About me

Hi! I’m Duncan Soiffer, a 2024 - 2025 (December) Master’s in Machine Learning student at Carnegie Mellon University. Previously, I was an undergraduate at Worcester Polytechnic Institute from 2020-2024, where I majored in computer science and math. I am broadly interested in all aspects of machine learning, and especially interested in core methods.

I currently work in Professor Virginia Smith’s lab, where my primary project is on extending agreement-based cascading to generative language tasks (where agreement is difficult to quantify). On the side, I’m also experimenting with exploring inductive bias and the solution space of representation learning methods through a novel (but as-of-yet unproven) lens.

Besides this, I have previously worked on reinforcement learning, AI for science, and a cubesatellite. Out of all of my projects, I’m most proud of my Major Qualifying Project (undergraduate thesis) at WPI. Along with fellow undergraduates Andrew Salls and Chase Miller, and supervised by Professors Daniel Reichman, Gábor Sárközy, and George Heineman, we improved the best known lower bounds on the Chvátal–Sankoff constants (click here for our paper) and additionally created a website to help teach and explore the Longest Common Subsequence problem more generally.