The primary goal of the delve is to gain practice finding papers on a topic. The delve is broke into the following steps, with a suggested timeline to follow.
3/25 - Selecting a Topic: your topic could be directly from a paper read in class, something mentioned in a paper, or a topic of interest from another class.
4/3 - Finding Related Work: you should make a list of roughly 10 papers whose title or citations suggest they are applicable to your topic. You should not yet read these papers in any depth, but may look at their citations for other papers.
4/10 - Identifying Key Papers: by reading the abstracts of the papers, identify 2-4 that seem particularly useful. These papers may not be the foundational papers, or even papers with new results. Survey papers are likely ideal for your purposes.
4/17 - Skim and Summarize: skim the ‘key papers’ identified above and summarize their contents. The goal of this step is not to understand the papers in depth, or even be able to articulate exactly what they cover, but to further narrow your focus.
4/24 - Read in Depth: identify which paper(s) or sections of papers have the most important information on your topic, and read them in depth. This should include no more than 40 pages of in depth reading.
5/1 - Produce an Artifact: you should prepare an artifact suitable for an undergraduate audience, equivalent in scope to a 400-600 word essay. This may be a written document explaining one of the algorithms or constructions with examples, a short video motivating the value of an approach, an argumentative essay about work that was overlooked or undervalued, or another deliverable of your choice.
5/7 - Babble: you should prepare a 5-10 minute 'babble' to give to your fellow students during our final exam block. This can be an explanation of the paper you read in depth, an overview of the topic you skimmed or summarized, an activity teaching a technique, a demonstration of a tool, or a similar activity.
General Compression Algorithms
Monadic Programming or Continuation Passing Style
Formal Verification
Scientific Computing
Neural Networks/LLMs