Tiziana Di Matteo
King’s College London & CSH External Faculty
Tiziana Di Matteo is professor of econophysics and works in the Department of Mathematics at King’s College London focusing on econophysics, complex systems, complex networks and data science. A trained physicist, she took her degree and PhD from the University of Salerno in Italy before assuming research roles at universities in Australia and Great Britain. She is a member of the CSH external faculty, the UCL Centre for Blockchain Technologies, the board of the Complex System Laboratory, and the Council of the executive committee of the Complex Systems Society and the Board of Directors of the Museo Storico della Fisica e Centro Studi e Ricerche “E. Fermi”.
Tiziana is editor-in-chief for the Journal Advances in Mathematical Physics, main editor of Physica A, editor of the European Physical Journal B, editor of the Artificial Intelligence in Finance journal and guest editor of several other volumes. She has been editor-in-chief for the Journal of Network Theory in Finance. She is co-founder of the Econophysics Network and has been a consultant for the Financial Services Authority, several hedge funds and companies.
Tiziana has authored over 100 papers and presented keynote talks at major international conferences in the US, across Europe and Asia, making her one of the world’s leaders in this field.
V. Macchiati, G. Brandi, T. Di Matteo, et al.
Systemic liquidity contagion in the European interbank market
Journal of Economic Interaction and Coordination (2021)
G. Brandi, T. Di Matteo
Predicting multidimensional data via tensor learning
Journal of Computational Science 53 (2021) 101372
I. Abtibuadesm,et al.
The use of scaling properties to detect relevant changes in financial time series: A new visual warning tool
Physica A: Statistical Mechanics and its Applications 565 (2020) 125561
S. Kumar, T. Di Matteo, A. Chakrabarti
Disentangling shock diffusion on complex networks: identification through graph planarity
Journal of Complex Networks 8 (3) (2020) cnaa023
M. Ausloos, D. Grech, T. Di Matteo, R. Kutner, C. Schickus, H. Stanley
Manifesto for a post-pandemic modeling
Physics A: Statistical Mechanics and its Applications 559 (2020) 125086
G. Brandi, T. Di Matteo
A new multilayer network construction via tensor learning
In: V. Krzhizhanovskaya, et al., Computational Science - ICCS 2020, Lecture Notes in Computer Science 12142, (2020) 148-154
G. Brandi, R. Gramatica, T. Di Matteo
Unveil stock correlation via a new tensor-based decomposition method
Journal of Computational Social Science 46 (2020) 101116
A. Verma, P. Vivo, T. Di Matteo
A memory-based method to select the number of relevant components in principal component analysis
J. Stat. Mech. (2019) 093408
R. J. Buonocore, G. Brandi, R. N. Mantegna, T. Di Matteo
On the interplay between multiscaling and stock dependence
Quantitative Finance 20(1) (2019) 133-145
O. Angelini, T. D. Matteo
Complexity of Products: The Effect of Data Regularisation
Entropy 20(11) (2018) 814