Large-Scale Spatio-Temporal Reasoning and Learning
||Large-Scale Spatio-Temporal Reasoning and Learning|
||Fredrik Heintz <email@example.com>|
||2023-08-31 – 2024-03-01|
The goal of our research is to develop novel reasoning and learning methods for large-scale spatio-temporal applications. This includes for example time-series learning (diffusion models and GANs) and multi-agent reinforcement learning. The expected scientific impact is publications in top-level conferences and the expected soecity impact is more effective decision-making methods for autonomous systems such as unmanned aircraft, more effective transporation solutions and methods for privacy-preserving synthetic data generation.