Model-driven Machine Learning
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Group members

David Greenberg
Group Leader

David leads the M-DML group. Before moving to Earth science, he did a Postdoc in ML and a PhD in computational neuroscience. His primary research goal is applying machine learning to address critical computational problems in Earth science, such as predictability, parameter tuning, parameterization, uncertainty quantification and data assimilation.
Tobias Machnitzki
PhD Student

Tobias started his PhD in the m-dml group in August 2020, after finishing his Masters in Meteorology at the University of Hamburg. He works on conditional generative adversarial networks with the intention to use their output diversity for estimating uncertainties in weather prediction tasks.
Shivani Sharma
PhD Student

Shivani completed her master's in Atmospheric Science from Indian Institute of Technology, Delhi. She's interested in using machine learning for parametrizations in atmospheric models to improve the representation of sub-grid scale processes and generate more accurate forecasts.
Vadim Zinchenko
PhD Student

Vadim's research focuses on the intersection of machine learning and Earth system modelling. He develops algorithms to combine of data-driven and numerical modelling. He's particulary interested in developing machine learning frameworks that can be applied for inverse problems in the geosciences, such as data assimilation, parameter tuning and process learning from noisy and incomplete observations. He is a participant in the CoastalFutures project of the German Alliance for Marine Research.
Yunfei Huang

Yunfei works on data-driven modeling for the Earth system. He is interested in the uncertainty quantification using deep learning, Bayesian approaches, and information theory. Previously, he obtained PhD in biophysics at the university of Cologne on advanced data analysis for traction force microscopy and data-driven discovery of physical equations.
Naveen Parameswaran
PhD exchange student

Naveen's research revolves around prediction tasks in the marine geosciences. He focuses on using machine learning methods to obtain a global prediction map of sedimentation rates in the seafloor. As a PhD student at GEOMAR in Kiel, he is currently visitng our group on a fellowship funded by the HIDA trainee network.
Andrey Vlasenko

Andrey is developing ML methods to effectively replace computationally intensive atmospheric chemistry calculations in complex climate systems. His position is funded by the Helmholtz AI project NACHMO. His interests include ocean-atmosphere systems, data assimilation, artificial intelligence, atmospheric chemistry, and algorithmic differentiation.


Marcel Nonnenmacher

Marcel worked on data-driven weather prediction. He's interested in representing prediction uncertainty through probability distributions. During his PhD in computational neuroscience, he worked on probabilistic modeling for incomplete data and black-box Bayesian inference. He is now a postdoc at UCL's Gatsby unit in London.
Kubilay Demir
Masters Student

Kubilay was enrolled in the MSc. program in Ocean and Climate Physics at the University of Hamburg. His research, carried out jointly with Kai Logemann at Hereon, applied the principle of Physics Informed Neural Networks to oceanographic problems. He is now a PhD student at Hereon's Institute of Coastal research, working on nutrient cycle in ocean ecosystem models.