Sara Magliacane

Sara Magliacane

Assistant Professor

University of Amsterdam

Biography

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 .

Interests
  • Causal Representation Learning
  • Causal discovery
  • Causality-inspired ML and RL
  • Causality in general
  • Neurosymbolic/StarAI approaches
Education
  • 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

Team

PhD students

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Daan Roos

PhD student (UvA)

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Danru Xu

PhD student (UvA)

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Mátyás Schubert

PhD student (UvA)

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Nadja Rutsch

PhD student (AUMC)

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Roel Hulsman

PhD student (UvA)

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Yongtuo Liu

PhD student (UvA)

Close collaborators

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Fan Feng

PhD student (City University Hong Kong)

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Riccardo Massidda

ELLIS PhD student (University of Pisa/UvA)

Guest researchers

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Jacopo Dapueto

Visiting PhD student (U Genoa)

Alumni

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Andrea Conte

Master student (University of Torino)

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Angelos Nalmpantis

Master student (UvA)

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Aoqi Zuo

Visiting PhD student (University of Melbourne)

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Danilo de Goede

Master student (UvA)

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Davide Talon

Visiting PhD student (IIT Genoa)

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Eva Sevenster

Master student (UvA)

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Frank Brongers

Master student (UvA)

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Ilze Amanda Auzina

PhD student at University of Amsterdam

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Jakub Řeha

PhD student (UvA)

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Mara Pislar

Master student (UvA)

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Phillip Lippe

ELLIS PhD student (UvA/Qualcomm)

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Pieter Bouwman

Master student (UvA)

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Willemijn de Hoop

Master student (UvA)

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Yohan Runhaar

Master student (UvA, Adyen)

News

Contact and Jobs

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.