Riccardo De Santi
ETH AI Center PhD student. Generative Optimization and Exploration for Large-Scale Scientific Discovery.
I am a PhD student in Machine Learning at the ETH AI Center, advised by Andreas Krause, Niao He, and Kjell Jorner, and affiliated with the Institute of Machine Learning and NCCR Catalysis. My current research focuses on optimization and exploration via generative models — bridging decision-making under uncertainty, optimization and generative modeling to tackle fundamental challenges in large-scale scientific discovery. I work on mathematical foundations, scalable learning methods, and real-world applications including enzyme design for sustainable chemistry.
Before this, I worked on unsupervised exploration in RL, earning an Outstanding Paper Award at ICML with Marcello Restelli, and visited Michael Bronstein at the University of Oxford and Imperial College London.
Feel free to reach out if you wish to collaborate, exchange ideas, or seek thesis supervision.
Contacts: rdesanti@ethz.ch | Google Scholar | Twitter | LinkedIn | Github
news
| Jul 8, 2025 | Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning accepted as an Oral at the Workshop on Generative AI and Biology at ICML 2025 |
|---|---|
| Jun 3, 2025 | Constrained Molecular Generation via Sequential Flow Model Fine-Tuning has been accepted at the Workshop on Generative AI and Biology at ICML 2025 |
| Jun 1, 2025 | Efficient Generative Models Personalization via Optimal Experimental Design accepted at the Workshop on Models of Human Feedback for AI Alignment at ICML 2025 |
| May 1, 2025 | Provable Maximum Entropy Manifold Exploration via Diffusion Models has been accepted at ICML 2025! |
| Jun 1, 2024 | Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods and Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction accepted at ICML 2024! |