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
  • P. Graniero, M. Khenkin, H. Köbler, N.T. Putri Hartono, R. Schlatmann, A. Abate, E. Unger, T.J. Jacobsson and C. Ulbrich (2023). The challenge of studying perovskite solar cells’ stability with machine learning. Front. Energy Res., Sec. Solar Energy, 11. https://doi.org/10.3389/fenrg.2023.1118654
  • N. H. Chan, M.Langer, B. Juhls, T. Rettelbach, P. Overduin, K. Huppert and J. Braun (2023). An Arctic Delta Reduced Complexity Model and its Reproduction of Key Geomorphological Structures. Earth Surface Dynamics, 259–285. https://doi.org/10.5194/esurf-11-259-2023
  • W. J. Foster, B. J. Allen, N. H. Kitzmann, J. Münchmeyer, T. Rettelbach,J. D. Witts, , R. J. Whittle, E. Larina, M. E. Clapham and A. M.  Dunhill (2023). How predictable are mass extinction events?Royal Society Open Science, 10 (3), 221507. https://doi.org/10.1098/rsos.221507
  • K. Palczynski, T. Kirschbaum, A. Bande, J. Dzubiella (2023). Hydration Structure of Diamondoids from Reactive Force Fields. J. Phys. Chem. C, 127, 6, 3217–3227. https://doi.org/10.1021/acs.jpcc.2c07777
  • T. Kirschbaum, B. von Seggern, J. Dzubiella, A. Bande, F. Noé (2023). Machine Learning Frontier Orbital Energies of Nanodiamonds. J. Chem. Theory Comput. https://doi.org/10.1021/acs.jctc.2c01275

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 correction.  ISPRS 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 analysis. IEEE 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-Spektrum, 19(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

  • M. Yang, E. Robertson, L. Esguerra, K. Busch and J. Wolters (2023). Optical convolutional neural network with atomic nonlinearity.https://doi.org/10.48550/arXiv.2301.09994 [Preprint]
  • L. Meßner, E. Robertson, L. Esguerra, K. Lüdge and J. Wolters (2023). Multiplexed random-access optical memory in warm cesium vapor. https://doi.org/10.48550/arXiv.2301.04885 [Preprint]
  • T. Kirschbaum, B. von Seggern, J. Dzubiella, A. Bande, and F. Noé. Machine Learning Frontier Orbital Energies of Nanodiamonds. (Oral presentation), Asia Pacific Conference of Theoretical and Computational Chemistry, Quy Nhon, Vietnam, 19-23 February 2023.
  • K. Singh. Machine Learning for Quantum Dynamics. (Oral presentation), Asia Pacific Conference of Theoretical and Computational Chemistry, Quy Nhon, Vietnam, 19-23 February 2023.
  • B. Groenke, M. Langer, G. Gallego, and J. Boike: Exploring physics-informed machine learning for accelerated simulation of permafrost processes.EGU General Assembly, Vienna, Austria, 24–28 April 2023. https://doi.org/10.5194/egusphere-egu23-10135

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, Germany, 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.

 

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 transformers. BioNLP 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.
  • 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.