Riccardo De Santi

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 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

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!
Jan 17, 2024	Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning accepted at ICLR 2024!
Nov 23, 2023	My TEDx talk Beyond the Limits of the Mind: Scientific Discovery Reimagined is now available online!
Nov 22, 2023	On December 1st I will ufficially start my PhD at the ETH AI Center advised by Andreas Krause, Niao He, and Kjell Jorner.
Oct 27, 2023	Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning accepted at the Causal Representation Learning Workshop at NeurIPS 2023!

selected publications

ICML

Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods

Riccardo De Santi*, Manish Prajapat*, and Andreas Krause

International Conference on Machine Learning (ICML), 2024

Bibtex PDF

@article{de2024global,
  title = {Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods},
  author = {De Santi*, Riccardo and Prajapat*, Manish and Krause, Andreas},
  journal = {International Conference on Machine Learning (ICML)},
  year = {2024},
}

ICML

Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction

Riccardo De Santi, Federico Arangath Joseph, Noah Liniger, and 2 more authors

International Conference on Machine Learning (ICML), 2024

Bibtex PDF

@article{de2024geometric,
  title = {Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction},
  author = {De Santi, Riccardo and Arangath Joseph, Federico and Liniger, Noah and Mutti, Mirco and Krause, Andreas},
  journal = {International Conference on Machine Learning (ICML)},
  year = {2024},
}

ICLR

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning

Mirco Mutti, Riccardo De Santi, Marcello Restelli, and 2 more authors

International Conference on Learning Representations (ICLR), 2024

Causal Representation Learning Workshop at NeurIPS 2023

Bibtex PDF

@article{mutti2023exploiting,
  title = {Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning},
  author = {Mutti, Mirco and De Santi, Riccardo and Restelli, Marcello and Marx, Alexander and Ramponi, Giorgia},
  journal = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  workshop = {Causal Representation Learning Workshop at NeurIPS 2023},
}

JMLR

Convex Reinforcement Learning in Finite Trials

Mirco Mutti, Riccardo De Santi, Piersilvio De Bartolomeis, and 1 more author

Journal of Machine Learning Research (JMLR), 2023

Bibtex PDF

@article{mutti2023convex,
  title = {Convex Reinforcement Learning in Finite Trials},
  author = {Mutti, Mirco and De Santi, Riccardo and De Bartolomeis, Piersilvio and Restelli, Marcello},
  journal = {Journal of Machine Learning Research (JMLR)},
  year = {2023},
}

AAAI

Provably efficient causal model-based reinforcement learning for systematic generalization

Mirco Mutti*, Riccardo De Santi*, Emanuele Rossi, and 3 more authors

Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2023

Workshop on Spurious Correlations, Invariance, and Stability at ICML 2022 and A Causal View on Dynamical Systems Workshop at NeurIPS 2022

Bibtex PDF

@inproceedings{mutti2023provably,
  title = {Provably efficient causal model-based reinforcement learning for systematic generalization},
  author = {Mutti*, Mirco and De Santi*, Riccardo and Rossi, Emanuele and Calderon, Juan Felipe and Bronstein, Michael and Restelli, Marcello},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  workshop = {Workshop on Spurious Correlations, Invariance, and Stability at ICML 2022 and A Causal View on Dynamical Systems Workshop at NeurIPS 2022},
}

Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments

Pietro Maldini*, Mirco Mutti*, Riccardo De Santi, and 1 more author

2022

Decision Awareness in Reinforcement Learning Workshop and First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward at ICML 2022

Bibtex PDF

@inproceedings{maldini2022non,
  title = {Non-Markovian Policies for Unsupervised Reinforcement Learning in Multiple Environments},
  author = {Maldini*, Pietro and Mutti*, Mirco and De Santi, Riccardo and Restelli, Marcello},
  workshop = {Decision Awareness in Reinforcement Learning Workshop and First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward at ICML 2022},
  year = {2022},
}

NeurIPS

Challenging common assumptions in convex reinforcement learning

Mirco Mutti*, Riccardo De Santi*, Piersilvio De Bartolomeis, and 1 more author

Advances in Neural Information Processing Systems (NeurIPS), 2022

Complex Feedback in Online Learning (CFOL) Workshop at ICML 2022

Bibtex PDF

@article{mutti2022challenging,
  title = {Challenging common assumptions in convex reinforcement learning},
  author = {Mutti*, Mirco and De Santi*, Riccardo and De Bartolomeis, Piersilvio and Restelli, Marcello},
  journal = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  workshop = {Complex Feedback in Online Learning (CFOL) Workshop at ICML 2022},
}

ICML

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

ICML Workshop on Reinforcement Learning Theory, 2021

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},
  workshop = {ICML Workshop on Reinforcement Learning Theory, 2021},
  talk = {https://icml.cc/virtual/2022/oral/16290},
}