معرفی
Marco Zullich holds a Ph.D. in Industrial and Information Engineering from the University of Trieste (2023). His research focuses on neural networks, uncertainty estimation, and machine learning applications. Key areas include Bayesian neural networks, input uncertainty modeling, and reinforcement learning. He has contributed to conferences such as the International Joint Conference on Computer Vision and the European Summer School on AI.
Education:
- Ph.D. in Industrial and Information Engineering, University of Trieste (2023)
Research Interests:
- Neural network architectures and their applications in uncertainty quantification
- Reinforcement learning for interpretable control systems
- Deep learning methods for image super-resolution
- Adaptive prompt tuning in few-shot learning scenarios
Recent Work Trends: His publications emphasize integrating uncertainty estimation into neural networks, exploring novel reinforcement learning frameworks, and advancing vision-guided machine learning techniques.
Teaching & Outreach: Contributed to courses like the ENLIGHT BIP Course on 'Deep Learning for Forestry' and the European Summer School's 'Uncertainty Quantification in Machine Learning'.
