Randa Natras, Dipl.-Ing. 
Deutsches Geodätisches Forschungsinstitut undLehrstuhl für Geodätische Geodynamik (Prof. Seitz)
Postadresse
Arcisstr. 21
80333 München
Tel.: +49 (89) 23031-1277
Fax.: +49 (89) 23031-1240
randa.natras@tum.de
Research Area
Artificial Intelligence / Machine Learning, Ionosphere, Space Weather, Global Navigation Satellite Systems, Geodesy
Academic Career
- since 10/2019, Doctoral candidate at Technische Universität München (TUM)
- since 04/2019 , Researcher at Deutsches Geodätisches Forschungsinstitut der TU Munich (DGFI-TUM) with DAAD Research Grants
- 05/2022 - 06/2022 & 11/2022, Guest investigator at Royal Observatory of Belgium (ROB), Brussels
- 09/2022 - 10/2022, Research stay at Technical University of Catalonia (UPC), Barcelona, Spain
- 06/2022, Young Scientist Award from the International Union of Radio Science (URSI)
- 06/2022, Invited talk: "Interpretable Machine Learning for Ionosphere Forecasting with Uncertainty Quantification", 1st Workshop on Data Science for GNSS Remote Sensing, Potsdam, Germany
- 05/2022, STCE Seminar: "Investigation of PROBA2 LYRA data for predicting the Vertical Total Electron Content of the ionosphere with Machine Learning", ROB, Brussels, Belgium
- 2021 - 2022, Organizer and Co-chair of Geodesy and Geoinformatics Symposium, IAT 2022, Bosnia and Herzegovina
- since 2020, Convener of session: "Data Science and Machine Learning in Geodesy", EGU, Austria
- 07/2020, Student of NASA Heliophysics Summer School: "Explosive Space Weather Events and their Impacts"
- 08/2019 - 08/2020, Teaching assistant in "Numerical Modeling" at TUM, Germany
- 11/2018, IAG (International Association of Geodesy) Travel Award for IWGI2018 in China
- 10/2018, Best Young Scientist Paper, 1st Western Balkan Conference on GIS, Geodesy and Geomatics
- 11/2017 - 07/2018, Research stay at Technische Universität Wien, Austria with OeAD Grant
- 2017, IAG Travel Award for IAG-IASPEI Joint Scientific Assembly in Japan
- 2016, Charter of the University of Sarajevo for the best Bachelor and Master student
- 2013 - 2016, Master Degree in Geodesy and Geoinformatics, Faculty of Civil Engineering, University of Sarajevo, Bosnia and Herzegovina
- 08/2015 - 09/2015, Research intern, Faculty of Mathematics and Natural Sciences, University of Oslo, Norway
- 2010 - 2013, Bachelor Degree in Geodesy and Geoinformatics, Faculty of Civil Engineering, University of Sarajevo, Bosnia and Herzegovina
Functions/Memberships
International Association of Geodesy (IAG)
- Inter-Commission Committee on Theory, Joint Study Group T.29 "Machine learning in geodesy", Member (since 2019)
Publications
2023
Natras R., Goss A., Halilovic D., Magnet N., Mulic M., Schmidt M., Weber R.: Regional Ionosphere Delay Models Based on CORS Data and Machine Learning. NAVIGATION: Journal of the Institute of Navigation, 70(3), navi.577, 10.33012/navi.577, 2023
(Open Access)
Natras R., Soja B., Schmidt M.: Uncertainty Quantification for Machine Learning-Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting. Space Weather, 10.1029/2023SW003483, 2023
(Open Access)
(Open Access)
2022
Barta V., Natras R., Srećković V., Koronczay D., Schmidt M., Šulic D.: Multi-instrumental investigation of the solar flares impact on the ionosphere on 05–06 December 2006. Frontiers in Environmental Science, 904335, 10.3389/fenvs.2022.904335, 2022
(Open Access)
Natras R., Halilovic D., Mulic M., Schmidt M.: Mid-latitude Ionosphere Variability (2013–2016), and Space Weather Impact on VTEC and Precise Point Positioning. Advanced Technologies, Systems, and Applications VII, 471–491 , 10.1007/978-3-031-17697-5_37, 2022
