Unit Milan
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Research Agenda
- Interactive learning and game theory
- Statistical learning and non-convex optimization
- Health and computational biology
- Natural language processing
- Computational social sciences
- Neural networks, in connection with classical AI and neuroscience
Unit Mission
Our unit advances the foundations of AI by designing adaptive methods for single-agent and multi-agent decision-making. We also investigate core theoretical challenges in deep learning to improve the understanding of modern ML models.
We apply these foundational insights to critical domains, developing novel ML techniques for personalized medicine and health. We also create advanced systems that can understand and interact through multiple modalities, such as language and vision.
To maximize our impact, the unit collaborates directly with industry partners on pilot projects and use cases. This strategy allows us to test and validate our adaptive AI solutions on real-world challenges, ensuring our research drives tangible industrial innovation.
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