Lucas Lacasa

Lucas Lacasa Saiz de Arce


I'm a Physicist/Applied Mathematician interested in developing methods for the analysis of Complex Systems. My current topics of interest include Networks, Dynamics, and their interfaces. I use tools from Dynamical Systems Theory, Time Series Analysis, Network Theory, Statistical Physics and Machine Learning to analyse the onset of Complexity and Criticality emerging in physical, socio-technical and biological systems, as well as to develop innovative methods for Data Analysis, a topic for which I was awarded an EPSRC Early Career Fellowship and was the recipient of the 2019 International Prize in Formal Sciences, awarded by USERN. Besides my theoretical work, applications of current interest include real-world modelling of complex, socio-technical systems.

I graduated (BSc+MSc) in Theoretical Physics from Complutense University in 2004 got my PhD in Physics of Complex Systems from Technical University of Madrid in 2009. I am currently a Research Associate Professor (with tenure) at IFISC, a Physics Institute of the Spanish National Research Council (CSIC). Before that I lived for 8 years in London (2013-2021), where I was Reader in Applied Mathematics at the School of Mathematical Sciences, Queen Mary University of London. Even before that, I was Assistant Professor of Applied Mathematics at the School of Aeronautics, Technical University of Madrid (2010-2013). I have also been an Associate Research Fellow at Kings College London and held visiting positions at other institutions including CBPF (Brazil), Oxford (Physics department, 2012) or UCLA (Mathematics, 2017).

I have published about 80 peer-reviewed publications, including papers in multidisciplinary venues such as PNAS or Nature Communications, Physical journals such as Physical Review X or Physical Review Letters, Mathematical journals such as Nonlinearity, or Computer Science journals such as IEEE TPAMI. My work has received over 5500 citations and has been highlighted in over 200 feature articles in the media.

Recent Publications

High-order correlations reveal complex memory in temporal hypergraphs

Gallo, Luca; Lacasa, Lucas; Latora, Vito; Battiston, Federico
Nature Communications 15, (2024)

Dynamical stability and chaos in artificial neural network trajectories along training

Danovski, Kaloyan; Soriano, Miguel C; Lacasa, Lucas
Frontiers in Complex Systems 2, 1367957 (2024)

An effective theory of collective deep learning

Arola-Fernández, Lluís; Lacasa, Lucas
Submitted (2024)

Artificial neural networks through the lens of dynamical systems theory

Danovski, Kaloyan (Supervisors: Lucas Lacasa, Miguel C. Soriano)
Master Thesis (2023)

Network bypasses sustain complexity

Estrada, Ernesto; Gómez-Gardeñes, Jesús; Lacasa, Lucas
Proceedings of the National Academy of Sciences of the USA (PNAS) 120, e2305001120 (2023)

Ongoing Research projects


Neuromorphic-Enhanced Heterogeneously-Integrated FMCW LiDAR

P.I.: Miguel C. Soriano
The NEHIL project, an EU-Korea partnership, is set to transform the landscape of digital technologies through groundbreaking neuromorphic architectures and advanced heterogeneous integration such as LiDAR systems. This collaborative initiative aims to ...


Modelling island ecological complexity in the context of global change

P.I.: Lucas Lacasa
** This project (PID2020-114324GB-C22) is part of a coordinated project between IFISC and IMEDEA, both research centers from CSIC located in Mallorca. The project is funded by AEI and a PhD fellowship ...


Dynamics of Temporal Networks: Memory and Deep Learning

P.I.: Lucas Lacasa
The interaction between elements of a complex system arising in physics, biology or sociology can be modelled as a mathematical graph. The precise architecture of this interaction backbone plays a fundamental role ...


Maria de Maeztu 2023-2026

P.I.: Ernesto Estrada, Ingo Fischer, Emilio Hernández-García, Rosa Lopez, Claudio Mirasso, Jose Javier Ramasco, Raúl Toral, Roberta Zambrini
After 15 years of its existence, IFISC can point to a proven track record of impactful research. The previous 2018-2022 MdM award has significantly enhanced the institute's capabilities, as demonstrated by an ...

Contact Form

This web uses cookies for data collection with a statistical purpose. If you continue browsing, it means acceptance of the installation of the same.

More info I agree