Natras R., Soja B., Schmidt M.: Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting. Remote Sensing, 14(15), 3547, 10.3390/rs14153547, 2022
(Open Access)
(Open Access)
Natras R., Soja B., Schmidt M.: Machine Learning Ensemble Approach for Ionosphere and Space Weather Forecasting with Uncertainty Quantification. 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC), IEEE Xplore, 10.23919/AT-AP-RASC54737.2022.9814334, 2022
2021
Natras R., Schmidt M.: Machine Learning Model Development for Space Weather Forecasting in the Ionosphere. Proceedings of the CIKM 2021 Workshops co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), Institute of Advanced Research in Artificial Intelligence (IARAI), Austria, Online, 2021
(Open Access)
Posters/Presentations
2022
Barta V., Natras R., Sreckovic V., Koronczay D., Schmidt M., Sulic D.: Multi-instrumental investigation of the solar flares impact on the ionosphere occurring in December 2006. European Geosciences Union (EGU) General Assembly, Vienna, Austria, https://doi.org/10.5194/egusphere-egu22-5277, 2022, 2022
Barta V., Natras R., Sreckovic V., Koronczay D., Schmidt M., Sulic D.: Multi-instrumental investigation of the solar flares impact on the ionosphere on 05-06 December 2006. 8th IAGA/ICMA/SCOSTEP Workshop on Vertical Coupling in the Atmosphere-Ionosphere System, Sopron, Hungary, 2022
Barta V., Natras R., Sreckovic V., Koronczay D., Schmidt M., Sulic D.: Multi-instrumental investigation of the solar flares impact on the ionosphere on 05–06 December 2006. 18th European Space Weather Week (ESWW2022), Zagreb, Croatia, 2022
(Poster)
Le N., Männel B., Natras R., Sakic P., Deng Z., Schuh H.: Apply noise filters for better forecast performance in Machine Learning. European Geosciences Union (EGU) General Assembly, Vienna, Austria, https://doi.org/10.5194/egusphere-egu22-4039, 2022
Natras R., Halilovic Dz., Mulic M., Schmidt M.: Mid-latitude Ionosphere Variability and Modeling including Space Weather Impact on VTEC and PPP. Symposium in Geodesy and Geoinformatics, 13th Annual Days of BHAAAS in Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina, online, 2022
Natras R., Soja B., Schmidt M., Dominique M., Türkmen A.: Machine Learning Approach for Forecasting Space Weather Effects in the Ionosphere with Uncertainty Quantification. European Geosciences Union (EGU) General Assembly, Vienna, Austria, https://doi.org/10.5194/egusphere-egu22-5408, 2022
Natras R., Soja B., Schmidt M.: Interpretable Machine Learning for Ionosphere Forecasting with Uncertainty Quantification. D4G: 1st Workshop on Data Science for GNSS Remote Sensing, Potsdam, Germany, 2022
Natras R., Soja B., Schmidt M.: Machine Learning Ensemble Approach for Ionosphere and Space Weather Forecasting with Uncertainty Quantification. 3rd URSI Atlantic / Asia-Pacific Radio Science Conference (URSI AT-AP-RASC 2022), Gran Canaria, Spain, 2022
Natras R., Soja B., Schmidt M.: Uncertainty Quantification for Ionosphere Forecasting with Machine Learning. International Workshop on GNSS Ionosphere (IWGI2022) - Observations,Modelling and Applications, Institute for Solar-Terrestrial Physics, German Aerospace Center (DLR), Neustrelitz, Germany and online, 2022
2021
Natras R., Schmidt M.: Ensemble Machine Learning for Geodetic Space Weather Forecasting. Scientific Assembly of the International Association of Geodesy (IAG) 2021, Beijing + online, 2021
Natras R., Schmidt M.: Ionospheric VTEC Forecasting using Machine Learning. European Geosciences Union (EGU) General Assembly, Online, 10.5194/egusphere-egu21-8907, 2021
Deutsches Geodätisches Forschungsinstitut
Prof. Dr.-Ing. habil. Florian Seitz
Technische Universität München
Arcisstraße 21
80333 München
Arcisstraße 21
80333 München