CEN Colloquium: Topological and Geometric Insights for 4D Vision, Environmental Perception, and Humanoid Robotics

  • Date: Feb 4, 2025
  • Time: 03:00 PM (Local Time Germany)
  • Speaker: Stephan Chalup, University of Newcastle
  • Location: Max Planck Institute for Human Development
  • Room: Open Campus Space
  • Host: Center for Environmental Neuroscience

Humans naturally perceive the environment in three dimensions, even though it embodies higher-dimensional characteristics such as dynamic, temporal, and contextual factors. This presentation investigates the integration of topology, geometry, and environmental elements to explore whether training in 4D intuition could enhance spatial perception, decision-making, and cognitive adaptability. A neural network approach is introduced for analysing the topology of 4D image data, providing innovative perspectives on how higher-dimensional understanding may improve human and machine spatial awareness. A geometric approach to real-time object detection underscores practical feasibility, while insights into neurodynamic landscapes reveal how neural networks evolve topologically during learning. The facial pareidolia hypothesis in design perception further highlights how humans may emotionally connect with their surroundings by identifying abstract facial patterns in architectural forms and textures. These advancements not only deepen our understanding of human environmental perception but also have practical implications for programming humanoid robots to better interpret and navigate complex, dynamic environments.

Professor Stephan Chalup, based at the University of Newcastle, Australia, conducts research in neural network development, high-dimensional data analysis, and interdisciplinary collaborations. He earned his Ph.D. in Computing Science from Queensland University of Technology in 2002, following studies in mathematics and neuroscience at the University of Heidelberg. Stephan’s early work addressed the internal mechanisms of artificial neural networks, including the use of recurrent neural networks to predict context-sensitive sequences—a conceptual precursor to modern AI innovations like large language models.

He is the Chief Investigator of the ARC Discovery Project on estimating the topology of low-dimensional data using deep neural networks, which aims to develop tools for visualizing and analysing complex 3D and 4D data. As head of the Interdisciplinary Machine Learning Research Group and the Newcastle Robotics Lab, Stephan leads research initiatives applying AI to fields such as environmental biology, architecture, health, transport safety, and material science.

To join remotely: https://mpib-berlin.webex.com/mpib-berlin/j.php?MTID=maff3dee0ad54cb921191ff33d0b92347

Meeting number: 2744 645 6180

Password: ArpQMePJ352

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