Greifswald Artificial INtelligence Group
GAIN Group Members
Currently, the GAIN-group consists of group leader
,
Ph.D. students
,
,
,
,
and
as well as student assistants.
Prof. Dr. rer. nat. Josephine Thomas
Josephine is the leader of the GAIN group and professor for machine Learning.
She is a trained physicist and gained her doctorate with a thesis on non-linear
dimension reduction applied to a topic of complex network theory.
She choose to work in machine learning because she likes to
do basic reseach but also see it go to a useful application. Specifically GNNs and PINNs enable her to
work in many application domains as well as theoretical research.
Areas of Interest:
Graphs, graph neural networks, network geometry, physics-inspired machine learning, astrophysics, renewable energies, the power grid.
E-mail:
Phone:
+49 3834 420 5510
PGP-key:
Download here
M.Sc. Silvia Beddar-Wiesing
Silvia is working towards her Ph.D. in the GAIN group with Dr. Josephine Thomas and Prof. Dr.
Bernhard Sick as her supervisors.
She graduated with a B.Sc. in Mathematics and a M.Sc. in Computer Science with a
specialization in Computational Intelligence and Data Analytics at the University of Kassel.
Her research topic addresses the embedding of structural-dynamic graphs using Neural
Networks. The focus is on embeddings that transform graphs with changing nodes and edges over time,
i.e.,
including additions and deletions of nodes and edges. In particular, the goal is to design a model based on Temporal Point Processes
that represents structural-dynamics in graphs as density function over time and, thus, provides an event prediction procedure.
Furthermore, Silvia is interested in comparing dynamic graphs and analyzing the expressivity of GNNs using the Weisfeiler-Lehman test.
Areas of Interest:
Pattern Recognition in Temporal and Structural Data, Expressivity of Neural Networks, Similarity Measures,
Data/Information/Knowledge Fusion.
E-mail:
Phone:
+49 561 804 6454
PGP-key:
Download here
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M.Sc. Alice Moallemy-Oureh
I am Alice and I am currently in the third year of my doctorate in the GAIN Group. Prior, I graduated
with a M.Sc. in Mathematics and its applications to Computer Science from the university of Kassel. My
specialisation in Mathematics lies in Algorithmic Commutative Computer Algebra and Geometry. In Computer
Science, I focused in Logic and Data Analytics.
I am particularly interested in developing Graph Neural Networks, which are designed to master the
handling of attribute dynamic graphs in continuous time representation. The first thing I would like to
focus on is the so-called node embedding of such graphs, as this can be used to keep almost unlimited
application possibilities open. The plan is to create more efficient wind power forecasts with the help
of such a model.
In general, I am a very open, honest, and enthusiastic person who likes to meet new people and is up for
any discussion - so don't be afraid to chat me up.
Areas of Interest:
Geometric Deep Learning, Machine Learning, Pattern Recognition, Commutative Computeralgebra,
Geometry, IT-security.
E-mail:
Phone:
+49 561 804 6363
PGP-key:
Download here
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M.Sc. Clara Holzhüter
I joined the GAIN group in 2022 after I graduated with B.Sc. and M.Sc. in Computer Science at the
University of Göttingen. During my studies, I specialized in Computational Neuroscience,
especially in Deep Learning and Computer Vision.
The research area of my Ph.D. in the GAIN
group is the application of GNNs in power grid applications. Due to the inherent graph structure of
electricity networks, GNNs are promising methods with regard to representation learning and grid
prediction tasks. My goal is to develop GNNs for grid calculations that make use of the graph
properties of grids including their topology.
As a Ph.D. student at the Fraunhofer IEE my
research also addresses the application of such methods to real world problems, particularly
in the field of congestion management and load flow approximation.
Areas of Interest:
Graph Neural Networks, Deep Learning, Reinforcement Learning, Power Grids, Machine Learning, Graph Theory, AC Load Flow.
E-mail (Uni):
E-mail (Fraunhofer):
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M.Sc. Pascal Plettenberg
I joined the GAIN group in 2024. I graduated with an M. Sc. in Physics from the
University of Kassel and during my studies, I specialized on Machine Learning applications in Theoretical
Physics and Materials Science.
Currently, I am aiming for my Ph.D. and my research topic is the application of GNNs to the design
and optimization of Printed Circuit Board (PCB) schematics. Electronic circuits can be naturally represented
as graphs and therefore, GNNs are a promising method for optimizing them in order to obtain electronic
devices that are more efficient and reliable. For this purpose, my goal is to combine GNNs with other
approaches, such as Active Learning and Self-Supervised Learning.
Areas of Interest:
Deep Learning, Graph Neural Networks, Computer Vision, Active Learning, Electronic Design Automation,
Materials Science, Quantum Physics.
E-mail:
PGP-key:
Download here
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M.Sc. Anna Hiemenz
I joined the GAIN group in 2025 after graduating with B.Sc. and M.Sc.
in Computer Science at the University of Oldenburg.
Right now I am in the early stages of my PhD and researching about current
GNN models and their theoretical foundations as well as open problems and challenges like
Oversmoothing/-squashing, Expressivity, and Explainability.
Areas of Interest:
Graph Neural Networks, Deep Learning, Machine Learning,
Geometric deep learning, Graph and GNN theory, applications and challenges of GNNS.
E-mail:
M.Sc. Enya Blohm-Sievers
Enya has joined the GAIN group in 2025 in order to work on Physics-Informed Neural Networks (PINNs).
She holds an MSc in Physics and has studied Philosophy Ms.C., with a strong focus on theoretical physics —
particulartly problems in gravitational theories and their philosophical foundations.
She is now dedicated to applying machine learning to solve complex physical differential equations.
Areas of Interest:
Deep Learning, Physics Informed Neural Networks, Geometry, General Relativity, Quantum Gravity, Philosophy of Physics.
E-mail: