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A desktop application to simulate lift control with a custom algorithm optimised to reduce distance travelled by the lift by 82.5%. Developed with Qt.

Python 100.00%
lift-control-simulation naive-algorithm lift lift-algorithm elevator optimisation destination destination-dispatch priority-queue python

lift-control-simulation's Introduction

Hi there, I'm Isaac! 👋

Graduated at the top of my cohort at the University of Exeter with a first-class honours degree (81%) in BSc Computer Science, ranking 1st for both overall grade and final year project. Proficient in Python, with experience in financial markets and big tech. Passionate about developing large-scale distributed systems and algorithmic optimisation.

Experience

Company Role Tenure
T. Rowe Price Fixed Income Quant Developer September 2023 – Present
T. Rowe Price Fixed Income Quant Developer Intern June 2022 – August 2022
IBM Software Developer Intern June 2021 – June 2022

Feel free to message me on LinkedIn!

Skills

Languages Python, SQL, C++, C
Technologies Airflow, pandas, NumPy, FastAPI, Flask, pytest, PyQt, scikit-learn, Dask, MongoDB
Tools Git, Linux, AWS (S3, MWAA, CloudWatch), Kubernetes, Qt, Splunk, Grafana, Swagger, CI/CD

lift-control-simulation's People

Contributors

isaaccheng9 avatar

Stargazers

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lift-control-simulation's Issues

Add option to perform a set of benchmark simulations

This would make the differences in performance between the naive algorithm and the improved algorithm clearer to the user. It would perform five sets of simulations using both algorithms, and then show the results in a new window. The new window would display:

  • A table of results
  • A graph to compare results visually (Matplotlib?)

Migrate GUI from PyQt5 to PyQt6

This will improve the handling of high DPI scaling, which is an increasingly common problem due to resolutions in laptops and monitors increasing.

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