Basics and Contact

  • Postal address:
    Technische Universität Berlin
    Institut für Mathematik
    FG Modellierung, Simulation und Optimierung realer Prozesse
    MA 4-4
    Straße des 17. Juni 136
    10623 Berlin

  • E-Mail: borghi[at]

  • Telephone: +49 (0)30 314-25752

  • Fields of Study:
    Double BSc in Automation Engineering, MSc in Systems and Control

  • Project: Q12

Research interests:

  • Computational methods for solving nonlinear eigenvalue problems
  • Identification of exceptional points
  • Model reduction methods
  • Data-driven modelling
  • Bifurcation theory
  • Koopman operator theory

Short CV:

  • [Sep. 2019-Oct.2021] MSc in Systems and Control at TU Delft, Delft, Netherlands.
  • [Sep. 2016-Jul.2019] Double BSc program in Automation Engineering at TongJi University, Shanghai, China.
  • [Sep. 2015-Dec.2018] BSc Automation Engineering at University of Bologna, Bologna, Italy.

Partnership with Industry:

  • Second BSc thesis titled “Simulation and Implementation of the Input Shaping Technique for a Flexible Transmission System” completed at Beckhoff Automation Co., Shanghai, China.

The ability to describe nature through mathematical models has always fascinated me. Defining a system through mathematical equations gives us the possibility to predict its dynamics and control them. For me, this is a very exciting and inspiring concept, which led me to do my MSc in Systems and Control.

In the development process of mathematical models, eigenvalues are essential parts for the analysis of the system’s dynamics. These are intrinsic factors of the model. From the construction of bridges to the analysis of the stability of rockets, the efficient computation of eigenvalues is pivotal in many engineering and scientific fields. In several applications, a nonlinear eigenvalue problem needs to be solved to compute the eigenvalues of the system. In the last decade, there have been numerous developments in the design of efficient
numerical methods for this type of problem.

I decided to join this Ph.D. program to contribute to the development of numerical tools for the computation of these intrinsic factors. An important step for understanding the dynamics of systems through mathematical models.