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Achievement: Pull Sharkx2Achievement: YOLOAchievement: Quickdraw

Achievements

Achievement: Pull Sharkx2Achievement: YOLOAchievement: Quickdraw

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@heorltd

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padj/README.md

Hello 👋

I’m Thomas Padgett, a principal consultant with the operational research group at HEOR. I'm an ex-aerospace engineer, with a PhD in computational fluid dynamics and agent-based modelling. I'm big on nature, being outside, plants, and mountains. 🌱🌱🌱

See my github pages site for updates on what I'm doing!

On-going work ✨

» Lots and lots of system dynamics for healthcare. » Multi-indication modelling and pricing.
» Discrete event simulation for capacity planning.
» Machine learning and data analytics using real world data (CPRD/HES).
» Public health policy modelling.
» Early drug pricing/revenue forecasting, inc. optimisation approaches for market access launch sequencing.

Interests 👀

» Heuristic optimisation techniques and their application to health.
» Health economic evaluations in R/python, particularly simulation (individual-level) models for obesity and diabetes.
» Building simple example models on Github for training/education (PSM, cSTM, SEIR, etc.)

Popular repositories Loading

  1. Age_Structure_Predictor Age_Structure_Predictor Public

    This project considers the prediction of the age structure/population pyramid of the United Kingdom based on ONS/UN data for birth rates, mortalities, and net migration values starting with initial…

    Python 1

  2. Genetic_Algorithm_TSP Genetic_Algorithm_TSP Public

    A bespoke python genetic algorithm to solve the generalised 2D travelling salesman problem.

    Python

  3. Patient_Level_Disease_Model Patient_Level_Disease_Model Public

    Simple model of a fictitious disease.

    Python

  4. Desire_Paths Desire_Paths Public

    This script simulates desire paths in a 2D domain using a biased A* algorithm, and pre-defined path and obstacle locations.

    Python

  5. padj padj Public

    Config files for my GitHub profile.

  6. R_IBM_example R_IBM_example Public

    Example workings for individual-based modelling approaches in R

    R