Software
Statistical Relational Learning
"Statistical Relational Learning" (SRL) is a subfield of artificial intelligence that combines logical representations with probabilistic reasoning. I've built some tools to help users incorporate these models into the wider machine learning ecosystem, or apply them to new applications. Also see: https://github.com/srlearn/
Relational Data Linter
A grammar and linter to check that relational or inductive logic programming datasets meet standards.
SRLBoost
⚡ Fast implementations of boosted relational dependency networks and Markov logic networks.
Bayesian Networks
Tools for working and modeling with Bayesian networks.
UniDAGs
Uniformly distributed samples of directed acyclic graphs for random Bayesian Network generation.
Miscellaneous
Occasionally one of my projects has a need that seems like it could be helpful elsewhere. So I extract the code into a more-general utility.
nuMoM2b-preprocessing
Preprocessing scripts to create reproducible partitions of the nuMoM2b data set.
ExportPublic.jl
Julia macro for people who prefer declaring public/private scoping with syntactic sugar.
Course Projects
Things I built while working on a course or learning something new. Generally I built these to "learn lessons" rather than "develop for long-term support," so the quality has high variance.
FanFiction Search Engine
A full-text search engine for Code Lyoko FanFiction. Final project for Search Informatics.
Malicious .exe Detection
Using classifiers to determine whether a Windows portable executable file (.exe) is malicious or benign.
(Notes and Acknowledgements)
Badges were made with shields.io, and Python package statistics are occasionally pulled from PePy (for example, see the PePy entry for srlearn).