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@MathePhysics

MathePhysics

Mathephysics is a loosely organised, inter-disciplinary research group led by Akshunna S. Dogra, focusing on the construction of rigorous theories for "Learning" (Machine and Biological) and the development of principled computational and mathematical models in scientific domains.

Our students, contributors, and mentors hail from a rich diversity of backgrounds (Albania, Australia, Canada, China, Croatia, Germany, Grenada, India, Pakistan, South Korea, United Kingdom, and the United States to date). Better still, they harbor equally diverse intellectual ambitions, pursuing expertise in various nooks of mathematics, several corners of physics, and the many labyrinthine mazes of machine/deep learning.

Formally supervised students have gone on to earn postgraduate positions at institutions such as Oxford University, University College London, University of Michigan, and UT Austin amongst others, and have found employment at reputable financial institutions such as Squarepoint Capital and Funding Circle.

Please email [email protected] if you would like to see what we are all getting up to and/or wish to be involved in some way.

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  1. UROP-2022 UROP-2022 Public

    Option Pricing with Machine Learning Methods

    Jupyter Notebook 14 3

  2. error-correction error-correction Public

    Error correction of neural network solutions to Poisson’s equation

    Python 2

  3. .github .github Public

  4. Koopman-Learning Koopman-Learning Public

    Forked from william-redman/Koopman-Neural-Network-Training

    Optimizing Neural Networks via Koopman Operator Theory

    MATLAB

  5. SHAPER SHAPER Public

    Forked from rikab/SHAPER

    Python implementation of the SHAPER algorithm for computing arbitrary shape observables for collider physics.

    Python

  6. FINDER FINDER Public

    Codebase associated with T. Murphy, A. S. Dogra et. al, FINDER: Feature Inference on Noisy Datasets using Eigenspace Residuals

    MATLAB

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