Dr David Kreil
Dr Kreil is a leading data scientist with expertise in modelling high dimensional spaces, recently collaborating with the US FDA on benchmarking and calibrating next generation sequencing assays. He has chaired international data analysis contests in the scientific community at CAMDA since 2008.
He has since brought his expertise to establish the Traffic4cast contest as a Competition Track at NeurIPS 2019. His current research interests include paradigm change detection, robustness, sensor fusion, and informative priors.
He is also a professor at Boku University Vienna.
C. Eichenberger, M. Neun, H. Martin, P. Herruzo, M. Spanring, Y. Lu, S. Choi, V. Konyakhin, N. Lukashina, A. Shpilman, N. Wiedemann, M. Raubal, B. Wang, H. L. Vu, R. Mohajerpoor, C. Cai, I. Kim, L. Hermes, A. Melnik, R. Velioglu, M. Vieth, M. Schilling, A. Bojesomo, H. Al Marzouqi, P. Liatsis, J. Santokhi, D. Hillier, Y. Yang, J. Sarwar, A. Jordan, E. Hewage, D. Jonietz, F. Tang, A. Gruca, M. Kopp, D. Kreil, and S. Hochreiter (2022) Traffic4cast at NeurIPS 2021 – Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes. NeurIPS 2021 Competitions and Demonstrations Track, PMLR, 176, 97-112, 2022-07-20. (more) (download)
O. Ghorbanzadeh, Y. Xu, P. Ghamisi, M. Kopp, and D. Kreil (2022) Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection. arXiv:2206.00515, 2022-06-01. (more) (download)
P. Herruzo, A. Gruca, L. Lliso, X. Calbet, P. Rípodas, S. Hochreiter, M. Kopp, and D. P. Kreil (2021) High-Resolution Multi-Channel Weather Forecasting – First Insights on Transfer Learning from the Weather4cast Competitions 2021. 2021 IEEE International Conference on Big Data (Big Data), 5750-5757, 2021-12-15. (more) (download)
A. Gruca, P. Herruzo, P. Rípodas, A. Kucik, C. Briese, M. K. Kopp, S. Hochreiter, P. Ghamisi, and D. P. Kreil (2021) CDCEO’21 – First Workshop on Complex Data Challenges in Earth Observation. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 4878–4879, 2021-10-26. (more) (download)
A. Fürst, E. Rumetshofer, V. Tran, H. Ramsauer, F. Tang, J. Lehner, D. Kreil, M. Kopp, G. Klambauer, A. Bitto-Nemling, and S. Hochreiter (2021) CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. arXiv:2110.11316, 2021-10-21. (more) (download)
M. Kopp, D. Kreil, M. Neun, D. Jonietz, H. Martin, P. Herruzo, A. Gruca, A. Soleymani, F. Wu, Y. Liu, J. Xu, J. Zhang, J. Santokhi, A. Bojesomo, H. Al Marzouqi, P. Liatsis, P. H. Kwok, Q. Qi, and S. Hochreiter (2021) Traffic4cast at NeurIPS 2020 – Yet More on the Unreasonable Effectiveness of Gridded Geo-Spatial Processes. Proceedings of the NeurIPS 2020 Competition and Demonstration Track, PMLR, 133, 325-343, 2021. (more) (download)
T. Adler, J. Brandstetter, M. Widrich, A. Mayr, D. Kreil, M. Kopp, G. Klambauer, and S. Hochreiter (2020) Cross-Domain Few-Shot Learning by Representation Fusion. arXiv:2010.06498, 2020-10-13. (more) (download)
D. P. Kreil, M. K. Kopp, D. Jonietz, M. Neun, A. Gruca, P. Herruzo, H. Martin, A. Soleymani, and S. Hochreiter (2020) The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task – Insights from the IARAI Traffic4cast Competition at NeurIPS 2019. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR, 123, 232-241, 2020-08-19. (more) (download)