovoltaic Energy Conversion (WCPEC-8), Milan, Italy,  26-30 September 2022.
  • C. Utama, C. Meske, J. Schneider, and C. Ulbrich. Reactive power control in photovoltaic systems through (explainable) artificial intelligence. (Poster presentation), 8th World Conference on Photovoltaic Energy Conversion (WCPEC-8), Milan, Italy, 26-30 September 2022.
  • K. Styp-Rekowski, I. Michaelis, C. Stolle, and O. Kao. Physics-informed Neural Network for Platform Magnetometer Calibration. (Oral Presentation), Swarm Data Quality Workshop, Uppsala, Sweden, 10-14 October 2022.
  • C. Utama, B. Karg, C. Meske, and S. Lucia. Explainable artificial intelligence for deep learning-based model predictive controllers. (Oral presentation), 26th International Conference on System Theory, Control and Computing (ICSTCC), Online, 19-21 October 2022.
  • J. Bader, K. Styp-Rekowski, L. Döhler, S. Becker, and O. Kao. Macaw: The Machine Learning Magnetometer Calibration Workflow. (Oral Presentation), IEEE International Conference on Data Mining, Orlando, Florida, USA, 28 November – 1 December 2022.

 

2021

  • E. Robertson, L. Jaruingue, L. Messner, L. Esguerra, G. Gallego, K. Lüdge, and J. Wolters. A scheme for optical reservoir computers with atomic memory. (Poster presentation), Hot Vapor Workshop, Stuttgart, Online, 22-24 March 2021.
  • F. van Geffen, B. Heim, U. Herzschuh, L. Pestryakova, E. Zakharov, R. Hänsch, B. Demir, B. Kleinschmit, M.Förster, and S. Kruse. SiDro forest: Siberian drone-mapped forest inventory. (Oral presentation), Arctic Science Summit Week, Portugal, Online,19-26 March 2021.
  • S. Redyuk, Z. Kaoudi, V. Markl, and S. Schelter. Automating data quality validation for dynamic data ingestion.  (Oral presentation), International Conference on Extending Database Technology (EDBT), Nicosia, Cyprus, 23-26 March 2021. https://www.youtube.com/watch?v=v9IR1zjqAek
  • G. Pfalz, B. Diekmann, J.-C. Freytag, and B.K. Biskaborn. Harmonizing heterogeneous multi-proxy data from Arctic lake sediment records. (PICO presentation), EGU General Assembly, Online, 19–30 April 2021, EGU21-9401. https://doi.org/10.5194/egusphere-egu21-9401
  • F. van Geffen, B. Heim, U. Herzschuh, L. Pestryakova, E. Zakharov, R. Hänsch, B. Demir, B. Kleinschmit, M.Förster, and S. Kruse. SiDro Forest: Siberian drone-mapped forest inventory. (PICO presentation), EGU General Assembly, Online, 19-30 April 2021, EGU21-15106. https://doi.org/10.5194/egusphere-egu21-15106
  • N. Veigel, H. Kreibich, and A. Cominola. Mining flood insurance Big Data to reveal the determinants of humans' flood resilience. (PICO presentation), EGU General Assembly, Online, 19–30 Apr 2021. https://doi.org/10.5194/egusphere-egu21-3042
  • S. Agarwal, N. Tosi, P. Kessel, S. Padovan, D. Breuer, and G. Montavon. Towards constraining Mars' thermal evolution using machine learning. (PICO presentation), EGU General Assembly, Online, 19-30 Apr 2021. https://doi.org/10.5194/egusphere-egu21-4044
  • J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. Insights into deep learning for earthquake magnitude and location estimation. (PICO presentation), EGU General Assembly, Online, 19-30 April 2021. https://doi.org/10.5194/egusphere-egu21-4718
  • B. Groenke, M. Langer, G. Gallego, and J. Boike. Learning soil freeze characteristic curves with universal differential equations, (PICO presentation), EGU General Assembly, Online, 19–30 Apr 2021. https://doi.org/10.5194/egusphere-egu21-13409
