Resources
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December 11, 2022
Research 101: Writing a paper
A memoir from Emma Hamel about writing a research paper. This article is intended for everyone that has some research they need to publish but don’t know how/where to start.
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December 12, 2022
Research 101: Writing tips and tricks I
A collection of various mathods and lessons for being more efficient at writing academic papers. Will be progressively updated over time by various authors.
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December 21, 2022
Campus Cluster - Basic usage
The campus cluster is a collection of high-performance servers maintained by the NCSA. It is a utility we will use for our research so it’s important to learn to use it. This guide is a beginner document in using the CampusCluster but can be applicable to many high-performance computing systems.
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April 19, 2023
Campus Cluster - Installing/using custom software
While the campus cluster has many software modules available by default, there will be times when you’ll need to use software that is not available through the default modules. This post will help guide you on using custom software on the campus cluster.
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April 20, 2023
Campus Cluster - Using Jupyter notebooks remotely
Jupyter notebooks are nice because you can interact with your script iteratively and interactively. But sometimes you need the computing power of a remote server. This short post is on how to setup a jupyter notebook on a remote server and interact with it through your browser.
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June 13, 2023
MathML reference
MathML is a XML-like markup language for mathematical languages. It’s typically used to display mathematical expressions on websites though the native tree-like structure makes it very useful for storing and parsing mathematical expressions.
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July 22, 2023
Latent Dirichlet Allocation Basics
This article explores the fundamental aspects of Latent Dirichlet Allocation (LDA), a highly-utilized unsupervised probabilistic technique for topic modeling. It elaborates the core principles of LDA, providing an accessible interpretation of the underlying mathematical concepts that dictate the operation of the model, as well as the training process using Gibbs Sampling with operable illustration. The real-world applications and potential extensions of the LDA model are also explored.
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