NHR Conference '25

> AI in Social Sciences

Social sciences are increasingly adopting AI and HPC to analyze societal dynamics in greater depth. Today, researchers employ detailed simulations, natural language processing, and agent-based models to explore phenomena ranging from public sentiment to electoral dynamics. These techniques streamline the analysis of extensive datasets and enable detailed simulations that uncover subtle patterns and spark fresh research.

This section brings together researchers working at the intersection of AI, HPC, and the social sciences. Alongside technical and methodological advances, we will also explore critical challenges and open up new ways of tracing social phenomena. We welcome contributions that explore such new avenues.

> Life Sciences

As the life sciences are moving rapidly from a descriptive discipline to a predictive one, researchers have to integrate vast amounts of heterogeneous data and develop highly nonlinear, multi-scale models. Data sources in the life sciences are diverse and include genome and proteome sequences, metabolic data, microscopy images and videos, timeseries, as well as clinical records and medical imagery. Interpreting nonlinear data in high-dimensional spaces is challenging for the human brain. 

Distilling physical laws or mathemtaical models from the data therefore requires scalable computational statistics and machine learning approaches. This entire workflow from handling and fusing data to developing and training models requires innovative HPC approaches to ensure scalability, flexibility, performance while maintaining fast development cycles. Abstract submissions for talks or posters within the described field are welcome.

> Data Management & Storage

Efficient data management and storage are crucial for HPC applications and systems to ensure seamless access, processing, and analysis of vast datasets while optimizing performance and resource utilization. Scalable storage solutions, parallel file systems, and data lifecycle management are key to handling large-scale simulations, AI workloads and the integration of analysis pipelines from large-scale research infrastructures and experiments.

We invite talks and posters on innovative storage architectures, data-intensive workflows, optimization strategies, FAIR data principles, and best practices in HPC data management. Contributions on real-world applications, emerging storage technologies, and interdisciplinary collaborations are highly encouraged to foster knowledge exchange and innovation in HPC.

September 22-23, 2025	Scientific Conference Early Bird Price: 100 EUR, 200 EUR for industry representatives until July 13, 2025  Regular Price: 120 EUR, 240 EUR for industry representatives Registration Deadline: September 07, 2025
September 24-25, 2025	NHR Networking Event This event is an opportunity for all NHR employees of all centers, NHR Graduate School Fellows and those involved in NHR working groups/projects to exchange ideas and progress their projects. This event is free of charge. Registration Deadline: September 07, 2025
ATTENTION UNDERGRADUATE STUDENTS Are you interested in attending the NHR conference in Göttingen? But you don't have the means to pay the fee? We will waive the conference fee for a limited number of students. Write us a short statement (max. 200 words) about your background and your motivation to attend the NHR conference. Please send us a pdf file to conference@nhr-verein.de   +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ FOOD RESTRICTIONS, ALLERGIES, FOOD INTOLERANCES Catering will be provided during the conference. Should any attendees follow special food restrictions or suffer from allergies and/or food intolerances, they are kindly requested to send an email to conference@nhr-verein.de, with the subject line "Catering", so that this information can be conveyed to the caterer.   +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

Abstract Submission | Poster/Contributed Talk 

Deadline: May 11, 2025  

Use your chance to present and discuss your work with experts in your field and submit an abstract for a poster or contributed talk.

Abstract submission | Workshop and Network Meeting 

Deadline: May 11, 2025 

Do you want to organize a working group meeting or project team meeting at the NHR Networking Event? Share your idea with us and we will arrange a time slot for your meeting.

Sandra Hummel |  AI in Social Sciences
 Sandra Hummel is an educational scientist who heads the junior research group “Situating AI-based Mentoring at the Center for Scalable Data  Analytics and Artificial Intelligence (ScaDS.AI)” at TU Dresden. Her research combines teaching-learning research, educational research on AI and  higher education didactics, with a special focus on adaptive, individualized educational technologies. A central focus of her work is on human-cent  red AI models that integrate didactic and epistemological principles into the design of learning environments.”
 

Sebastian Pokutta |  AI in Social Sciences
Sebastian Pokutta is the Vice President of the Zuse Institute Berlin (ZIB) and a Professor of Mathematics at TU Berlin with a research focus on Artificial  Intelligence and Optimization. He is also a Chair of the Cluster of Excellence MATH+ and a Chair of the Research Campus MODAL. His research is si  tuated at the intersection of Artificial Intelligence and Optimization, combining Machine Learning with Optimization techniques as well as the Theory  of Extended Formulations, exploring the limits of computation in alternative models of complexity. 

Alexander Ecker | Life Sciences 
Alexander Ecker is a professor of Data Science at the University of Göttingen and a Max Planck Fellow at the MPI for Dynamics and Self-Organization  in Göttingen. His research focuses on how neural systems perform visual perception. Working at the intersection of machine learning and neurosc  ience, he studies both biological vision and computer vision. His interdisciplinary approach integrates machine learning, computer vision, behavioral  studies, and neuronal population recordings in the brain.

 
 

Ivo Sbalzarini | Life Sciences
 Ivo Sbalzarini holds the Chair of Scientific Computing for Systems Biology at the Institute of Artificial Intelligence at TU Dresden. He is also a tenu  red Senior Research Group Leader with the Max Planck Institute of Molecular Cell Biology and Genetics, and Director of the Center for Systems Bio  logy Dresden (CSBD). His research focuses on developing, applying, and teaching particle methods for image-based computational biology. This in  cludes particle methods for multi-scale simulations, data-driven modeling, bioinspired and robust learning, and parallel high-performance compu  ting for particle methods. Current applications revolve around the topic of Systems Biology of Development.

Oliver Knodel | Data Management & Storage
Oliver Knodel is a data scientist with a background in research at university and large-scale research infrastructures.  His has expertise in data sci  ence, data management and publication, heterogenous hardware architectures, FPGA prototyping and big data. Oliver Knodel heads the ‘Scientific  Data Management’ group at the Helmholtz-Zentrum Dresden-Rossendorf and is a member of the EOSC node PaNOSC and the Mu2e collaboration  at Fermilab. He is an experienced engineer with a PhD in computer science from the Technical University of Dresden.

Sarah Neuwirth | Data Management & Storage
Sarah Neuwirth is a Full Professor of Computer Science and Chair of the High Performance Computing and its Applications research group at Johan  nes Gutenberg University Mainz (JGU). She also serves as Co-Director of the NHR@SW HPC Center. Her research interests include parallel file and  storage systems, performance engineering, parallel I/O, reproducible benchmarking, performance portability, and parallel programming models. She  has been actively involved as a co-PI in numerous research collaborations, including partnerships with the Jülich Supercomputing Centre (DEEP Pro  ject Series, EUPEX), Lawrence Livermore National Laboratory (LLNL), BITS Pilani Goa Campus, Oak Ridge National Laboratory (ORNL), and Virginia  Tech.