Biography
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. My research focuses on three directions, causal representation learning, causality-inspired machine learning and how can causality ideas help RL adapt to new domains and nonstationarity faster. The goal is to leverage ideas from causality to make ML methods robust to distribution shift and generalizable across domains and tasks. I also continue working on my previous research on causal discovery, i.e. learning causal relations from data.
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 the VU Amsterdam on learning causal relations jointly from different experimental settings, even with latent confounders and small samples. During Spring 2022, I was visiting the Simons Institute in Berkeley for a semester on Causality.
Download my resumé .
- Causality, causal discovery
- Causal Representation Learning
- Causality-inspired ML and RL
PhD in Artificial Intelligence, 2017
VU Amsterdam
MEng in Computer Science, 2011
Politecnico di Milano, Politecnico di Torino
News
15 March 2023: I will give a keynote at Yes Causal Inference
I gave 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.
I gave a talk on Causality and robustness to distribution shifts at the Bellairs Workshop on Causality
I gave a keynote on causality-inspired ML at Danish Data Science 2022 on November 8, here are the slides.
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”.
I gave a lecture at the Advanced machine learning for Innovative Drug Discovery School in Leuven.
I gave a lecture at the International Summer School on Advances in AI in Como.
I talked about causality-inspired ML at the 2022 Pacific Causal Inference conference.
Invited talk at the Workshop on Artificial Intelligence, Causality and Personalized Medicine in Hannover.
The first workshop on Causal Representation Learning at UAI 2022 was a success! Here are all the slides and the videos of the talks in the afternoon session.
Recent Publications
Team and Collaborators
PhD students
Danru Xu
PhD student (UvA)
Mátyás Schubert
PhD student (UvA)
Yongtuo Liu
PhD student (UvA)
Guest researchers
Andrea Conte
Master student (University of Torino)
Davide Talon
PhD student (IIT Genoa)
Fan Feng
PhD student (City University Hong Kong)
Alumni
Eva Sevenster
Master student (UvA)
Frank Brongers
Master student (UvA)
Nadja Rutsch
Master student (UvA)
Willemijn de Hoop
Master student (UvA)
Contact
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. There is an open PhD position in high-dimensional causal inference I will be co-supervising with Stéphanie van der Pas, deadline is April 2nd 2023.
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.
- s DOT magliacane AT uva DOT nl
- Lab 42, Science Park 900, Amsterdam, 1012 WX