Internal Meeting:

Poster Event

Friday, 29.04.2022 · 9:30 a.m. - 6:00 p.m.

Our first poster event took place on-site at the Super C, RWTH Aachen. The event was created to enable knowledge exchange and to build cooperation between the doctoral researchers.

Two talks were given by doctoral researchers:

  • Timo Stomberg - Exploring Wilderness Using Explainable Machine Learning in Satellite Imagery
  • Daniel Wolff - Physics-Informed Neural Networks as Reduced Simulation Models for Engineering Applications

Several posters were presented by doctoral researchers:

  • Alaukik Saxena - A materials informatics framework to discover patterns in atom probe tomography data
  • Alessio Quercia - Sample importance to learn a given task
  • Anna Lara Simson - Real-time ice characterization with autonomous melting robot
  • Ann-Kathrin Edrich - A modular and scalable workflow for data-driven modelling of shallow landslide susceptibility
  • Aytekin Demirci - Data Mining and Machine Learning of Dislocation Systems
  • Bamidele Oloruntoba - High Resolution Land Surface Modelling over Africa: the role of spatial and temporal resolution
  • Cristiano Köhler - Tracking the provenance of electrophysiology data analysis results
  • Danimir Doncevic - Optimal Reduced-Order Models for Energy System Optimization
  • Dwaipayan Chatterjee - Understanding cloud systems structure and organization using a machine’s self-learning capability
  • Eike Cramer - Conditional Normalizing Flows for Data-Driven Scenario Generation
  • Emile de Bruyn - Finding Features in 4D Distributions of Ions to Speed Up Drug Discovery
  • Felix Terhag - Parameter-Free Uncertainty Estimates for Volume Predictions in Heart MRIs
  • Giuliano Santarpia - RepOdor: A Manually-Curated, Comprehensive Repository of Odorants and their OlfactoryReceptors
  • Ingo Steldermann - Moment Approximations for Shallow Flow
  • Jan Rittig - Computer-Aided Molecular Design with Graph Machine Learning
  • Jazib Hassan - Hybrid Process Modelling Combining Mechanistic Equations with Machine Learning
  • Johann Fredrik Jadebeck - Madness of Crowds or Sanity of Herds? Rational Uncertainty Quantification for 13C Metabolic Flux Analysis
  • Johannes Kruse - Explainable AI for Power Grid Stability and Control
  • Johannes Seiffarth - Automated Quantitative Analysis for Microbial Live-Cell Imaging
  • Johannes Wasmer - Boosting Simulations of Quantum Materials with Machine Learning
  • Jorge Guzmán - Hybrid models for cellular signalling: meso-scale pathway identification
  • Karel van der Weg - Improved classification of protein function by a localized 3D protein descriptor and deep learning
  • Karina Ruzaeva - A hybrid multi-object segmentation framework with model-based B-splines for microbial single cell analysis
  • Laura Helleckes - Bayesian Optimisation Meets Robotic Workflows: Data-Efficient Characterisation of Bacterial Strains Using Thompson Sampling
  • Leonardo Boledi - Computational multi-physics modeling to predict the performance of melting probes in ice
  • Lisa Beumer - Verification from Space – Building Transparency and Confidence through Earth Observation Big Data
  • Marcel Zimmer -Entangled Gaussian Processes for Digital Twins of Power Systems
  • Mario Rüttgers - Machine learning in flow simulations
  • Maximilian Siska - Towards Automatic Experimentation and Discovery in Bioprocess Development
  • Sonja Germscheid - Stochastic scheduling optimization of industrial processes under electricity price uncertainty
  • Sophia Wiechert - Efficient Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks
  • Stephan Malzacher - BioCatHub: Research data management based on the FAIR data principles in Biocatalysis
  • Viktor Grimm - Prediction of flow with Neural Networks - A Physics-Aware Approach for 2D-Flow