Tiziana Di Matteo


Tiziana Di Matteo received her BSc (Hons) in Physics (110/110 Cum Laude) in 1994 and her PhD in Physics in 1999 in the Dipartimento di Fisica Teorica - Università di Salerno, Italy.  From 1999-2002 she held a postdoctoral Fellow and Research contract "Assegno di Ricerca", at the Dipartimento di Fisica, Università di Salerno, Salerno, Italy. In 2002 she became a postdoctoral Fellow in Applied Mathematics at the Research School of Physical Sciences, Australian National University, Canberra, Australia, and from 2003-2008 she was a QEII Fellow in Applied Mathematics at the same university in Australia.  She joined King’s College London in 2009 as a Lecturer in Financial Mathematics and was promoted to Professor of Econophysics in 2014.

Tiziana is also an External Faculty member of the Complexity Science Hub, Vienna, Austria and a Member of the Board of Directors of the Museo Storico della Fisica e Centro Studi e Ricerche “E. Fermi”. She is also affiliated to the UCL Centre for Blockchain Technologies. 

She 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. Tiziana is Co-founder of the Econophysics Network and she has been a consultant for the Financial Services Authority, several hedge funds and companies.

Research Interests: Econophysics, Application of methods from Statistical Physics to Finance, Data Analytics, Complex Systems, Science of Networks


  • How Network Filtering can extract knowledge from data

    02-11-2022 - 09:15-10:00

    In this talk I will present network-theoretic tools [1-2] to filter information in large-scale datasets and I will show that these are powerful tools to study complex datasets. In particular I will introduce correlation-based information filtering networks and the planar filtered graphs (PMFG) and I will show that applications to financial data-sets can meaningfully identify industrial activities and structural market changes [3-4]. 

    It has been shown that by making use of the 3-clique structure of the PMFG a clustering can be extracted allowing dimensionality reduction that keeps both local information and global hierarchy in a deterministic manner without the use of any prior information [5-6]. However, the algorithm so far proposed to construct the PMFG is numerically costly with O(N3) computational complexity and cannot be applied to large-scale data. There is therefore scope to search for novel algorithms that can provide, in a numerically efficient way, such a reduction to planar filtered graphs. I will introduce a new algorithm, the TMFG (Triangulated Maximally Filtered Graph), that efficiently extracts a planar subgraph which optimizes an objective function. The method is scalable to very large datasets and it can take advantage of parallel and GPUs computing [7]. Filtered graphs are valuable tools for risk management and portfolio optimization too [8-9] and they allow to construct probabilistic sparse modeling for financial systems that can be used for forecasting, stress testing and risk allocation [10].

    Filtered graphs can be used not only to extract relevant and significant information but more importantly to extract knowledge from an overwhelming quantity of unstructured and structured data.  I will provide a practitioner example by a successful Silicon Valley start-up, Yewno. The key idea underlying Yewno’s products is the concept of the Knowledge Graph, a framework based on filtered graph research, whose goal is to extract signals from evolving corpus of data. The common principle is that a methodology leveraging on developments in Computational linguistics and graph theory is used to build a graph representation of knowledge [11], which can be automatically analysed to discover hidden relations between components in many different complex systems. This Knowledge Graph based framework and inference engine has a wide range of applications, including finance, economics, biotech, law, education, marketing and general research.


    [1] T. Aste, T. Di Matteo, S. T. Hyde, Physica A 346 (2005) 20.

    [2] T. Aste, Ruggero Gramatica, T. Di Matteo, Physical Review E 86 (2012) 036109.

    [3] M. Tumminello, T. Aste, T. Di Matteo, R. N. Mantegna, PNAS 102, n. 30 (2005) 10421.

    [4] N. Musmeci, Tomaso Aste, T. Di Matteo, Journal of Network Theory in Finance 1(1) (2015) 1-22.

    [5] W.-M. Song, T. Di Matteo, and T. Aste, PLoS ONE 7 (2012) e31929.

    [6] N. Musmeci, T. Aste, T. Di Matteo, PLoS ONE 10(3): e0116201 (2015). 

    [7] Guido Previde Massara, T. Di Matteo, T. Aste, Journal of Complex networks 5 (2), 161 (2016).

    [8] F. Pozzi, T. Di Matteo and T. Aste, Scientific Reports 3 (2013) 1665. 

    [9] N. Musmeci, T. Aste and T. Di Matteo, Scientific Reports 6 (2016) 36320.

    [10] Wolfram Barfuss, Guido Previde Massara, T. Di Matteo, T. Aste, Phys.Rev. E 94 (2016) 062306.

    [11] Ruggero Gramatica, T. Di Matteo, Stefano Giorgetti, Massimo Barbiani, Dorian Bevec and Tomaso Aste, PLoS One (2014) PLoS ONE 9(1): e84912. 

Tiziana Di Matteo