Trans-Disciplinary Applications
Machine learning holds immense potential for scientific discovery across disciplines. ELIZA scholars apply AI techniques to fields such as life sciences and physics, enabling breakthroughs in medical imaging, computational biology, and complex simulations. The "Transdisciplinary Applications" research area at ELIZA applies machine learning methodologies and techniques to address challenges and foster innovation across a diverse range of scientific disciplines. This area aims to exploit new technological-scientific areas or usage options of AI, thus going beyond classical subject or application boundaries.
Key aspects explored within the Transdisciplinary Applications research area at ELIZA include:
- Bioinformatics and computational biology, which concerns the use of machine learning to understand biological processes, analyse large-scale biological data such as single-cell genomics, predict RNA structures and interactions and contribute to the development of more targeted therapies.
- Medical image analysis, where machine learning is applied to detect cancer cells in medical images from diverse settings or, when specific data is scarce, potentially using generative models to augment datasets.
- Simulation-based inference, which brings together simulations and real-world data to learn, and involves developing methods to find simulator parameters that produce data similar to observed real-world data.
- Understanding neural networks consists of investigating what information artificial neural networks extract from training data and how they represent it.
- Interdisciplinary applications use machine learning in diverse scientific fields to tackle important contemporary challenges, such as climate change.
The involvement of academically proven scholars from various disciplines and relevant actors from the Research and Development departments of companies ensures practical orientation and diverse career paths for young AI researchers within ELIZA, including those working on transdisciplinary applications.
Foundations of Trans-disciplinary Applications
ELIZA connects machine learning with breakthrough applications across scientific disciplines. Our transdisciplinary approach empowers exceptional students to apply AI beyond traditional boundaries—from life sciences to physics—creating new pathways for discovery.
