Lucas Lacasa

Lucas Lacasa Saiz de Arce
Perfil

ABOUT ME

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 epidemic modelling in the context of COVID19, quantitative linguistics and real-world modelling of 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 4000 citations and has been highlighted in over 150 feature articles in the media.

Publicaciones recientes

Network bypasses sustain complexity

Estrada, Ernesto; Gómez-Gardeñes, Jesús; Lacasa, Lucas
PNAS 120, e2305001120 (2023)

Irreversibility of symbolic time series: a cautionary tale

Arola-Fernández, LLuis; Lacasa, Lucas
Physical Review E 108, 014201 (2023) , (2023)

Lyapunov Exponents for Temporal Networks

Caligiuri, Annalisa; Eguiluz, Victor, Di Gaetano, Leonardo; Galla, Tobias; Lacasa, Lucas
Physical Review E 107, 4 (2023) , (2023)

High-order correlations reveal complex memory in temporal hypergraphs

Gallo, Luca; Lacasa, Lucas; Latora, Vito; Battiston, Federico
, (2023)

Chaotic renormalization group flow and entropy gradients over Haros graphs

Calero-Sanz, Jorge; Luque, Bartolo; Lacasa, Lucas
Physical Review E 107, 4 (2023) , (2022)

Proyectos de investigación vigentes

MISLAND

Modelling island ecological complexity in the context of global change

I.P.: Lucas Lacasa, Víctor M. Eguíluz
** 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 ...

DYNDEEP

Dynamics of Temporal Networks: Memory and Deep Learning

I.P.: Lluis Arola Fernández, 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 ...

MdM-IFISC-2

Maria de Maeztu 2023-2026

I.P.: Ernesto Estrada, Ingo Fischer, Emilio Hernández-García, Rosa Lopez, Víctor M. Eguíluz, 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 ...

AIHUB

AIHUB

I.P.: Jose Javier Ramasco
HUB CSIC for fomenting the research and services on Artificial Intelligence.

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