My work lies at the intersection of machine learning, AI, and applied sciences. Recently, I worked on ML&AI for patient classification, early disease diagnosis and prognosis at Volv Global, an AI & healthcare startup. From 2017 to 2022 I was a Senior Data Scientist at the Swiss Data Science Center, a joint center between EPFL and ETH Zürich, where I worked on various machine learning problems applied to climate science. Previously, I was a postdoctoral researcher at the Courant Institute of Mathematical Sciences, New York University, working on machine learning for dynamical systems, time series, and atmosphere ocean science. As a postdoctoral researcher at the University of Montpellier and INRIA I worked on anomaly detection in biomedical data with application to cancer and aneurysm detection. I obtained my PhD from the University of Geneva on machine learning and pattern recognition.

Fields of interest: machine learning, AI, dynamical systems, causal inference, applied science

Activities: Climate Informatics Conference (Steering committee), Environmental Data Science (Editor)

Contact

Email: szekely (dot) eni (at) gmail (dot) com

Publications

Pre-prints

E. Székely, S. Sippel, N. Meinshausen, G. Obozinski, R. Knutti. Robust detection and attribution of climate change under interventions. arxiv:2212.04905

S. Das, D. Giannakis, E. Székely. An information-geometric approach to feature extraction and moment reconstruction in dynamical systems. arXiv:2004.02172

Journals and book chapters

R. de Fondeville, Z. Wu, E. Székely, G. Obozinski, D.I.V. Domeisen (2023). Improved extended-range prediction of persistent stratospheric perturbations using machine learning. Weather and Climate Dynamics, 4(2), 287-307, doi.org/10.5194/wcd-4-287-2023

Z. Wu, T. Beucler, E. Székely , W.T. Ball, D.I.V. Domeisen (2022). Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable neural networks. Environmental Data Science, 1, e17. doi:10.1017/eds.2022.19

J. Cortés-Andrés, G. Camps-Valls, S. Sippel, E. Székely, D. Sejdinovic, E. Diaz, A. Pérez-Suay, Z. Li, M. Mahecha, M. Reichstein (2022). Physics-aware nonparametric regression models for Earth data analysis. Environmental Research Letters, 17(5), doi.org/10.1088/1748-9326/ac6762/pdf

S. Sippel, N. Meinshausen, E. Székely, E. Fischer, A.G. Pendergrass, F. Lehner, R. Knutti (2021). Robust detection of forced warming in the presence of potentially large climate variability. Science Advances, 7(43), eabh4429, doi/10.1126/sciadv.abh4429

Z. Wu, B. Jiménez-Esteve, R. de Fondeville, E. Székely, G. Obozinski, W.T. Ball, D. Domeisen (2021). Emergence of representative signals for sudden stratospheric warmings beyond current predictable lead times. Weather and Climate Dynamics, 2, 841-865, doi.org/10.5194/wcd-2-841-2021

S. Sippel, N. Meinshausen, E. M. Fischer, E. Székely, R. Knutti (2020). Climate change now detectable from any single day of weather at global scale. Nature Climate Change, 10, 35-41, doi:10.1038/s41558-019-0666-7. See also News & Views and 10 years of Nature Climate Change

R. Wüest et al. (2019). Macroecology in the age of big data - Where to go from here? Journal of Biogeography, 1-12, doi.org/10.1111/jbi.13633

R. Alexander, Z. Zhao, E. Székely, D. Giannakis (2017). Kernel analog forecasting of tropical intraseasonal oscillations. Journal of the Atmospheric Sciences, 74, 1321-1342, doi.org/10.1175/JAS-D-16-0147.1

E. Székely, D. Giannakis, A. J. Majda (2016). Extraction and predictability of coherent intraseasonal signals in infrared brightness temperature data. Climate Dynamics, Springer, 46(5), 1473-1502, doi.org/10.1007/s00382-015-2658-2

E. Székely, D. Giannakis, A. J. Majda (2016). Initiation and termination of intraseasonal oscillations in nonlinear Laplacian spectral analysis-based indices. Mathematics of Climate and Weather Forecasting, Special issue on Stochasticity and Organization of Tropical Convection, 2, 1-25, doi.org/10.1515/mcwf-2016-0001

