I am an assistant professor in the Amsterdam Machine Learning Lab at the University Amsterdam and an ELLIS Scholar in the Interactive Learning and Interventional Representations program. During Spring 2022, I was visiting the Simons Institute in Berkeley for a semester on Causality.
My research is at the intersection of causality and machine learning. My goal is to find how can causal reasoning improve state-of-the-art AI, especially in terms of robustness, generalization across domains/tasks, and safety, and imbue it with the strong theoretical guarantees typical of causality research. This Figure summarizes the research directions I have been pursuing and how they connect to each other.
My research focuses on three directions: (i) causal representation learning (i.e. learning causal varaibles from high-dimensional data, e.g. sequences of images [1, 2, 3, 4]), (i) causal discovery (i.e. learning causal relations from data, e.g. focusing on a statistically and computationally efficient way [5]), and (iii) downstream tasks, e.g., how can causality ideas help ML/RL adapt to new domains or nonstationarity and compositionally generalize ([6, 7, 8]).
Previously I was a Research Scientist at MIT-IBM Watson AI lab and a postdoctoral researcher at IBM Research NY, working on methods to design experiments that would allow one to learn causal relations in a sample-efficient and intervention-efficient way. I received a PhD at VU Amsterdam on learning causal relations jointly from different experimental settings, even with latent confounders and small samples. During my PhD, I interned at Google Zürich and NYC. Previously, I studied Computer Engineering at Politecnico di Milano and Torino and at the University of Trieste.
Download my resumé .
PhD in Artificial Intelligence, 2017
VU Amsterdam
MSc in Computer Engineering, 2011
Politecnico di Milano, Politecnico di Torino (double degree)
BSc in Computer Engineering, 2008
Università degli Studi di Trieste
until June 15: I have an open PhD position of Learning concepts with theoretical guarantees in collaboration with Frans Oliehoek at TU Delft
May 2025: Matyas went to present our work on SNAP: Sequential Non-Ancestor Pruning at AISTATS 2025.
April 2025: Organized a causality workshop with Virginia Aglietti at DALI 2025 in Sorrento.
February 2025: My first two PhD students got their PhD, both cum laude! Congrats [Riccardo Massidda (U Pisa)] (https://bsky.app/profile/smaglia.bsky.social/post/3lifjpfg2ek2w) and Phillip Lippe (U Amsterdam)!! Matyas and I attended the Bellairs workshop on Causality in the Era of Foundation Models.
December 2024: Guido Imbens (Stanford) visited our group.
October 2024: Visiting Harvard to give an invited talk on (i)CITRIS and BISCUIT in the Harvard Data Science Initiative Causal Seminar. I’m also a communication chair for CLeaR 2025 and helping organize a CRL workshop at NeurIPS 2024.
September 2024: I have been travelling quite a lot, first to Dagstuhl for a seminar on Artificial Intelligence and Formal Methods Join Forces for Reliable Autonomy , then to Nijmegen to give a keynote at PGM 2024, Oslo as an examiner for the PhD defense of Ghadi Al Hajj (it was successful :), and finally to Münich for an Oktoberfest Causality mini-workshop organized by TUM.
Summer 2024: Busy summer! First I co-organized a Logic and AI workshop at the Institute for Advanced Study (IAS) here in Amsterdam. Then I went to UAI 2024, where Riccardo presented our UAI 2024 paper on learning linear abstractions, even without knowing neither the abstract nor the concrete causal graph. There I gave an invited talk at the Causal Inference for Time Series workshop and then helped organize the (other?) Causal Inference workshop. After that I attended ICML 2024, where Danru and I presented our ICML 2024 paper on CRL in partially observed settings, while with Yongtuo we presented our ICML 2024 paper on Amortized Equation Discovery in Hybrid Dynamical Systems. Finally, I gave an invited talk at ONRL for their AI Seminar Series.
For teaching matters, you can contact me via Canvas.
PhD students. I have an open PhD position of Learning concepts with theoretical guarantees in collaboration with Frans Oliehoek at TU Delft. Deadline: 15 June.
Other jobs. Currently I don’t have any open vacancy for interns, research assistants or postdocs, but if I do, they will be announced on the UvA vacancies website.
Master students. If you are a Master in AI student at the University of Amsterdam and are interested in causality, feel free to contact me for potential thesis topics. As a rule, I also don’t supervise Bachelor or Master students from other universities.