Here you find the list of talks in the MODUS Seminar from past semesters. The list goes back to 2023, for earlier talks please see the MODUS elearning course.
Summer Semester 2023
Chair of Biochemistry IV - Biophysical Chemistry, Universität Bayreuth
AI-driven revolutions in structural biology: a new dawn for biomolecular NMR spectroscopy
Chair of Nutritional Biochemistry, Universität Bayreuth
Challenges in histological tissue analysis
Frankfurt School of Finance & Management
Bounded Confidence Revisited
Data-based prediction of dynamical (control) systems
Rice University, Houston, USA
Interpolatory methods for model reduction and the Loewner framework
Lehrstuhl für Data Systems, Universität Bayreuth
Recent Advances in Graph Partitioning for Increasing the Performance of Large-Scale Distributed Graph Processing
Abstract: Graph-structured data is found in various domains such as social networks, websites, and recommendation networks. To analyze large graphs and gain high-level insights, distributed graph processing frameworks such as Spark/GraphX and Giraph have been established. For distributed processing, the graph needs to be split into multiple partitions, while the cut size and balancing of the partitions need to be optimized. This problem is known as graph partitioning.
In this talk, I will summarize recent advances of graph partitioning and introduce important new concepts that have been developed in my group. First, two novel techniques that reduce the memory footprint of graph partitioning while maintaining a high partitioning quality: Hybrid Edge Partitioning and Two-Phase Streaming. Second, EASE, a framework for optimizing the choice of partitioning technique for a given graph and processing algorithm. EASE is based on machine learning and achieves better performance than a manual partitioner selection based on heuristics. Finally, I will provide an outlook on open problems.
AG Künstliche Intelligenz in der Spektroskopie und Mikroskopie, Universität Bayreuth
AI for spectroscopy and microscopy: inverse modelling and data modelling tasks
Lehrstuhl Theoretische Physik I, Universität Bayreuth
Insights into turbulence from fully resolved simulations
Abstract: Fluid turbulence plays an important role in nature and engineering processes. Despite its importance, many aspects still remain to be understood. From a physics perspective, one challenge is to derive theories of turbulence which allow us to understand and predict nontrivial statistical features of turbulence such as the frequent occurrence of extreme events. Fully resolved turbulence simulations provide a useful framework to investigate the spatio-temporal properties of turbulence. In this presentation, I will discuss some recent works which demonstrate how theoretical modeling and simulations can be combined to better understand fundamental aspects of turbulence.