Riccardo De Santi

Office: Caltech, ANB 328

Pasadena, California, USA

Currently, I’m at California Institute of Technology (Caltech), visiting Yisong Yue’s group and Frances H. Arnold’s Nobel-awarded lab, working to close the loop between generative exploration and chemical wet-lab discovery. 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. I mentor LeadTheFuture students taking their first steps into research.

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 thesis supervision.

Contacts: rdesanti@ethz.ch | Google Scholar | Twitter | LinkedIn | Github

news

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 accepted as an 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
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

selected publications

ICMLOral Presentation

A Unified Density Operator View of Flow Control and Merging

Riccardo De Santi, Malte Franke, Ya-Ping Hsieh, and 1 more author

International Conference on Machine Learning (ICML), 2026

Oral at Real-World Constrained and Preference-Aligned Flow and Diffusion-Based Models Workshop at ICLR 2026

Bibtex PDF

@article{de2026unified,
  title = {A Unified Density Operator View of Flow Control and Merging},
  author = {De Santi, Riccardo and Franke, Malte and Hsieh, Ya-Ping and Krause, Andreas},
  journal = {International Conference on Machine Learning (ICML)},
  year = {2026},
  award = {Oral at Real-World Constrained and Preference-Aligned Flow and Diffusion-Based Models Workshop at ICLR 2026},
  badge_extra = {Oral Presentation}
}

ICMLOral Presentation

Constrained Molecular Generation via Sequential Flow Model Fine-Tuning

Sven Gutjahr*, Riccardo De Santi*, Luca Schaufelberger*, and 2 more authors

International Conference on Machine Learning (ICML), 2026

Oral at Frontiers in Probabilistic Inference Workshop at NeurIPS 2025

Bibtex

@article{gutjahr2025constrained,
  title = {Constrained Molecular Generation via Sequential Flow Model Fine-Tuning},
  author = {Gutjahr*, Sven and De Santi*, Riccardo and Schaufelberger*, Luca and Jorner, Kjell and Krause, Andreas},
  journal = {International Conference on Machine Learning (ICML)},
  year = {2026},
  award = {Oral at Frontiers in Probabilistic Inference Workshop at NeurIPS 2025},
  badge_extra = {Oral Presentation}
}

ICLR

Verifier-Constrained Flow Expansion for Discovery Beyond the Data

Riccardo De Santi*, Kimon Protopapas*, Ya-Ping Hsieh, and 1 more author

International Conference on Learning Representations (ICLR), 2026

Bibtex PDF Code

@article{de2026verifier,
  title = {Verifier-Constrained Flow Expansion for Discovery Beyond the Data},
  author = {De Santi*, Riccardo and Protopapas*, Kimon and Hsieh, Ya-Ping and and Krause, Andreas},
  journal = {International Conference on Learning Representations (ICLR)},
  year = {2026},
}

NeurIPS SpotlightSpotlight and Oral Presentation

Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning

Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, and 3 more authors

Advances in Neural Information Processing Systems (NeurIPS), 2025

Spotlight at NeurIPS 2025 and Oral at Workshop on Generative AI and Biology at ICML 2025

Bibtex PDF Code

@article{de2025flow,
  title = {Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning},
  author = {De Santi, Riccardo and Vlastelica, Marin and Hsieh, Ya-Ping and Shen, Zebang and He, Niao and Krause, Andreas},
  journal = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  award = {Spotlight at NeurIPS 2025 and Oral at Workshop on Generative AI and Biology at ICML 2025},
  badge_extra = {Spotlight and Oral Presentation},
}

ICML

Provable Maximum Entropy Manifold Exploration via Diffusion Models

Riccardo De Santi*, Marin Vlastelica*, Ya-Ping Hsieh, and 3 more authors

International Conference on Machine Learning (ICML), 2025

Bibtex PDF Code

@article{de2025provable,
  title = {Provable Maximum Entropy Manifold Exploration via Diffusion Models},
  author = {De Santi*, Riccardo and Vlastelica*, Marin and Hsieh, Ya-Ping and Shen, Zebang and He, Niao and Krause, Andreas},
  journal = {International Conference on Machine Learning (ICML)},
  year = {2025},
}

ICMLOutstanding Paper

The Importance of Non-Markovianity in Maximum State Entropy Exploration

Mirco Mutti*, Riccardo De Santi*, and Marcello Restelli

International Conference on Machine Learning (ICML), 2022

Outstanding Paper Award at ICML 2022

Bibtex Talk PDF

@inproceedings{mutti2022importance,
  title = {The Importance of Non-Markovianity in Maximum State Entropy Exploration},
  author = {Mutti*, Mirco and De Santi*, Riccardo and Restelli, Marcello},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  award = {Outstanding Paper Award at ICML 2022},
  badge_extra = {Outstanding Paper},
  talk = {https://icml.cc/virtual/2022/oral/16290},
}