Sara Magliacane
Full Professor (W3) at University of Amsterdam
Saarbrücken, Saarland, Germany
Summary
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Senior🎓
Top SchoolSara Magliacane is a Full Professor of Computer Science at Saarland University and an assistant professor affiliated with the University of Amsterdam, bringing 14 years of research and engineering experience at the intersection of causality, neuro-symbolic AI, and program synthesis. Her work spans academic leadership and industry research, including pivotal roles in the MIT-IBM Watson AI Lab and IBM Research on causal discovery, causal transfer learning, and safe AI approaches. She has a strong systems background from earlier engineering roles at Google and in database/query optimization, combined with a PhD from VU Amsterdam and practical teaching experience designing courses on combining symbolic and statistical AI. Colleagues know her for bridging rigorous theoretical work with hands-on implementations that led to publications and a book chapter on SPARQL query engines. She routinely collaborates across institutions and disciplines, pairing probabilistic and symbolic methods to make AI both more interpretable and robust. A less obvious strength is her track record of translating research prototypes into engineered artifacts across both academic and industrial environments.
14 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Vrije Universiteit Amsterdam (VU Amsterdam)Master of Engineering - MEng, Computer Engineering, Master of Engineering - MEng, Computer Engineering at Politecnico di MilanoAlta Scuola Politecnica Diploma, Managment of innovative projects in complex environments, Alta Scuola Politecnica Diploma, Managment of innovative projects in complex environments at Alta Scuola PolitecnicaBachelor of Engineering - BE, Computer Engineering, Bachelor of Engineering - BE, Computer Engineering at Università degli Studi di Trieste
English, Italian, Slovenian
Stackoverflow
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Github Skills (12)
distributions9
adaptation9
predict9
causal8
causal-inference6
deep-learning5
pytorch5
inference4
w3c4
reinforcement-learning1
machine-learning1
cds1
Programming languages (4)
Github contributions (5)
caus-am/dom_adapt
Oct 2018 - Mar 2021
Code for the NIPS 2018 paper "Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions"
Contributions:13 commits, 1 PR, 15 pushes in 2 years 5 months
pytorchdistributionsdeep-learningcausalconditional
IntelligentSystems/PlanetWars
Dec 2012 - Dec 2013
Contributions:36 commits in 1 year
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