Emerging Fields

The Emerging Fields are devoted to pioneering interdisciplinary research in new fields including the social sciences and humanities. The following list displays the research projects presently running in the five Emerging Fields:


Extracting Dynamical Laws from Complex Data (EF1)
Scientists in Charge: Jens Eisert, Gitta Kutyniok, Klaus-Robert Müller

  • EF1-1: Quantifying Uncertainties in Explainable AI
                Gitta Kutyniok, Klaus-Robert Müller, Wojciech Samek
  • EF1-2: Quantum Kinetics
                 Klaus-Robert Müller, Frank Noé
  • EF1-3: Approximate Convex Hulls With Bounded Complexity
                Michael Joswig, Klaus-Robert Müller
  • EF1-4: Extracting Dynamical Laws by Deep Neural Networks: A Theoretical Perspective
                Gitta Kutyniok, Frank Noé, Barbara Zwicknagl
  • EF1-5: On robustness of deep neural networks
                Christian Bayer, Peter Karl Friz
  • EF1-6: Graph Embedding for Analyzing the Microbiome
                Tim Conrad, Stefan Klus, Gregoire Montavon
  • EF1-7: Quantum machine learning
                Jens Eisert, Klaus-Robert Müller


Digital Shapes (EF2)
Scientists in Charge: Alexander Bobenko, Hans-Christian Hege

  • EF2-1: Smooth discrete surfaces
                Alexander Bobenko, Andrew Sageman-Furnas
  • EF2-2: The structure within disordered cellular materials
                 Myfanwy Evans, Francisco Garcia-Moreno, Frank Lutz, John Sullivan
  • EF2-3: Spline Models for Shape Trajectory Analysis
                Hans-Christian Hege, Christoph von Tycowicz, Anja Hennemuth, Friederike Fless
  • EF2-4: Conforming Regular Triangulations
                Marc Alexa, Hang Si, Boris Springborn


Model-based Imaging (EF3)
Scientists in Charge: Michael Hintermüller, Vladimir Spokoiny

  • EF3-1: Model-based geometry reconstruction from TEM images
                Thomas Koprucki, Karsten Tabelow
  • EF3-2: Model-based 4D reconstruction of subcellular structures
                Peter Hiesinger, Max von Kleist, Steffen Prohaska, Martin Weiser
  • EF3-3: Optimal Transport for Imaging
                Michael Hintermüller, Vladimir Spokoiny, Pavel Dvurechenskii
  • EF3-4: Physics-regularized learning
                Carsten Gräser, Ralf Kornhuber, Christof Schütte
  • EF3-5: Direct Reconstruction of Biophysical Parameters Using Dictionary Learning and Robust Regularization
                Michael Hintermüller, Tobias Schäffter


Particles and Agents (EF4)
Scientists in Charge: Peter Karl Friz, Wolfgang König

  • EF4-1: Influence of mobility on connectivity
                Benedikt Jahnel, Wolfgang König
  • EF4-2: Understanding tipping and other dynamical transitions in systems containing human agents
                Jobst Heitzig, Péter Koltai, Jürgen Kurths, Christof Schütte
  • EF4-3: Learning reduced models for large-scale mobility ABMs
                Carlo Jaeger, Stefan Klus, Christof Schütte, Sarah Wolf
  • EF4-4: Diffusion in dynamically crowded cells: stochastic models from spatiotemporal motion data
                Felix Höfling, Carsten Hartmann


Concepts of Change in Historical Processes (EF5)
Scientists in Charge: Friederike Fless, Rupert Klein

  • EF5-1: Spreading of copper technology in ancient times
                Natasa Djurdjevac Conrad, Christof Schütte, Wolfram Schier
  • EF5-2: Data-driven Modeling of the Romanization Process of Northern Africa
                Friederike Fless, Benjamin Ducke, Natasa Djurdjevac Conrad, Christof Schütte
  • EF5-3: A mathematical theory of responsibility in complex multi-agent decision problems with uncertainties
                Rupert Klein, Jobst Heitzig, Markus Brill