PETER J. BADDOO
Welcome to my academic website!
I am an applied mathematician at MIT. My main research interests are currently:
Complex function theory
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.
SELECTED RECENT PAPERS
Here are three papers I've been working on recently:
You can view a more complete list here.
11/21: I gave invited talks at Tufts, Dartmouth and MIT
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
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
06/21: We uploaded our paper on multiple interacting swimmers to the arXiv
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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