and
Neural Networks
GAIN Group Members
Currently, the GAIN-group consists of group leader, Ph.D. students,andas well as student assistants,,and.
In addition,Prof.
Dr. Bernhard Sick, head of the Intelligent Embedded Systems department of the University of Kassel, is our mentor. And,
senior scientist in Machine Learning and group leader at Fraunhofer IEE, is project leader at our project partner
Fraunhofer IEE.
If you are interested in joining us, feel free tocontactus.
Dr. rer. nat. Josephine Thomas
Josephine is the leader of the junior research group GAIN.
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 on Machine Learning and specifically on GNNs because she likes to
do basic reseach but also see it go to a useful application quickly
and because graphs are just cool: they can describe the formation of the universe as well as the
internet, what more can a scientist want?
Her focus in GAIN next to supervising PhD students is the explainability of GNN algorithms.
Areas of Interest:
Graphs, deep learning, network geometry, quantum physics, renewable energies.
E-mail:
Phone:
+49 561 804 6061
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 smooth embeddings 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 obtain a model
that processes structural-dynamic graphs of different time granularity and that represents them
smoothly in a latent space.
Areas of Interest:
Pattern Recognition, Machine Learning, Graph Theory, Combinatorial Optimization, Time Series Analysis,
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 first 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 have 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 the power grid.
Due to the inherent graph structure of electricity networks, GNNs are promising methods with regard to
representation learning and information extraction for different use cases. My goal is to develop GNNs
which enhance the efficiency and safety of electric networks by directly making use of their topology.
For this purpose, I aim to combine Reinforcement Learning with GNNs to learn to control the power grid
with regard to its topology. The field of graphs which change their topology or attributes over time is
of particular interest for me, since power grids are highly dynamic systems.
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 renewable energies.
Areas of Interest:
Graph Neural Networks, Deep Learning, Computer Vision, Machine Learning, Graph Theory, Computational
Neuroscience, Renewable Energies
E-mail (Uni):
PGP-key (Uni):
Download here
E-mail (Fraunhofer):
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