My research focuses on understanding, optimizing and predicting relations between the microscopic and macroscopic properties of complex large-scale interacting systems.
I like to approach research by addressing application-oriented problems involving domain experts from different disciplines via developing models and algorithms derived from statistical physics principles.
I obtained a degree in Physics at University of Padova and a PhD in Statistical Physics at Universitè Paris Sud 11 in 2015, advised by Silvio Franz and Satya Majumdar with a Marie Curie ITN fellowship .
After that, I was a Program Postdoc at the Santa Fe Institute working with Cris Moore and then at the Columbia Data Science Institute working with David Blei.
We are always happy to consider applications for internships, PhD and Postdoc positions.
Interested candidates should have a strong background in Physics, Math and Statistics and interested to work on interdisciplinary applications in Machine Learning, Probability and Computer Science.
To apply, please send your CV, list of 2-3 references, transcript of records and covering letter via email.
I study large interacting systems following two main research directions to tackle the problem under different angles.
Inference on networks
Complex interacting systems are often represented by large datasets containing a considerable amount of information. The question is how to capture the relavant macroscopic behavior by retaining only a small amount of information. This can be formalized as an inference problem where we want to extract a set of model parameters capable of describing the observed system and making future predictions.
In particular, I am interested in analyzing systems where elements interact in multiple ways, as in multilayer networks, and systems displaying hidden hierarchies that might play a role in determining the interaction patterns that we observe.
Routing Optimization on networks
Optimizing traffic on a network is a relevant problem in situations where traffic congestion has a big impact on transmission performance. This can be formalized as a computationally-hard constrained optimization problem where interactions are non-local and a global optimization is required.
I investigate this problem by adopting approaches that combine insights from statistical physics and a methodologies developed in optimal transport theory.
Caterina De Bacco is a Cyber Valley Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany.
Cyber Valley Research Group Leader, 2018
Max Planck Institute for Intelligent Systems
Postdoc at Data Science Institute, 2018
Program Postdoc, 2017
Santa Fe Institute
PhD in Statistical Physics, 2015
Universitè Paris Sud 11
MSc in Theoretical Physics, 2012
Università di Padova
BSc in Physics, 2010
Università di Padova