Projects

Note: Here, I hacked together a diffusion llm implementation on nanoGPT. All the code can be found in this github repo I’ve been really interested in diffusion models lately, and a really interesting application of them is in language modeling. Specifically, I am talking about diffusion LLMs, where an LM iteratively refines a text output. For example, the LLaDa paper outlines a method to start from a fixed number of masked tokens and refine that window to produce a coherent output. The advantage with this is that it is able to parallelize a large number of tokens all at once, whereas autoregressive LMs can really only produce one token at a time (when not batching, as in most inferece applications). ...

This project was done as our final project for William Gilpin’s Graduate Computational Physics Course. Our complete GitHub repository, with instructions on how to replicate our results, can be found here. Introduction The goal of this project is to simulate the behavior of a bike-sharing system in a network of stations and destinations, and then optimize the positions of the stations. We approach the simulation of the bike-sharing system with Agent Based Modeling (ABM). ...

About two years ago, I attended a seminar given by Dr. Sid Redner of the Santa Fe Institute titled, “Is Basketball Scoring a Random Walk?” I was certainly skeptical that such an exciting game shared similarities with coin flipping, but, nevertheless, Dr. Redner went on to convince me–and surely many other audience members–that basketball does indeed exhibit behavior akin to a random walk. At the very end of his lecture, Dr. Redner said something along the lines of, “the obvious betting applications are left as an exercise to the audience.” So, as enthusiastic audience members, let’s try to tackle this exercise. ...