E. Székely, D. Giannakis, A. J. Majda (2015). Kernel and information-theoretic methods for the extraction and predictability of organized tropical convection. Machine Learning and Data Mining Approaches to Climate Science, Springer, 147-159

E. Székely, A. Sallaberry, F. Zaidi, P. Poncelet (2015). A graph-based method for detecting rare events: Identifying pathologic cells. IEEE Computer Graphics and Applications, 35(3), 65-73

E. Székely, E. Bruno, S. Marchand-Maillet (2011). Unsupervised quadratic discriminant embeddings using Gaussian mixture models. In Communications in Computer and Information Science, Vol. 128, Knowledge Discovery, Knowledge Engineering and Knowledge Management, Springer-Verlag, 128, 107-120

S. Marchand-Maillet, D. Morrison, E. Székely, J. Kludas, M. von Wyl, E. Bruno (2011). Mining Networked Media Collections, Springer, 6535, 1-11

S. Marchand-Maillet, D. Morrison, E. Székely, E. Bruno (2010). Interactive representations of multimodal databases. In J. -P. Thiran, H. Bourlard and F. Marques (Eds.), Academic Press, 279-307

Conferences and workshops

E. Székely, S. Sippel, R. Knutti, G. Obozinski, N. Meinshausen. A direct approach to detection and attribution of climate change. Proceedings of the 9th International Workshop on Climate Informatics: CI2019 (No. NCAR/TN-561+PROC), 119-124 (2019) doi:10.5065/y82j-f154

E. Székely, D. Giannakis. Pattern extraction in dynamical systems using information geometry: application to tropical intraseasonal oscillations. Climate Informatics Workshop, Boulder, USA (2017)

J. Slawinska, E. Székely, D. Giannakis. Data-driven Koopman analysis of tropical climate space-time variability. Workshop on Mining Big Data in Climate and Environment, 17th SIAM International Conference on Data Mining (SDM), Houston, USA (2017)

E. Székely, D. Giannakis, A. J. Majda. Nonlinear Madden-Julian oscillation indices using kernel methods. Proceedings of the Fifth International Workshop on Climate Informatics: CI 2015. J. G. Dy, J. Emile-Geay, V. Lakshmanan, Y. Liu (Eds.) (2015)

E. Székely, D. Giannakis, A. J. Majda. Extraction and predictability of coherent intraseasonal signals in infrared brightness temperature data. Climate Informatics Workshop, Boulder, USA (2014)

E. Székely, P. Poncelet, F. Masseglia, M. Teisseire, R. Cezar. A density-based backward approach to isolate rare events in large-scale applications. Proceedings of the 16th International Conference on Discovery Science, Singapore, Republic of Singapore, 8140, pp. 249-264 (2013)

E. Székely, E. Bruno, S. Marchand-Maillet. High-dimensional multimodal distribution embedding. Workshop on Visual Analytics and Knowledge Discovery (VAKD’10) at the IEEE International Conference on Data Mining (ICDM), Sydney, Australia, pp. 434-441 (2010)

E. Székely, E. Bruno, S. Marchand-Maillet. Distance transformation for effective dimension reduction of high-dimensional data. International Workshop on Topological Learning, Prague, Czech Republic (2009)

E. Székely, E. Bruno, S. Marchand-Maillet. Unsupervised discriminant embedding in cluster spaces. International Conference on Knowledge Discovery and Information Retrieval (KDIR’09), Madeira, Portugal (2009)

S. Marchand-Maillet, E. Székely, E. Bruno. Optimizing Strategies for the Exploration of Social-Networks and Associated Data Collections. Proceedings of the International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS’09) - Special session on “People, Pixels, Peers: Interactive Content in Social Networks”, London, UK (2009)

E. Székely, E. Bruno, S. Marchand-Maillet. Clustered multidimensional scaling for exploration in information retrieval. International Conference on the Theory of Information Retrieval (ICTIR’07), Budapest, Hungary (2007)