Prediction through Description
Recent advances in modern machine learning allow us to exploit Big Data to study complex challenges that were unsolvable before. At IARAI we focus on pressing problems with great impact on society and the planet. We can now, for instance, predict patterns in urban traffic or large-scale rainfall, assess climate change, classify biomedical images, and help discover new drugs.
We train models to learn the features required to make good predictions. These features thus describe the essence of the object or state examined, and are therefore of interest in their own right.
We research Adversarial Attacks, Self-Supervised Learning, as well as Point Clouds and Sets for the detection of multi-dimensional objects. Another focus are bias & noise in data and models, reliability, uncertainty, and interpretability of models and predictions.