Welcome to my academic website!

I am an applied mathematician at MIT. My main research interests are currently:

  • Complex function theory

  • Fluid dynamics

  • Machine learning and data-driven methods



I am currently an Instructor in Applied Mathematics at MIT. Previously I was an EPSRC Doctoral Prize Fellow in the Department of Mathematics at Imperial College London. I completed my PhD in July 2019 in the Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. Prior to that, I completed a four-year MMath at the University of Oxford.


I am originally from Reading, UK. Beyond research, my interests are sports, music and theology.


Here are three papers I've been working on recently:

  • 2021: "Physics-informed dynamic mode decomposition (piDMD)" (link, video)
    P. J. Baddoo, B. Herrmann, B. J. McKeon, J. N. Kutz & S. L. Brunton

  • 2021: "Log-lightning computation of capacity and Green's function" (link, video abstract)
    P. J. Baddoo & L. N. Trefethen, Maple Transactions


  • 2021: "Generalization of waving-plate theory to multiple interacting swimmers" (link, video)
    P. J. Baddoo, N. J. Moore, A. U. Oza & D. G. Crowdy

You can view a more complete list here.


12/21: I was interviewed with Nick Trefethen and Richard Brent on our articles in Maple Transactions

12/21: I gave invited talks at Tufts, Dartmouth, MIT and Warwick

12/21: We uploaded our paper on physics-informed DMD to the arXiv

09/21: I gave the Applied Math Colloquium at NJIT
08/21: I presented at Ghana Numerical Analysis Days 🇬🇭
08/21: Our proposal for a mini-symposium on data-driven modelling was accepted at USNC/TAM

07/21: Our paper was published in the inaugural edition of Maple Transactions

07/21: I filmed two new video abstracts on kernel learning and log-lightning computations

07/21: I visited Profs Steve Brunton and Nathan Kutz at the University of Washington

06/21: I presented at the Euromech colloquium on Machine learning methods for turbulent flows

06/21: We uploaded our paper on kernel learning for dynamical systems to the arXiv


  1. The image in the header is an illustration of the trajectories of point vortices embedded in a potential flow with a periodic array of obstacles. The dynamical system can be expressed in a conservative form which leads to a Hamiltonian that describes the vortex paths. The colours denote the energy of each configuration: red means highly energetic states whereas blue corresponds to states with low interaction energy. Analytic expressions for the trajectories are available in a canonical circular domain which is then mapped to the physical domain using a new periodic Schwarz–Christoffel mapping formula.

  2. If you came here looking for a dating website, you'd be better off at (one "d")