  • B. Ghosh, M. Motagh, M.H. Haghighi, and T. Walter. Using minimal spanning tree based ICA optimization for volcanic unrest determination. (Oral presentation), EGU General Assembly, Online, 19-30 April, 2021.
  • J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. The Ttansformer earthquake alerting model: A data driven approach to early warning. (Oral presentation), Seismological Society of America (SSA) Annual Meeting, Online, 19-23 April 2021.
  • T. Gimm, X. Wang, K. Palczynski, A. Bande, and J. Dzubiella. Nanodiamond-adsorbate interactions studied by DFT. (Poster presentation),Bunsen-Tagung 2021 - Multi-scale modelling & and physical chemistry of colloids, Online, 10-12 May 2021.
  • M. Saenger, L. Weber and U. Leser. WBI at MEDIQA 2021: Summarizing consumer health questions with generative transformersBioNLP Workshop - MEDIQA, 11 June 2021. https://www.aclweb.org/anthology/2021.bionlp-1.9.pdf
  • P. Tillmann,  K. Jäger, E.A. Katz, and C. Becker. Relaxed current-matching constraints in perovskite/silicon tandem solar cell by bifacial operation and luminescent coupling. (Oral presentation), IEEE Photovoltaic Specialists Conference (PVSC), Online, 20-25 June 2021.
  • B. Ghosh, M.H. Haghighi, M. Motagh, and S. Maghsudi. Using generative adversarial networks for extraction of InSAR signals from large-scale Sentinel-1 interferograms by improving tropospheric noise correction,  (Oral presentation), ISPRS Congress, 4-10 July 2021.
  • P. Graniero, D. Rößler, C. Ulbrich, and N. Kliewer. Potentials and challenges for integration of electric bus fleets and PV-systems. (Oral presentation), 31st European Conference on Operational Research (EURO 2021), Athens, Greece / Online, 11-14 July 2021.
  • A.H.C. Vlot, S. Maghsudi, and U. Ohler. Single-cEll Marker IdentificaTiON by Enrichment Scoring. (Poster and oral presentation), ISMB/ECCB 2021, Online, 25-30 July 2021.
  • L. Nazaretyan. The Regulatory Mendelian Mutation (ReMM) score for GRCh38. (Poster presentation), ISMB/ECCB 2021, Online, 25-30 July, 2021.
  • H. Stolte. Checking plausibility in exploratory data analysis.  (Oral presentation), 47th International Conference on Very Large Databases, Copenhagen, Denmark, 16-20 August 2021. http://ceur-ws.org/Vol-2971/paper08.pdf
  • K. Styp-Rekowski, C. Stolle, O. Kao, and I. Michaelis. Satellite Platform Magnetometer Calibration Using Machine Learning. (Oral Presentation), Joint Scientific Assembly IAGA-IASPEI, Online, 21-27 August 2021.
  • K. Styp-Rekowski, C. Stolle, O. Kao, and I. Michaelis. Automatic Calibration of Satellite Platform Magnetometers with Neural Network-based Time Shift Approximation. (Oral Presentation), Joint Scientific Assembly IAGA-IASPEI, Online, 21-27 August 2021.
  • L. Nazaretyan, M. Schubach, and M. Kircher. The Regulatory Mendelian Mutation (ReMM) score for GRCh38. (Poster presentation), ESHG 2021, Online, August 28–31, 2021.
  • S. Agarwal, N. Tosi, P. Kessel, D. Breuer, and G. Montavon. Deep learning for surrogate modelling of 2D mantle convection. (Oral presentation), German-Swiss Geodynamics Workshop 2021, Bad Belzig, 29 Aug–1 Sep 2021.
  • J. Nordin, N. Miranda, V. Brinnel, and J. van Santen. The AMPEL System as an official VRO community broker: Current developments and applications. (Oral presentation), Virtual Annual Meeting of the German Astronomical Society, 13-17 September, 2021.
  • T. Gimm, X. Wang, K. Palczynski, A. Bande, and J. Dzubiella. Nanodiamond-adsorbate interactions studied by DFT. (Poster presentation), 57th Symposium of Theoretical Chemistry, Online, 20-24 September 2021.
