Sotabase

Career

· Professor, Saarland University2026–
· Professor, University of Amsterdam2026–
· Assistant Professor, University of Amsterdam2020–
· Research Scientist, MIT-IBM Watson AI Lab2019–
· Postdoctoral Researcher, IBM Research NY2017–2019
· Intern, Google Research2014–2014
· Intern, Google2013–2013
· Research Assistant, Politecnico di Milano2011–2011
· PhD in Artificial Intelligence, VU Amsterdam2011–2017
· Software Engineer, Agenzia Regionale della Sanità, Trieste2008–2008
· MSc in Computer Engineering, Politecnico di Milano, Politecnico di Torino2008–2011
· Research Intern, Department of Electronics and Informatics, University of Trieste2007–2008
· BSc in Computer Engineering, Università degli Studi di Trieste2004–2008

Publications (28)

CITRIS: Causal Identifiability from Temporal Intervened Sequences
International Conference on Machine Learning · 2022
124
cited
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
International Conference on Learning Representations · 2021
73
cited
Verifiably safe exploration for end-to-end reinforcement learning
International Conference on Hybrid Systems: Computation and Control · 2020
58
cited
Multi-View Causal Representation Learning with Partial Observability
International Conference on Learning Representations · 2023
57
cited
Sample Efficient Active Learning of Causal Trees
Neural Information Processing Systems · 2019
44
cited
Factored Adaptation for Non-Stationary Reinforcement Learning
Neural Information Processing Systems · 2022
41
cited
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
International Conference on Learning Representations · 2022
40
cited
Active Structure Learning of Causal DAGs via Directed Clique Tree
Neural Information Processing Systems · 2020
39
cited
BISCUIT: Causal Representation Learning from Binary Interactions
Conference on Uncertainty in Artificial Intelligence · 2023
35
cited
iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects
arXiv.org · 2022
23
cited
A Sparsity Principle for Partially Observable Causal Representation Learning
International Conference on Machine Learning · 2024
22
cited
Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
Neural Information Processing Systems · 2023
15
cited
Modulated Neural ODEs
Neural Information Processing Systems · 2023
11
cited
Graph Switching Dynamical Systems
International Conference on Machine Learning · 2023
10
cited
Learning Causal Abstractions of Linear Structural Causal Models
Conference on Uncertainty in Artificial Intelligence · 2024
7
cited
Towards the Reusability and Compositionality of Causal Representations
CLEaR · 2024
7
cited
Amortized Equation Discovery in Hybrid Dynamical Systems
International Conference on Machine Learning · 2024
2
cited
Combining Causal Models for More Accurate Abstractions of Neural Networks
CLEaR · 2025
1
cited
IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning
IEEE Trans. Neural Networks Learn. Syst. · 2024
1
cited
Learning to Defer for Causal Discovery with Imperfect Experts
arXiv.org · 2025
1
cited
Sotabase
Sara Magliacane | Researcher Profile | Sotabase | Sotabase