Maxim Samarin

Maxim Samarin

Senior Data Scientist
Academia
(Alumni)

Maxim joined the SDSC as a Senior Data Scientist in September 2023. He is interested in developing practical Machine Learning tools and deriving actionable insights for complex real-world challenges. He has worked on a variety of topics in generative AI, computer vision, and deep learning theory, with practical applications in different areas like object detection in environmental science or data-driven discovery of novel molecules in chemistry. Maxim holds a Ph.D. in computer science from the University of Basel and a B.Sc. as well as an M.Sc. in physics from ETH Zurich.

Projects

MedCare

In Progress
Detecting novel drug combinations associated with adverse events
Biomedical Data Science

SPEED2ZERO

In Progress
Sustainable pathways towards net zero Switzerland
Energy, Climate & Environment

DeepDown

Completed
Multivariate climate downscaling using deep learning models
Energy, Climate & Environment

Publications

Samarin, M.; Horton, P.; Otero, N.; Allen, S.; Volpi, M. "Assessment of Local Climate Impact through Multivariate Downscaling Using Deep Learning" EXCLAIM Symposium 2025 View publication
Horton, P.; Samarin, M.; Otero, N.; Allen, S.; Volpi, M. "Multivariate climate downscaling using deep learning models" EGU General Assembly 2025 View publication
Schillinger, M.; Shen, X.; Samarin, M.; Meinshausen, N. "Multivariate Generative Downscaling of Climate Simulation Data with Proper Scoring Rules" EXCLAIM Symposium 2025 View publication
Goshtasbpour, S.; Samarin, M.; Volpi, M. "Learning Extreme Temperature Regimes" ICLR 2025 Workshop on Tackling Climate Change with Machine Learning 2025 View publication
Schillinger, M.; Shen, X.; Samarin, M.; Meinshausen, N. "Machine Learning for Multivariate Downscaling: A Generative Model Inspired by Forecast Evaluation" EGU General Assembly 2024 View publication
Schillinger, M.; Shen, X.; Samarin, M.; Meinshausen, N. "Generative Modelling for Multivariate Downscaling via Proper Scoring Rules" International Meeting on Statistical Climatology 2024 View publication

Mentioned in

Case Studies

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