  • J. Münchmeyer, J. Woollam, D. Jozinovic, J. Saul, A. Michelini, C. Giunchi, T. Diehl, F. Haslinger, D. Lange, A. Rietbrock, and F. Tilmann. SeisBench: A framework for machine learning in seismology. (Oral presentation) 37th General Assembly of the European Seismological Commission, Online, 19-24 September 2021.
  • S. Agarwal, N. Tosi, P. Kessel, D. Breuer, and G. Montavon. Deep learning for surrogate modelling of 2D mantle convection. (Oral presentation), European Planetary Science Congress 2021, Online, 13–24 Sep 2021. https://doi.org/10.5194/epsc2021-218
  • A.H.C. Vlot, S. Maghsudi, and U. Ohler. Identification of cis-regulatory regions using Single-cEll Marker IdentificaTiON by Enrichment Scoring (SEMITONES). (Poster presentation), EMBO Workshop Enhanceropathies: Understanding enhancer function to understand human disease, 6-9 October 2021.
  • P. Graniero. Data driven mitigation measures in advanced PV plant monitoring. (Oral presentation), Intersolar Conference, Munich, Germany, 6-7 October 2021.
  • K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao. Machine Learning-based Information Extraction from Non-dedicated Sensors. (Oral Presentation), Photonics Days Berlin Brandenburg, Berlin, Germany, 4-7 October 2021.
  • K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao. Calibration of GRACE-FO and GOCE Platform Magnetometers Using Machine Learning. (Oral Presentation), Swarm Data Quality Workshop, Athens, Greece, 11-16 October 2021.
  • T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-Ch. Freytag, and G. Grosse. Quantifying erosional dynamics in ice-wedge networks with computer vision and graph theory. (Invited talk), Regional Conference on Permafrost, Online, 24-29 October 2021.
  • B. Groenke, M. Langer, G. Gallego and J. Boike. A model-driven approach to quantifying uncertainty in permafrost temperature trends. (Poster presentation), 6th Data Science Symposium, Bremen, Germany, 8-9 Nov 2021.
  • S. Agarwal, N. Tosi, P. Kessel, D. Breuer, and G. Montavon. Deep learning for surrogate modelling of 2D mantle convection. (Oral presentation), The 74th Annual Meeting of the Division of Fluid Dynamics, Online, 21-23 Nov 2021.
  • T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-Ch. Freytag, and G. Grosse. Evaluating the effects of tundra fires on soil microtopography and hydrologic surface networks in polygonal permafrost landscapes. (Oral presentation), AGU Fall Meeting, New Orleans, USA / Online, 13-17 December 2021.
  • J. Münchmeyer, U. Leser, and F. Tilmann. A probabilistic view of earthquake rupture predictability. (Oral presentation), AGU Fall Meeting, New Orleans, USA / Online 13-17 December 2021.
  • N. Veigel, H. Kreibich, and A. Cominola. Mining Flood Insurance Big Data to incorporate behavioural and social aspects in flood risk modelling (Poster presentation), AGU Fall Meeting, New Orleans, USA / Online, 13–17 Dec 2021.
  • S. Agarwal, N. Tosi, P. Kessel, D. Breuer, and G. Montavon. A machine learning framework for constraining mantle convection parameters. (Oral presentation), American Geophysical Union Fall Meeting,  New Orleans, USA, 13-17 Dec 2021.
  • K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao. Calibration of the GRACE-FO Satellite Platform Magnetometers and Co-Estimation of Intrinsic Time Shift in Data. (Oral Presentation), IEEE Big Data 2021, Online, 15-18 December 2021.

