Hi there! I'm Asher, a half-decent coder and junior at Brown University. I focus on statistical analysis and machine learning, with a particular interest in the creation of new algorithms with mathematical guarantees. The following are some of my most interesting repositories:

Active Projects

• In ML Papers, I'm working on reading and replicating as many ML papers as I can, though it's a long process and I mostly work on it when I'm bored and aren't actively building something new. So far, I've built FFNN, Decision Tree, Random Forest, and Gradient Booster. Next to build are CNN, RNN, and SVM. I write notes on most/all of my readings and replications.

Finished Projects

• For the 2024 election, I founded 24cast.org, the very first open-source, ML-based election prediction model. You can find these repositories in the Brown Political Review organization. Of particular interest is the model itself, and the code for election night, which includes a conformal prediction model designed to take in live election data and output mathematically valid, minimum-size confidence intervals using linear regression. There's plenty other cool stuff in that repo too -- take a look around!
• Along with one other Brown student, I designed Time-Based Bayesian Optimization, a method for derivative-free optimization in high-cost evaluation circumstances. It was created for a school project, and was ultimately submitted into NeurIPS. While it wasn't accepted (another researcher had discovered it a few years back), it's still fun to look at! The paper can be found in the README.
• At an event hosted by the Institute for Replication, I worked with PhD students and professors to replicate a paper on green policies and rightward political shifts in Europe. The code can be found here.

These are just some of my projects, and I've got plenty more in the works! Feel free to reach out to me at [email protected] with any questions.