Feb 28th, 2021 | Model selection and Hodge’s Problem
Dec 31st, 2020 | My 2020 Strava running year in review
2020 was a good year for my running training and the first year I started using Strava. It was fun to look a bit deeper into the data they collect.
Oct 6th, 2020 | SUTVA vs. Exchangeability
An explanation of two concepts in causal inference and statistics and how they differ, per a question in a recent lab meeting.
Aug 27, 2020 | Trunk: the curse of dimensionality
Feb 27, 2020 | McNemar’s Test for comparing classifiers
Feb 17, 2020 | Learning with an unreliable teacher
Jan 19, 2020 | The Impact Labs Fellowship
I was selected as a 2020 Impact Labs Fellow and spend two weeks in NYC participating in this computer science and social entrepreneurship bootcamp!
Nov 30, 2019 | Gershgorin Circle Theorem Visualization
A visualization of a theorem covered in my Matrix Analysis class that bounds the spectrum of a square matrix.
June 24, 2019 | Predictive Analysis and Electronics Board Failures
An application of Bayesian inference inspired by a friend’s question and how to better incorporate uncertainty into your estimates.
June 18, 2019 | Choosing recipes with network science
A friend’s dilemma led me to an interesting application of my network science toolbox. It turns out, recipes are easily represented as a network. Take a look for an in depth analysis.
Ever wondered how partisan the US house of Representatives really is? It turns out that twitter provides some pretty interesting data and Python can help us understand said data. This work was inspired by material from a class I took at the Technical University of Denmark (DTU). Not only can we visualize the retweets of the individuals in the 2018 House as a network, we can examine their tweets too to analyze the sentiment within.