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.
	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 Postdoc 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.
	Andrey Vlasenko Postdoc 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.
	Minh Nguyen Postdoc Minh's research focuses on differentiable physics approaches for ocean modelling. Using a combination of numerical methods and data-driven ML, he develops techniques for gradient-based optimization in realistic ocean models, and in particular for NEMO. Applications include data assimiliation, parameter tuning and parametrization learning. He is funded by EDITO ModelLab, an EU project aiming to create digital twins of the ocean.
	Ali Can Bekar Postdoc Ali's work revolves around using machine learning to enhance numerical simulations. He's particularly interested in applied mathematics, fluid mechanics, and scientific computing. Lately, he's been delving into the simulations of mantle convection in Mars and Mercury.
	Nishant Kumar Postdoc Nishant’s research focuses on advancing generative AI methodologies to address complex challenges in computer vision. His work also involves applying invertible neural networks to solve inverse problems. Currently, he is interested in leveraging his machine learning expertise to improve climate and earth system modeling. His full profile is available on his personal homepage. He is supported by the HClimRep project, a Helmholtz Foundation Model Initiative for developing cutting-edge AI foundation models for climate research.

Alumni

	Marcel Nonnenmacher Postdoc 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.
	Tobias Schanz 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 worked on conditional density estimation with an emphasis on ensemble methods, and self-supervised learning for aquatic life imaging. He now works as an AI engineer at iits-consulting.
	Kubilay Demir Masters Student Kubilay was enrolled in the masters 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 cycles in ocean ecosystem models.
	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 visited our group on a fellowship funded by the HIDA trainee network.