2020

  • B. Ghosh, M. Motagh, S. Maghsudi, and M.H. Haghighi. Reduction of tropospheric noise delay from large-scale interferograms using generative adversarial networks. (Oral presentation), 40th Annual Scientific and Technical Conference of the DGPF, Stuttgart, Germany, 4 - 6 March 2020.
  • S. Redyuk, V. Markl, and S. Schelter. Towards unsupervised data quality validation on dynamic data.  (Workshop paper and presentation), ETMLP 2020, Copenhagen, Denmark, 30 March 2020. https://www.youtube.com/watch?v=Xhq8X64RA1Q
  • S. Agarwal, N. Tosi, P. Kessel, D. Breuer, S. Padovan, and G. Montavon. Mars’ thermal evolution from machine-learning-based 1D surrogate modelling, (Oral presentation), EGU General Assembly, Online, 4-8 May 2020.
  • J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. End-to-end PGA estimation for earthquake early warning using transformer networks. (Oral presentation), EGU General Assembly, Online, 4-8 May 2020.
  • B. Ghosh, M. Motagh, S. Maghsudi, and M.H. Haghighi. Automatic flood monitoring based on SAR intensity and interferometric coherence using machine learning. (Oral presentation), EGU General Assembly, Online, 4-8 May, 2020.
  • H. Lilienkamp, F. Cotton, G. Caire, G. Weatherill, and S. von Specht. Fully data-driven, partially non-ergodic ground motion modeling using convolutional neural networks. (Poster presentation), Taiwan Earthquake Research Center Annual Meeting, 20-22 October 2020.
  • P. Tillmann, C. Becker, and K. Jäger. Analysing the angular reflection losses of bifacial solar cells.  (Poster presentation), European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC), Online, 7-11 September 2020.
  • A.H.C. Vlot, S. Maghsudi, and U. Ohler. Identification of marker genes and cis-regulatory regions using Single-cEll Marker IdentificaTiON by Enrichment Scoring (SEMITONES). (Poster presentation), 13th annual RECOMB/ISCB Conference on Regulatory & Systems Genomics with DREAM Challenges, 16-19 September 2020.
  • P. Graniero, A. Louwen, R. Schlatmann, and C. Ulbrich. Comparison of different data sources for  Machine Learning algorithms in photovoltaic output power estimation. (Poster presentation), 37th EU PVSEC, Online, 7-11 September 2020.
  • S. Agarwal, N. Tosi, P. Kessel, D. Breuer, S. Padovan, and G. Montavon. Learning high dimensional surrogates from mantle convection simulations. (Oral presentation), 73rd Annual Meeting of the APS Division of Fluid Dynamics, Online, 23 November 2020.
  • J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. The transformer earthquake alerting model: Improving earthquake early warning with deep learning. (Oral presentation), AGU Fall Meeting, Online, 1-17 December 2020.
  • T. Rettelbach, M. Langer, I. Nitze, B. Jones. J. Boike, J-Ch. Freytag, and G. Grosse. Potential von Graphen für die quantitative Analyse von tauenden Eiskeilpolygonnetzwerken. (Oral presentation), 11. Treffen des AK Permafrost der DGP, Online, 11 December 2020.
  • T. Rettelbach, G. Grosse, I. Nitze, J. Brauchle, T. Bucher, M. Gessner, B.M. Jones, J. Boike, M. Langer, and J-Ch. Freytag. A quantitative graph-based assessment of ice-wedge trough dynamics in polygonal thermokarst landscapes of the Anaktuvuk River fire scar. (Oral presentationh), AGU Fall Meeting, Online, 1-17 December 2020.

 

2019

  • J. Münchmeyer, D. Bindi, C. Sippl, and F. Tilmann. Increasing magnitude scale consistency by combining multiple waveform features through Machine Learning.(Oral presentation), EGU General Assembly, Vienna, 7-12 April 2019.
  • S. Redyuk. Automated documentation of end-to-end experiments in data science.(Workshop paper and presentation), ICDE’19, Macau, China, 8-11 April, 2019. 10.1109/ICDE.2019.00243
  • H. Lilienkamp, G. Weatherill, F. Cotton, and Giuseppe Caire. The role of spatial cross-correlation structures of ground motion fields forseismic risk assessment of spatially distributed assets and infrastructurenetworks.  (Poster presentation), EGU General Assembly, Vienna, Austria, 7–12 April 2019.
  • S. Redyuk, S. Schelter, T. Rukat, V. Markl, and F. Biessmann. Learning to validate the predictions of black box machine learning models on unseen data.(Workshop paper and presentation), HILDA’19, Amsterdam, Netherlands, 5 July, 2019. doi.org/10.1145/3328519.3329126
  • S. Agarwal, N. Tosi, D. Breuer, S. Padovan, P. Kessel, and G. Montavon. Unravelling interior evolution of terrestrial planets using machine learning. (Oral presentation), Artificial Intelligence in Astronomy at ESO, Garching, Germany, 22-26 July 2019.
  • L. Weber, P. Minervini, J. Münchmeyer, U Leser, and T Rocktäschel. NLProlog: Reasoning with weak unification for question answering in natural language.  (Poster presentation), 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 28 July - 2 August, 2019.
  • G. Pfalz, B. Diekmann, J.-C. Freytag, and B.K. Biskaborn. Decipher Arctic lakes ecosystem dynamics. (Poster presentation), YES Congress 2019, Berlin, Germany, 9-13 September, 2019.
  • S. Agarwal, N. Tosi, D. Breuer, P. Kessel, and G. Montavon. Using machine learning to predict 1D steady-state temperature profiles from compressible mantle convection simulations. (Oral presentation),72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, USA, 23-26 November 2019.
  • J. Münchmeyer, D. Bindi, U. Leser, and F. Tilmann. Convolutional event embeddings for fast probabilistic earthquake assessment. (Poster presentation), AGU General Assembly, San Francisco, 9-13 December 2019.
  • H. Lilienkamp, S. Specht, G. Weatherill, and F. Cotton. Exploring the physics and uncertainties in spatial cross-correlation models for ground motion intensity measures.  (Oral presentation),AGU General Assembly, San Francisco, USA, 9–13 December 2019.