Riccardo De Santi
ETH AI Center PhD student. Exploration for Out-of-Distribution Discovery: from Theory to Molecules.
OAT Y 14, ETH
Zurich, Switzerland
I am a Ph.D. student at ETH Zurich, advised by Andreas Krause, Niao He, and Kjell Jorner, and supported by the ETH AI Center and NCCR Catalysis. After visiting California Institute of Technology (Caltech), I continue working with Yisong Yue’s group and Frances H. Arnold’s lab to close the loop between generative discovery and chemical wet-lab validation. Recently, I was selected as a 2026 Rising Star in Data Science by UChicago, Stanford, Harvard, and UCSD. I serve as a research mentor for LeadTheFuture.
My research focuses on developing generative algorithms for discovery beyond the data — bridging flow and diffusion modeling, decision-making under uncertainty, and optimization, to enable new-to-nature discovery. Broadly, I aim to contribute to the foundations of a science of generative discovery: principled methods that move generative modeling beyond distribution matching and toward the discovery of new, valid, and useful structures, designs, and hypotheses.
This research program builds on my earlier work on the foundations of exploration in RL, which includes an Outstanding Paper Award at ICML with Marcello Restelli, and research visits with Michael Bronstein at the University of Oxford and Imperial College London on geometric and causal inductive biases for exploration.
Feel free to reach out if you wish to collaborate, exchange ideas, or seek project or thesis supervision.
Contacts: rdesanti@ethz.ch | Google Scholar | Twitter | LinkedIn | Github
News
| Oct 16, 2026 | We introduce LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators — the first method to steer any scientific generator with natural-language discovery goals by distilling the knowledge and reasoning of frontier LLMs. |
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| Sep 30, 2026 | Selected as a 2026 Rising Star in Data Science by UChicago, Stanford, Harvard, UCSD. |
| Sep 28, 2026 | Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules and On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization (collab. with Genesis Molecular AI) accepted at NeurIPS 2026. |
| Sep 26, 2026 | Our Next Generation of Discovery Loops session, co-organized with Samuel Kaski, Chris Holmes, Yee Whye Teh, and colleagues, has been accepted at the ELLIS UnConference 2026 in Paris. |
| Jun 30, 2026 | Gave a series of talks on Foundations of Generative Discovery Beyond the Data via Flow and Diffusion Models at Caltech, UC Berkeley, UCLA, UCSD, UC Irvine, Stanford. |
| Apr 30, 2026 | Three works on Flows for Discovery accepted at ICML 2026: (1) A Unified Density Operator View of Flow Control and Merging, (2) Constrained Flow Optimization via Sequential Fine-Tuning for Molecular Design, and (3) Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning. |
| Apr 15, 2026 | A Unified Density Operator View of Flow Control and Merging accepted with Oral presentation at the Workshop on Real-World Constrained and Preference-Aligned Flow and Diffusion-Based Models at ICLR 2026. |
| Jan 26, 2026 | Three works on Flows for Discovery accepted at ICLR 2026: (1) Verifier-Constrained Flow Expansion for Discovery Beyond the Data, (2) Riemannian Optimization via Pre-trained Diffusion Models, and (3) Value Matching: Scalable and Gradient-Free Reward-Guided Flow Adaptation. |
| Oct 15, 2025 | Co-designed with Andreas Krause the new Diffusion Generative Models module for ETH Zurich’s Probabilistic AI course (~500 students). |
| Oct 6, 2025 | Constrained Flow Optimization via Sequential Fine-Tuning for Molecular Design awarded Oral at the Frontiers in Probabilistic Inference Workshop at NeurIPS 2025. |
| Sep 18, 2025 | Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning has been accepted as Spotlight at NeurIPS 2025 |
Selected Publications
- NeurIPSSpotlight and Oral Presentation