Daniele Massaro

Postdoctoral researcher at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. My current research develops a causal-spectral framework for gust-wing interaction. Previously, at KTH and MIT, I studied transitional and turbulent shear flows through space-time adaptive direct numerical simulations, and developed machine-learning models for predicting unsteady vortical flows.

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About Me

Originally from Verona, I hold a Master of Science in Aerospace Engineering from Politecnico di Milano, with a thesis on the stability of wall-bounded flows with spanwise forcing under the supervision of Prof. Maurizio Quadrio. I then obtained a PhD in computational fluid dynamics from KTH Royal Institute of Technology in Stockholm, under the supervision of Prof. Philipp Schlatter. There, I conducted space-adaptive direct numerical simulations to study transitional and turbulent shear flows. I subsequently spent two years as a researcher at MIT, working with Prof. Wim van Rees on vortex dynamics and machine-learning-based prediction of unsteady aerodynamic loads. I am currently a Postdoctoral Fellow at the University of Cambridge, where I work with Prof. Matthew Colbrook on a causal-spectral framework for vortex-gust wing interaction.


Contact Details

Daniele Massaro
Massachusetts Institute of Technology
Dept. of Mechanical Engineering
77 Massachusetts Avenue
Cambridge, MA 02139
USA
danmas@mit.edu

Research

Interests

I’ve always had wide-ranging interests, from philosophy to the natural sciences. Although I ended up following a scientific career, I’ve kept a real love of literature and the humanities, and I still lose myself in classic novels whenever I can (you’ll find a few recommendations below). At Politecnico di Milano, I built a solid theoretical foundation, particularly through courses in functional and complex analysis, while also studying rotary-wing dynamics and spacecraft orbits. But it was fluid mechanics that really captured me: there’s something rather elegant about the way it brings together mathematics, physics and engineering.

My work on turbulence began with my Master's thesis on stability and transition delay. During my PhD, I delved into wall turbulence and coherent structures, using high-order spectral element methods and adaptive mesh refinement for direct numerical simulations. To gain a deeper understanding, we combined established techniques such as Proper Orthogonal Decomposition with cutting-edge approaches such as Shannon transfer entropy from information theory, investigating mechanisms in transitional and turbulent flows.

At MIT, I explored vortex-dominated flows, drawing on MIT's long-standing tradition in artificial intelligence to develop AI models for predicting complex aerodynamic interactions.

I am always keen to learn and to collaborate, especially on topics ranging from classical turbulence theory to modern AI modelling of complex flow systems.

Teaching

MIT

  • Master's thesis supervision - Spring 2026
  • Committee member, de Florez Competition (MechE), 2025 and 2026

KTH

Teaching assistant

  • Mekanik I (Classical Mechanics) - 2020, 2021
  • Strömningsmekanik (Fluid Mechanics) - 2021, 2022, 2023
  • Teknisk strömningsmekanik (Fluid Mechanics for Engineers) - 2021, 2022, 2023, 2024
  • Master's thesis supervision - Fall 2021

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