CONFERENCES

2023

• 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
• D. Collin, S. Bianco, G. Gallego, Y. Shprits. Forecasting solar wind speed from solar EUV images. (Oral and poster presentation), International Workshop on Machine Learning and Computer Vision in Heliophysics, Sofia, Bulgaria, 19-21 April 2023.
• D. Collin, S. Bianco, G. Gallego, Y. Shprits. Forecasting solar wind speed by machine learning based on coronal hole characteristics. (Poster presentation), EGU General Assembly , Vienna, Austria, 24–28 April 2023. https://doi.org/10.5194/egusphere-egu23-6968
• D. Collin, S. Bianco, G. Gallego, Y. Shprits. Forecasting solar wind speed from solar EUV images. (Oral presentation), IUGG General Assembly, Berlin, Germany, 11-20 July 2023. https://doi.org/10.57757/IUGG23-2070
• B. Groenke, M. Langer, J. Nitzbon, S. Westermann, G. Gallego, and J. Boike. Explaining uncertainty in the thermal state of permafrost with Bayesian inversion of hydrothermal dynamics. 6th European Conference on Permafrost, Puigcerdà, Catalonia, Spain, 18-22 June 2023.
• B. Groenke, M. Langer, G. Gallego, and J. Boike. Applications of physic-informed machine learning in accelerating dynamical models of permafrost processes. IUGG23 General Assembly, Berlin, Germany, 11-20 July, 2023.
• V. Döpper et al. Unlocking the Potential of Arctic to Boreal Multi-Source Point Clouds: Deep and Transfer Learning for Automated Segmentation and Classification. (Poster presentation), SilviLaser 2023, London, UK, 6-8 September, 2023.
• J. Schaible, B. Nouri, T. Kotzab, M. Loevenich, N. Blum, A. Hammer, K. Jäger, C. Becker and S. Wilbert. Application of Nowcasting to Reduce the Impact of Irradiance Ramps on PV Power Plants. (Oral presentation), EU PVSEC, Lisbon, Portugal, 18-22 September 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.

• 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, ..., 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, ..., and H. Soto. 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.

• P. Graniero, G.A.F. Basulto, R. Schlatmann, R. Klenk, and 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 Transformer 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.

• 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-C. 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-C. 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-C. 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-C. 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 G. 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.