The impact of the Department of Excellence

The project has developed in depth the two pre-established objectives, attaining the expected results on termination of the project and laying the foundations for the consolidation of the medium- to long-range  impact of the  project.

The text below describes in greater detail some of the more important results regarding scientific and research activities and collaboration with companies.

The SNS research group in Financial Mathematics has undergone a notable evolution: of particular interest is the synergy that has been created with the European project SoBigData++ “European Integrated Infrastructure for Social Mining and Big Data Analytics”, which has led to a more in-depth study of the topics of Machine Learning and Neural Networks, with applications in risk assessment for the share markets and in machine learning for dynamical systems, both deterministic and with stochastic components. A software library (NetworkSNS) has also been developed, which is available free online and contains the implementation in Python of several  methods developed within the group.

The research group in Numerical Analysis and Computational Sciences was formed thanks to the hiring of a full professor in the s.s.d. MAT/08 leading to the training of a new line of research on Numerical Analysis, previously lacking at the Scuola Normale Superiore.

The two research groups created on the DipE have also contributed to the teaching syllabus for the new PhD course in “Computational Methods and Mathematical Models for Sciences and Finance”, which offers courses never offered before now and made possible by the creation of the research group in Computational Sciences, in line with the intentions of the DipE.

In addition, two new lines of research have been created in Informatics and Bio-informatics, which have further supported the research and training programmes in Computational Sciences. The creation of a new Informatics unit marked a historical innovation for the Scuola Normale, which had previously never had teaching personnel pertaining to this competition macro-sector. This choice also enabled a response to the ever increasing interest in research on topics of Machine Learning and Big Data, opening up a new line of research in the area of the so-called “Explainable Artificial Intelligence” and “Neural Networks”.

The two new lines of research in the informatics area interact naturally with the modern structure of calculation dedicated and acquired thanks to DipE funding (on this subject, see the  INFRASTRUTTURE DI RICERCA [RESEARCH INFRASTRUCTURE] page). The said structure has also served as a transversal stimulus for other groups of the Faculty of Sciences carrying out  significant research activity linked to computational-type approaches.

Furthermore, several important projects have been set up in collaboration with the companies involved in the topics of the DipE. More specifically, some joint activities have been started with Unicredit and Fineco Asset Management aimed at the development of portfolio optimisation topics, integrating the classic approaches with new resolutive methods based on machine learning. A project has also been started up in collaboration with the company A2A, focussing on the study of new machine learning methods applied to the prediction of order book high frequency financial series, with applications to the energy market. Lastly, the collaboration activated with Consob concerning a training course on data analysis, network science, artificial intelligence and data science has led to the development of innovative tools for the detection of market abuse (such as insider trading or price manipulation) and has witnessed the application of several methods of statistical analysis of Big Data to the extremely vast data base containing all the  operations carried out on the Italian shares market.

Thanks also to the activities linked to the DipE of the Faculty of Sciences, the Scuola Normale can now be considered as a prestigious research centre in the field of the Computational Sciences and Data Science, with significant synergies between the various disciplinary areas and with a sizeable number of lecturers, researchers and students engaged in the field. The project has also promoted a considerable increase in interdisciplinary collaborations regarding problems of considerable scientific interest and impact.

Lastly, the DipE has provided economic incentives for the technical and administrative personnel involved in the starting up of the project, with particular reference to the personnel supporting the teaching, research and communication activities of the SNS and to the personnel supporting the laboratories and research centres of the SNS involved in the support activities and the research (Laboratorio NEST, Centro HPC).