I am an assistant professor in the Amsterdam Machine Learning Lab at the University Amsterdam and a Research Scientist at MIT-IBM Watson AI lab. During Spring 2022, I was visiting the Simons Institute in Berkeley for a semester on Causality.
The goal of my research is to find how can causality improve current machine learning (ML) algorithms, especially in terms of robustness, generalization across domains/tasks, and safety. My research focuses on three directions: causal representation learning (i.e. learning causal factors from high-dimensional data, e.g. sequences of images [1, 2, 3]), causal discovery (i.e. learning causal relations from data), and causality-inspired ML, e.g. how can ideas from causality help ML/RL adapt to new domains, nonstationarity and varying number of objects with different latent parameters, even when we cannot guarantee that we identified the true causal factors.
Previously I was 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
MSc in Computer Engineering, 2011
Politecnico di Milano, Politecnico di Torino (double degree)
BSc in Computer Engineering, 2008
Università degli Studi di Trieste
October 2023: Invited talk at the IROS workshop on Causality for Robotics: Answering the Question of Why!
October 2023: We are organizing a NeurIPS 2023 workshop on Causal Representation Learning, deadline Oct 2. Update we got 62 submissions!
July 2023: Invited talk in the Second Workshop on Spurious Correlations at ICML.
July 2023: Invited talk on Causality-inspired ML at the European Meeting of Statisticians in Warsaw. Pierogi <3
June 2023: Invited talk on Causal vs Causality-inspired representation learning at the IST seminar series Mathematics, Physics & Machine Learning.
5 May 2023: Keynote at the ICLR 2023 workshop Time Series Representation Learning in Health
11-14 April 2023: I’m in the organization team of CLeaR 2023, you can see what’s going on here on Twitter
28 March 2023: Invited talk at the “Interdisciplinary Perspectives on AI & Culture” event on Prediction and causality with historian Melvin Wevers.
20 March 2023: Invited talk on causality and distribution shifts at the 1st joint webinar of the IMS New Researchers Group, Young Data Science Researcher Seminar Zürich and the YoungStatS Project. Check the video of the talk and also check the previous talk by Zijian Guo and the discussion by Niklas Pfister.
15 March 2023: Keynote at Yes Causal Inference in Eidhoven.
February 2023: Two lectures on causal discovery and causality-inspired ML at the Barcelona School of Economics at the ML and causality course in the Data Science Master.
January 2023: I was one of the four speakers (with Guido Imbens, Kathrin Schultz and Thomas Icard) at the Royal Netherlands Academy of Arts and Sciences (KNAW) event on Causality in economics, computer science, logic, and language. This is the second time I gave a talk after Guido Imbens, and talking in a session with a Nobel Laureate is still as nerve-wracking as the first time.
January 2023: Talk on Causality and robustness to distribution shifts at the Bellairs Workshop on Causality
November-December 2022: Busy months: I’m an area chair for CLeaR 2023, a meta-reviewer for AAAI 2023, helping organize CLeaR 2023, UAI 2023 and the NeurIPS workshop “A causal view on dynamical systems”.
For teaching matters, you can contact me via Canvas.
PhD students If you have questions about joining my group or AMLab, check this list first.
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.