siboehm
This is the website of Simon Boehm. I study Data Science at ETH Zurich. Currently I am an intern with QuantCo, an econ startup, where I work on data engineering.
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A Local Search Engine
April 30, 2021
A tool for searching through every document I've ever read, locally and within seconds.
Reading books and blogs works well enough for gaining knowledge. However both are almost useless as works of reference. Searching through a library of books (physical or digital) is just too slow to be useful. For searching through already read blogposts there seems to be no solution at all. So often I resort to Googling which shows me different, inferior sites instead of returning the things I have already read. This is inefficient and keeps me from drawing new connections between knowledge I've already consumed.
What I now use instead: A local tool that searches through my library of books, saved posts and notes while being as fast as Google. Just like Google, it is maintenance-free and money-free as well. Maintenance free because many self-built tools are a huge timesink, and I want this to save me time. Money free because I intend to keep this tool for many years and don't like continuous payments. All it took was plugging together a handful of well-built tools in a smart way, which I did in a single afternoon.
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René Girard & Mimetic Theory for Non-Philosophers
May 27, 2020
Mimetic theory is a simple but immensely powerful concept. It explains how humans learn, why laws exist, and why too many people want to go into Finance. The idea was developed by René Girard, a french philosopher, member of the Académie Française and professor at Stanford. In the last few years, independent of Girard’s research, studies into imitation, formation of desire, and mirror neurons have been published that bring forward empirical justification for the theory. Let’s start by looking at the core concept of mimetic theory: Imitative desire.This is the primer I would have wanted to read before diving into the primary literature, which is eye-opening but can be dense.
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The Normalizing Flow Network
August 8, 2019
The Normalizing Flow Network (NFN) is a normalizing-flow based regression model, great at modelling complex conditional densities. Look at our recent paper on noise regularization for conditional density estimation for some results of using the NFN on real-world and benchmark regression datasets.
Here I’ll explain the structure of the NFN and go through some of the math. Implementations can be found in our Open-Source Python package as well as in my repo.