PETER J. BADDOO
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 datadriven methods
BIO
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 fouryear MMath at the University of Oxford.
I am originally from Reading, UK. Beyond research, my interests are sports, music and theology.
SELECTED RECENT PAPERS
Here are three papers I've been working on recently:

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

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

2021: "Generalization of wavingplate 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.
NEWS
08/22: I visited the AI Institute in Dynamic Systems at the University of Washington
07/22: I visited the Woods Hole GFD Program
06/22: I gave talks at USNC/TAM, CAvid, Osaka University, and Ghana Numerical Analysis
05/22: I won a Community Building award at MIT Math
05/22: I gave two guest lectures in Gil Strang’s course on machine learning
04/22: Our paper on kernel learning for dynamical systems was published in PRSA
02/22: I taught complex variables with applications in Spring 2022
MISCELLANY

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.

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