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This talk by Patrick Laub was delivered to the Quantitative Methods Network (QMNET) at the University of Melbourne on June 4th, 2021. Additional information is below:

TITLE:
A Software Engineer's Toolkit for Quantitative Research

ABSTRACT:
The daily tasks of a quantitative researcher overlap in many ways with the daily tasks of a programmer. Luckily, there are a great software tools which are designed for programmers which can be leveraged to produce quantitative research outputs. I'll give a brief overview/demo of some of these tools, with a focus on how you can use them to improve or speed up your research/publication workflow. This includes how to: connect to a remote computer (SSH/Mosh), solve maths/stats problems with code (Python, R, C++, Mathematica, Julia), run & prototype code (Jupyter, VS Code), speed up some code (JIT-compiling, parallelisation), collaborate with others (Git, Github), write a maths-heavy manuscript (LaTeX, Markdown), give an interactive presentation with formulas & code (reveal.js/slides.com, RISE) and publish it to the web (Jekyll, Github Pages). The talk should be useful to those who are totally new to these tools, and to seasoned veterans (who, hopefully, may have some extra tips to contribute).

BIO:
Patrick Laub is a mathematician & software engineer, currently working as a Postdoctoral Fellow at The University of Melbourne. He is interested in the intersection of mathematics/statistics and computing, and has previously interned at Google & worked for Data61. His recent research topics include the Empirical Dynamic Modelling, Approximate Bayesian Computation, and Hawkes Processes. Patrick's joint PhD in computational applied probability was completed between University of Queensland and Aarhus University. For further information, see https://pat-laub.github.io/ .