Computer Science on rkempe’s blog

A Few Notes on Distributed Systems Models and Failures

Mon, 08 Jun 2026 00:11:31 -0400

While the terms “distributed systems” and “distributed computing” are often used interchangeably, for my purposes, I’ve found it useful to draw a distinction. Distributed systems research is often focused on how to get multiple components of a system to act together in a reliable way, whereas distributed computing research is focused on how to use multi-component systems to solve a problem by breaking it down into different parts. While distributed systems is more of an engineering discipline focused on systems architecture, distributed computing is more of a mathematical discipline focused on the design of algorithms.

Some Interesting Things I’ve Read/Watched: Link Dump #3

Sat, 08 Nov 2025 00:20:43 -0500

Hi! Here is a linkdump. I told myself I would post these with really minimal context because I am too busy to summarize these things and have a really giant backlog of links I’ve been wanting to post, but uh, the writer in me won out and I failed. Some of these have way more description than others. But I do also have quotes I’ve pulled for some of them, so maybe that will help make this post more interesting.

(Computer-)Scientific Abstracts, As Analyzed by a Confused Undergrad

Fri, 04 Jul 2025 19:42:17 -0400

I recently decided I wanted try presenting my work at an academic conference, so I had to write and submit an abstract for my proposed presentation. The problem, of course, was that I had no idea how to write a scientific abstract, which was a bit of a problem. Most of the advice I received and could find online was too vague for my taste, so I decided to take matters into my own hands and deconstruct some abstracts to see how those authors did it. And I’m really glad I did, because it was very enlightening.

Some Interesting Things I’ve Read Lately, Episode 3

Mon, 30 Jun 2025 20:23:47 -0400

You know the drill. In the last episode I did of this series, I said the next one would be coming shortly, huh?

Yeah, right. I lied. These come out when they come out.

Some Interesting Things I’ve Read/Watched: Link Dump #2

Mon, 30 Jun 2025 15:34:26 -0400

Here’s another linkdump—a list of links to stuff I thought was interesting but likely won’t get to properly reviewing any time soon.¹

I reserve the right to more fully review any of these article at a later date, of course. (Though at this point, it’s extremely unlikely to ever happen.)

Why Does Theory Matter in Computer Science? (Part 5)

Thu, 22 May 2025 21:34:31 -0400

Here is a long list of sources I consulted at various points while doing research for this project. As it might have become clear through reading the previous installments of this series, this was more a talk about the densest subgraph problem than it was about theory being useful in computer science. There are a few reasons for that, which I might explain in more detail at some point in the future. This means that I presented a lot of information that I borrowed from other people! Here’s an annotated bibliography of sorts, in case you were interested in going deeper, for whatever reason.

Why Does Theory Matter in Computer Science? (Part 4)

Thu, 22 May 2025 21:28:43 -0400

In Part 3, we discussed the densest subgraph problem (DSP) and some algorithms for solving it. In this section, we’ll be looking at how we can generalize this problem and those algorithms to solve some other problems. This is the part of the talk where I will introduce some fancy new math I had to learn in order to understand how one might generalize iterative peeling to solve adjacent problems.

Some Interesting Things I’ve Read/Watched: Link Dump #1

Sun, 16 Mar 2025 22:07:57 -0400

I keep coming across articles (and occasionally videos, and occasionally fiction or poetry) online that I want to share and comment on, but I recently realized that I will never have time to fully comment on everything I read that I find interesting. So I’m pulling another page out of Cory Doctorow’s book: here is a dump of links to cool stuff, along with some (hopefully very) brief descriptions of why I found these articles interesting. (Also, here is a link to a link dump where Doctorow ¹ talks about link blogging. It might be interesting for you to look at.)

Why Does Theory Matter in Computer Science? (Part 3)

Thu, 13 Feb 2025 23:17:01 -0500

In Part 2 of this talk, we gave a crash course to graph theory and showed how we can use it to view some real-world problems as instances of the densest subgraph problem (DSP). But what exactly is the DSP? If you’ve studied graph theory, you may have heard of something called the Maximum Clique Problem. The goal of the max clique problem is to find the largest complete subgraph in a graph. If we consider our vertices to be people, and edges to represent a friendship relationship between two people, in the max clique problem we are trying to find the largest friend group in a community.

Why Does Theory Matter in Computer Science? (Part 2)

Fri, 24 Jan 2025 12:47:46 -0500

In the first part of this talk, I made the case that theory is useful because it allows us to find (or at the very least, have the correct toolkit and language to explore) solutions to real-world problems. In this part, we are going to look at some examples of such problems and develop mathematical language to be able to discuss them more abstractly. I’ve put the term “real-world” in quotes in the title, because I’m going to be talking about these problems in a lot of generality. However, I want to stress that specific instances of these problems are actually relevant in industry, and I think it’ll be easy to see why once I start talking about them.

Why Does Theory Matter in Computer Science? (Part 1)

Sun, 29 Dec 2024 00:38:01 -0500

If you’re a computer science student, you probably had to take an introductory discrete math course at some point. Did you enjoy it? If so, this talk probably isn’t for you, so you can feel free to skip the rest. (Or not – hopefully you feel like you can still learn something from me!) Jokes aside, it’s actually okay not to enjoy your intro to discrete math course: like, personally, I loved mine, but I also completely hated my discrete probability course and would prefer never to see it again. But I pick on discrete math because I feel like if it’s taught well, it can be a turning point for many people, and it certainly was for me.

Understanding the Python Memory Model

Sun, 24 Nov 2024 00:28:41 -0500

One of my quests during the research project I did last summer was to find a way to simulate pointers in Python, or at the very least, get some sort of named reference-like behaviour from the language. Unfortunately, I quickly learned that this is quasi-impossible, because Python is a language that aims to abstract as many implementation details away from the user as possible, and gives programmers very limited ways in which to interact with the language.

Heuristics, Approximation Algorithms, and Relaxations: An Introduction

Sun, 24 Nov 2024 00:03:31 -0500

While all NP-hard optimization problems are identical in terms of exact solvability, they may differ wildly from the approximative point of view. If the goal is to obtain an answer that is “good enough”, some problems become much easier (such as KNAPSACK), while others (such as CLIQUE) remain extremely hard.

Some Interesting Things I’ve Read Lately, Episode 1

Fri, 16 Aug 2024 23:51:41 -0400

Here’s a look at some of the articles and books I’ve been reading lately, or at least, the ones that stuck out to me. Originally, I wanted to do this as a weekly series, inspired by Cory Doctorow’s link posts where he comments on various articles he’s read - but I don’t have that kind of time. Also, I’m really not that great at remembering the various articles I’ve stumbled through online, so you’re going to get these when you get them.