Screening CVs is a critical step in the hiring process, used to determine which candidates progress further in the hiring funnel. While the approach to CV screening can vary by company and industry, the primary goal remains the same: to identify candidates with the potential to succeed in subsequent interview stages.

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In this final post on the toy Javascript async message queue server implementation, I make it compatible with the Python version. I use the library of unit tests developed previously to ensure identical behaviour between the two codebases.

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I write a small library of unit tests for the message queue servers.

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I apply NIST's suite of statistical tests to my randomness extractor implementations from the previous posts.

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I discuss various models of biased bit sequences, and how to extract uniform random (or close to it) output bit sequences from them, illustrated with Python code.

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In this final post on the toy Python async message queue server implementation, I make further feature and code improvements.

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I write a somewhat more complicated, but still relatively simple async message queue server in Javascript.

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I write a simple, bi-directional async message queue server in Javascript.

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Why are Randomized Controlled Trials (RCTs, known as A/B testing in much of the industry) testing is widely regarded as the golden standard of causal inference? What else can a Data Scientist do if A/B testing is not possible, and why are those alternatives inferior to A/B testing?

This papers shows, using 15 experiments (for ads on Facebook) where a RCT was conducted, that common observational methods (run on the Facebook data, by ignoring the control group) severely mis-estimate the true treatment life (as measured by the RCT), often by a factor of 3x or more. This is true, even though Facebook has (i) very large sample sizes, and, (ii) very high quality data (per-user feature vector) about its users which are used in the observational methods. This should be a major red flag for Data Scientists working on common marketing measurements (such as marketing campaigns) using observational methods.

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Last year, in 2023, Dennis Austin, one of the original developers of Powerpoint passed away. The news made it to the front page on Hacker News, and prompted a lively discussion of the merits of Powerpoint itself. I posted a top-level comment, which itself sparked a lively thread of responses. In this post I will expand and extend my points about Powerpoint in a business settings, after pointing to hard-learned lessons from Edward Tufte and Jeff Bezos.

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Based on the book Compact Stars by N. K. Glendenning a general relativistic treatment of pressure and energy in compact stars is given, followed by simple models of neutron and quark stars. Hypothetical strange quark matter and strange stars are introduced highlighting the importance of sub-millisecond pulsar detections. (Note: this is a student paper I wrote sometime in 2008-2010.)

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I run Monte Carlo simulations of content production over random Watts-Strogatz graphs to show various effects relevant to modeling and understanding Randomized Controlled Trials on social networks: the network effect, spillover effect, experiment dampening effect, intrinsic dampening effect, clustering effect, degree distribution effect and the experiment size effect. I will also define some simple metrics to measure their strength. When running experiments these potentially unexpected effects must be understood and controlled for in some manner, such as modeling the underlying graph structure to establish a baseline.

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Even since I've had a 386 in my room as a teenager, I've taken computers apart and modified and upgraded them. When getting a new workstation computer, I have never bought a pre-assembled one (like a Dell or HP), I always buy the components and build it myself. I've been doing this for 30+ years, so I'm used to it, it's part of my lifecycle. I built my current workstation in 2022, but due to reasons I describe in this post, I've been making a fair amount of modifications to it ever since. Finally, now in 2024 April, I feel that the system has come together and is perfect — I don't want to or plan to make any more changes to it.

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For the past 8 years I've been using Cloud9 for writing code in the cloud, and I can wholeheartedly recommend it: it just works for me. 7 years ago AWS acquired the company, so the project is no longer developed. However, this is not a problem, since it still just works. The only problem I ever had was a minor UI bug, which I fixed in a couple of minutes, as detailed in this post. This was possible because Cloud9 is open-source. I ❤️ open source.

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A compilation of academic-style papers I have written over the years.

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I clean up the code I wrote for earlier posts, and run simple trading experiments.

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I investigate the volatility of aggressive take-profit strategies on tech stocks.

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I investigate the volatility of some securities, and its stability over time.

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It is the beginning of the year — a good time to reflect on the previous year and make plans for the year ahead. I wrote this document for my team members in 2024 January to kick off the year. This is an abridged version with sensitive content removed.

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I wrote this document for my team members — Data Scientists and Data Engineers — to help them do a better on their annual performance reviews.

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