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PhD Program in Systems Science

PhD Program in Systems Science

Coordinator: Rocco De Nicola


PhD Program Overview

The PhD Program in Systems Science provides the proficiencies needed to build quantitative and qualitative models for the analysis of economic, technological, and social systems. Increasingly important examples of such systems are smart grids, social networks, smart communities, smart cities, management of immigration flows and international exchanges, global financial systems, autonomous vehicles, intelligent and sustainable production systems, health systems, logistics systems, and many others. A focus of the program is on modelling, analyzing, controlling, and regulating cyber-physical and socio-economic systems, characterized by the interaction between decision makers, digital systems and physical units, due to their relevance in many contexts, such as in the automotive, aerospace, chemical, infrastructure, energy, finance, healthcare, digital and manufacturing industries, to mention a few. 

The main educational objective of the PhD Program is to help students master and effectively employ basic methodological tools (mathematical modeling, data analysis, statistics, machine learning, econometrics, algorithms, software tools) and potentially develop new ones. 

The program is organized in two Tracks:

The intersection of the Tracks at the course level provides the Program with an innovative interdisciplinary nature and educational approach. Barriers that traditionally divide domains of knowledge are largely overcome through tackling problems that arise in diverse application frameworks - like economics, finance, industry, computer systems, etc. - with a variety of scientific methodologies for the analysis of systems derived from physics, statistics, econometrics, computer science, systems engineering, and computational methods. 

In particular, the educational offer of the Program includes the use of tools such as machine learning and reconstruction of mathematical models from data, stochastic processes, statistics, network analysis, analysis of dynamic systems, numerical optimization methods, numerical integration for differential equations, and high-dimensional econometrics, which is increasingly characterized by significant and highly- innovative computational components. Such tools allow the study of systems that are extremely complex by dimension or execution speed, and are typically characterized by large amounts of data, the so-called Big Data. The focus on core general methodological skills and on a shared vocabulary of the Program provides the student with a broad versatility to operate in a large variety of application domains. 

Thanks to the interactions between its two Tracks, the PhD program in Systems Science on the one hand enriches students in Computer Science and Systems Engineering with the ability to analyze the socio- economic dimension of industrial problems, from different points of view, and their interplay with institutions, regulatory aspects and market design. On the other hand, students in Economics, Networks and Business Analytics acquire a deeper knowledge of technical tools such as linear algebra, numerical methods for differential equations, optimization, programming and control of dynamic systems, network analysis, statistics and machine learning, and data analytics.

Input and Output Profiles

Perspective students should preferably have a master-level background in computer science, economics, engineering, mathematics, physics, statistics, or in a related field. The Program offers a preparation to analyze and solve a broad spectrum of highly complex problems with an elevated institutional, social and industrial interest, including government solutions and effective intervention policies. Employment opportunities abound within academia (in various disciplines such as engineering, computer science, economics and management), in the public sector (research laboratories, study centers, and regulatory centers), and in the private sector (industry, professional consultancy firms). 

Scientific Board

  • Prof. Rocco De Nicola (Full Professor of Computer Science, IMT School)
  • Prof. Alessandro Abate (Full Professor of Verification and Control, University of Oxford)
  • Dr. Alessandro Arduino (Principal Research Fellow, National University of Singapore)
  • Prof. Alberto Bemporad (Full Professor of Control Systems, IMT School)
  • Prof. Ennio Bilancini (Full Professor of Political Economy, IMT School)
  • Prof. Valerio Capraro (Senior Lecturer in Economics, Middlesex University London)
  • Prof. Luca Cardelli (Royal Society Research Professor, University of Oxford)
  • Prof. Maria Luisa Catoni (Full Professor of Ancient Art History and Archaeology, IMT School)
  • Prof. Gabriele Costa (Assistant Professor in Computer Security, IMT School)
  • Prof. Irene Crimaldi (Associate Professor of Statistics, IMT School)
  • Prof. Tiziana Di Matteo (Full Professor of Econophysics, King’s College London)
  • Prof. Giorgio Stefano Gnecco (Associate Professor in Operations Research, IMT School)
  • Prof. Nicola Lattanzi (Full Professor of Business Administration, IMT School)
  • Prof. Marco Paggi (Full Professor of Structural Mechanics, IMT School)
  • Prof. Pietro Panzarasa (Reader in Organisational Theory and Behaviour, Queen Mary College London)
  • Dr. Luca Polonio (Assistant Professor in General Psychology, IMT School)
  • Prof. José A. Reinoso Cuevas (Associate Professor in Structural Mechanics, Universidad De Sevilla)
  • Prof. Massimo Riccaboni (Full Professor of Economics and Management, IMT School)
  • Prof. Armando Rungi (Assistant Professor in Industrial Organization and International Trade, IMT School)
  • Prof. Francesco Serti (Associate Professor in Economics, IMT School)
  • Prof. Mirco Tribastone (Full Professor of Computer Science, IMT School)
  • Prof. Luca Viganò (Full Professor of Computer Science, King’s College London)
  • Prof. Mario Zanon (Assistant Professor in Control Systems, IMT School)