Simmering Ideas, Not Prepackaged Solutions.
These experiments are my messy playground, not polished products. Dive in, tinker, and maybe uncover a gem amongst the sandcastles.
This is the messy backstage of a curious coder. Dive into experiments in progress, untamed ideas, and code that might just change the game (or accidentally set your cat on fire).
Think of it like a science fair after dark. Some experiments might blow your mind, others might blow up in your face. That's the beauty of exploration, the thrill of seeing where curiosity leads. So, put on your lab coat (or pajamas, no judgment), and take a peek behind the curtain. You might just stumble upon a diamond in the rough, or at least a good laugh at my expense.
NLP Exploration: Topic Models
Using topic models in tomotopy to determine the topics of a corpus, the aspects discussed, and the relationships between topics.
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2 Trustworthy Alternatives to Improving Performance Lists in Track & Field
Improving meet performance lists in track & field using the previous performances of the competitors
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Fair Division of Goods: The Impossibility of Perfect Fairness
Part 1 of a series exploring the mathematical foundations of fair division. Learn why perfect fairness is impossible with indivisible goods, how to formalize competing fairness criteria, and why understanding divisible goods (cake-cutting) illuminates the challenges of discrete allocation.
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Boosting Web Performance: Implementing K-Means Clustering with WebAssembly and Emscripten
Exploring complexities of optimizing web performance by implementing K-Means clustering algorithms using WebAssembly and Emscripten.
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Neural Variational Document Models with PyTorch for Topic Extraction
Discover how Neural Variational Document Models, implemented using PyTorch, improve topic modeling and unsupervised learning in natural language processing. Learn about the architecture, training process, and applications of these latent variable models for text analysis and beyond.
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Fair Division of Goods: Cake Cutting
Part 2 explores cake-cutting algorithms for divisible goods, establishing theoretical foundations before confronting the harsh reality that most real-world problems involve indivisible items where perfect fairness often becomes impossible.
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